TypeSafe shipped Jev on September 15, 2026. Founder Diogo Almeida calls it a System One model: unstructured state in, typed probabilities out, in one parallel pass. It does not write text. The question types are choice, score, and noul.
That is a real product. The week after the launch, the clips going around sold a different one.
I am writing this on October 3 with the same builder lens as the other model notes: what the release actually does, what I would wire into a product, and where the public story runs ahead of the API.
What shipped
Jev is a closed, hosted decision API. You send a state (a string or structured data) and a set of typed questions. It answers all of them in one call. A choice picks among labels you name. A score rates an ordered rubric. A noul returns a probability from 0 to 1. Every answer carries a probability. There is no string to parse, because there is no string.
Spec
Jev
Maker
TypeSafe AI
Released
September 15, 2026, early access
Output
choice, score, noul, with probabilities
List price
$0.042 per million input tokens. Output unmetered
Latency they quote
70 to 500 ms end to end
Context
About 32k tokens of state. Laya Studio cites TypeSafe docs for a 64k-token request, with 32k shared by the state and the longest question
Choice size
Up to 255 options
Weights
Closed
TypeSafe’s own comparison table draws the line. Chatbots, copilots, and coding agents sit on the LLM side, because those jobs need generated text and a human in the loop. Jev’s column is the smart if-statement: classify, route, score, extract, or branch where a hand-written rule is too brittle. The same column covers map-reduce over records, real-time gates around 100 ms, and scoring or guardrailing another model’s output.
Their speed and cost headlines are against LLMs on System One shaped queries, on evals they built. The launch post says end-to-end time can be 40x to 200x faster than frontier chat models for that shape of question. The 193.6x faster and 444.6x cheaper figures on the homepage come from those workflow evals, and they say those multiples are the high end. The reference answers are an average of GPT-6 Astra and Claude Fable 5.1, run through TypeSafe’s own wrapper. Treat that as a vendor Pareto chart, then measure your own questions.
The clips are selling a different product
The launch post includes two “fun demos,” and the nuance boxes under them are more useful than the clips.
Doom. The bot plays from structured game state, a data structure with text. TypeSafe says it is on text, and that images are still ahead. They also say a non-AI Doom bot could play better. The demo is there to show a model reacting to different representations of state at roughly ten queries a second, on the order of $7 an hour at list price. A spectator reads “AI plays Doom.” The request is a menu over fields the game already computed.
Wikiracing. Start on one Wikipedia page, reach another using only links on the page. Each step is “which link,” sometimes hundreds or thousands of them. Jev’s choice cap is 255. Above that they score links independently, then make an explicit choice, which is why some steps slow down. The clip looks like an agent browsing the web. The model is picking a label from a list of link titles.
Then the influencer layer arrived, and the captions got larger than the request body.
Browser use.LangChain’s Jev post points at Kyle Jeong at Browserbase: browser-use agents “for fractions of a cent.” Jeong’s own loop is specific. Observe the page, send the accessibility tree as state, send the actions as questions, Jev picks, Stagehand performs the click. The request he sketched is a choice for the operation, targets, and an input value, plus a noul for whether the goal is done. Jev receives a tree and a menu. It does not see the screen. Stagehand is the part that acts.
Trading. The same LangChain roundup calls Jarrod Watts’ project a live trading agent. The public jev-trader page is plainer. Every Monad block, about 300 ms, the bot asks whether MON will be higher or lower than the mid after about 100 blocks, roughly 30 seconds. The answer is buy or sell. The public deployment dry-runs a mock model until you set MODEL=jev and a TypeSafe key. Live orders also need a private key. A 30-second price direction is a choice question. TypeSafe’s published job is routing, scoring, and guardrails inside software you already trust. A price guess on a dry-run mock is a spectacle with the same wire shape.
Oracles and coding thumbnails. Third-party demo sites ship an Ask Jev Anything box: type any yes or no, get YES, NO, or IT DEPENDS with odds. That is a bare noul with no workflow around it. Community indexes title other clips the cheapest agentic loop, usually Jev plus Claude Code. A review router that returns allow, confirm, or block on a diff is inside the product. The title that implies Jev wrote the patch is the caption doing extra work. Claude Code or Codex still plans, edits, and repairs. Jev answers a short list of questions about the diff.
This is the part that feels hyped by AI tech-bro influencers. The demos are real, and several of them are clever harnesses. The harness is what the viewer remembers: a browser, a trade, a game, a coding session. Jev’s contribution in each of them is a typed pick over options the demo author already listed. TypeSafe spent the launch post telling you the LLM column is where chat, copilots, and coding agents live. The timeline spent the next two weeks filming that column and putting Jev’s name on it.
Email triage at scale, which LangChain also mentions, is the clip that matches the column TypeSafe published. A ticket, a department, an urgency score, a churn probability. That is the product. Doom speedruns and buy-or-sell bots are what travel.
Laya is the free weights
Convai Innovations published Laya under Apache 2.0. It answers the same three question types. State in, probabilities out, no generated text. The code and weights are on Hugging Face and GitHub. pip install laya is the install. Self-host speaks TypeSafe’s POST /v1/systemone shape through laya-serve, so a Jev client can point at your own process.
Three checkpoints ship in the one repo. The SDK downloads the one you ask for.
Checkpoint
Encoder
Params
Context per question
convaiinnovations/laya
ModernBERT-large
421M
512 tokens
laya-multilingual
mmBERT-base
322M
1,024 tokens
laya-typed-decisions
ModernBERT-large
421M
1,024 tokens
English is the default for Latin text, guardrails, and triage. Multilingual covers the 100-plus language claim and is the faster of the two small checkpoints. Typed-decisions is the fine-tune aimed at agent observability, customer service, invoices, and security alerts.
Free means the weights and the self-hosted call. Apache 2.0, no per-token invoice, commercial use allowed. You still pay for the GPU, the electricity, and the time to calibrate. A T4-class card is the machine in Convai’s latency notes. CPU is possible and slower.
Laya Studio is a separate hosted API in front of those weights. It is an independent company, unaffiliated with Convai and with TypeSafe. It bills input tokens and sits under Jev’s list price, with a small free token grant on a new workspace. That host is convenient. It is a paid API. The free path is the checkpoint on hardware you run.
Jev vs Laya
Both are System One decision models trained with reinforcement learning for calibrated decisions. Both take choice, score, and noul. Laya Studio’s comparison, citing the Laya model card, is the public side-by-side. I am using those published rows. I have not re-run them.
Laya
Jev
Weights
Apache 2.0, self-host
Closed hosted API
Price
$0 per call on your GPU
$0.042 / million input tokens, output free
Single-question latency
39.5 ms English, 32.8 ms multilingual, in-process on a T4
236 to 276 ms p50, hosted, as cited on the Laya card
AG News (4 labels)
0.950
0.910
DAIR Emotion (6 labels)
0.595
0.480
Typed-decisions
0.766 on the fine-tuned checkpoint
0.727
Banking77 (77 labels)
0.425
0.870
Choice size to plan for
Keep it under about 20
Up to 255
Context
512 or 1,024 tokens per question
About 32k tokens of state
Calibration (ECE, lower is better)
0.466 as shipped, 0.081 after a temperature refit
0.246 on DMB forced-uncertainty items, 0.144 on typed-decisions
Languages
Multilingual checkpoint, 45 of 51 MASSIVE languages above 3x random on the card
English primary. Other languages are documented as uneven, with no published per-language board
Fine-tune
Open weights. Convai’s notebook is a few hours on two T4s
Shape the question. No per-customer fine-tune
On a short English question with a handful of labels, Laya leads the published rows and returns in tens of milliseconds on one GPU. On a 77-way label set, Jev leads by a wide margin, 0.870 against 0.425. That gap is the practical rule: keep Laya’s choice lists small, or split a big taxonomy into stages. Jev is the one that can hold a long ticket, a trace, or a document in one state.
Calibration needs a second look. Laya’s better ECE, 0.081, is after a temperature refit. As shipped, the card’s ECE is 0.466, which is the worse of the two. If you gate on confidence, refit on your labels. The formulas differ on top of that. Laya’s choice confidence is one minus normalised entropy. TypeSafe illustrates Jev’s with a different function of the top probability. A threshold you liked on jev-1.13.0 will mean something else on Laya. Re-derive it.
The wire shape is the part that makes a trial cheap. Both speak POST /v1/systemone with state and questions. Laya accepts a Jev model id such as jev-latest and ignores it, then routes to its own checkpoint. The response adds routing (which checkpoint answered) and an action object. Jev clients that ignore unknown fields keep parsing. Confidence is the field you have to re-tune. It is present on both, and it is computed differently. Laya also puts a confidence on noul answers. TypeSafe’s noul payload, in the docs Laya Studio quotes, carries the probability and omits that field.
What I would run
If the job is
I would use
I would leave alone
Short text, under about 20 labels, data stays on my machines
Laya, self-hosted, then a temperature refit on my labels
A hosted key “because the demo was fast”
20 to 255 labels, or a long state
Jev, pinned to jev-1.13.0
Stuffing 77 labels into Laya and hoping the AG News row transfers
No one to run a GPU
Jev’s API
Laya Studio, unless the residency and the bill are the point
Non-English routing
Laya multilingual, checked per language
Assuming Jev’s English rows hold
Writing or editing code
The coding model I already trust
A Jev or Laya call dressed up as the agent
A yes/no gate inside a workflow (refund, injection, escalate)
Either, after I label a few hundred real cases
An “ask anything” box
Laya is the default I would try first when the question is small, the text is short, and I can run a GPU. The weights are free, the latency on the card is the interesting part, and Apache 2.0 means the checkpoint can sit next to the data. Jev is what I would call when the label set is large, the state is long, or I want a hosted endpoint and zero-shot probabilities this week. Early access and a moving jev-latest alias are reasons to pin the id and keep a fallback.
Neither one replaces the model that writes code, browses with vision, or explains a trade. Those demos borrowed a decision head and filmed the harness.
Takeaway
Jev is a fast, closed API for typed decisions with probabilities, at $0.042 per million input tokens, in a latency band TypeSafe puts between 70 and 500 ms. Its own launch post puts classify, route, score, and guardrail on that API, and puts chat, copilots, and coding agents on a normal LLM. The clips that traveled are Doom on pre-chewed game state, link picking, a browser loop whose eyes are an accessibility tree, and a trader whose public deploy is a mock until you bring a key.
Laya does the same job with Apache-2.0 weights you can run for the cost of a GPU. It is faster on the published local numbers, stronger on the small public classification rows, and far behind on many-label choice. Self-host is the free version. Laya Studio is a cheaper host, run by someone else.
I would use Laya for short, few-label, on-prem decisions, and Jev when the menu is huge or the state is long. I would not pick either one because a timeline clip made the harness look like the model.
If you want a routing or guardrail layer that stays inside the decision the product can actually make, start on the contact page.
The week of September 28 through October 2 had three posts already: Claude Sonnet 5.5 on Monday, GPT-6.1 Sol on Tuesday, and Gemini 4 Argon on Wednesday. This note is the rest, written on Friday, October 2, 2026. I am not inventing Saturday or Sunday.
No new flagship model id showed up on Thursday or Friday. Argon is still a Fairwind gate. The coding shortlist from those three posts is the shortlist.
The week at a glance
Date
What
Why a builder cares
Sept 28
Claude Sonnet 5.5
Covered in the Sonnet post. Same $2 / $10 as Sonnet 5. between_tools before you migrate
Sept 28
DeepSeek DSec paper
Training sandbox infrastructure. Not a new model id
Sept 29
GPT-6.1 Sol
Covered in the 6.1 Sol post. Sol’s sticker, half the cache read. GPT-6.1 Astra did not ship
Sept 29
Dots and Ultrafast
DevDay product and a speed tier. Dots runs on Astra. Ultrafast is about 6x the price
Open-source libraries for Huawei Ascend. Not a V4.1 Pro release
Oct 1
Microsoft MAI speech
Streaming transcription and two voice models. Not a coding default
Oct 2
No new flagship id
Argon is still Fairwind-only. The week stops here
DevDay, after the model post
Tuesday’s model is already written up. Two other DevDay items change how you spend, not which checkpoint you fine-tune.
Dots. OpenAI showed always-on agents at DevDay on September 29. Simon Willison’s live notes and MediaNama’s write-up agree on the shape: Dots are powered by GPT-6 Astra, they keep working in a cloud computer and browser after you stop prompting, and the first wave is ChatGPT for Pro and Business Premium in eligible markets. Enterprise, Edu, and Healthcare are a beta an admin has to turn on. The first dot is included. This is a product on top of Astra. It is not a new API model id, and it does not retire gpt-6.1-sol.
Ultrafast. The same keynote described a speed tier for Astra: on the order of 8x in Codex, up to about 300 tokens a second, at about 6x standard price. Astra Ultrafast is the one available now, on the higher Pro tier and Enterprise, and in the API. GPT-6.1 Sol Ultrafast was described as later. Faster tokens that cost 6x are a latency buy, not a discount. Leave it off unless a human is waiting on the stream.
DeepSeek published infrastructure, not V4.1 Pro
September 28: DeepSeek and Tsinghua posted DeepSeek Elastic Compute (DSec). It is the sandbox platform under agent training from V3.2 through V4.1. The abstract describes FnCall, container, microVM, and full-VM backends, and a split between stateful rollout and preemptible GPU training. Their production figures are about 3 million sandboxes a day, more than 380,000 concurrent, and more than 5,000 created per second, on a unit of about 160 nodes.
That explains how they train agents. It does not put a new checkpoint on the API. deepseek-v4-pro is still the Pro id from the 0813 post. V4.1 Pro is still a name, not a release.
September 30: DeepSeek open-sourced Ascend ports of the kernel and communication libraries, including TileLang, DeepGEMM, DeepEP, TileKernels, FlashMLA, and DeepSelect, aimed at Huawei Ascend 950. Geopolitechs quotes DeepSeek’s own note from that day. This is a serving path that does not go through Nvidia. It is not a model launch, and it does not change the id I would call on a client API.
Microsoft shipped speech
October 1: Microsoft AI announced MAI-Transcribe-2-Streaming, plus MAI-Voice-2.1 and a faster MAI-Voice-2.1-Flash. They are transcription and text-to-speech models. They sit next to last week’s Gemini 3.8 TTS note: useful if you are building voice, irrelevant if you are picking a coding default.
Friday
October 2 did not add a model id. TechRepublic’s recap, citing Reuters, says Argon led some of Google’s published rows and trailed on two of four coding benchmarks, and that there is still no public release date. That matches the Argon post: a watch item, not a default. I am not using a cyber write-up as a setup guide.
What I would change
If you are on
Change
Leave alone
Claude, everyday tasks
Move Sonnet 5 routes to claude-sonnet-5-5 after between_tools replaces disabled
Do not quote the 70.6% Terminal-Bench cell as the medium default
OpenAI agents
Test gpt-6.1-sol at high on a repo task and at max on a computer-use task. Budget cache reads at $0.10
Do not point background Dots at Astra and expect the Sol invoice
Latency on Astra
Turn on Ultrafast only where a person is waiting, and budget about 6x
Do not make Ultrafast the default Codex setting
Gemini for coding
Stay on 3.8 Flash until Argon has an id
Do not put the $2 / $10 intro into a statement of work
DeepSeek
Keep the current V4 Pro or Flash id
Do not treat the DSec paper or the Ascend kernels as a new checkpoint
Takeaway
September 28 to October 2 was two callable model updates and one gated announcement. Sonnet 5.5 is the everyday Claude id if you fix thinking first. GPT-6.1 Sol is the OpenAI id I would test against last week’s Sol, at the effort the chart actually used. Argon is a name and a price without an endpoint. Dots and Ultrafast spend Astra on purpose. DeepSeek shipped a paper and kernels.
I would change a Sonnet string and run one 6.1 Sol trial. I would not change the rest of the stack because the week was loud.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Google announced Gemini 4 Argon on September 30, 2026. It is their next frontier model for long-horizon software engineering, knowledge work such as legal and finance, and cybersecurity defense. It is rolling out to trusted cyber defenders in the Fairwind Program. It is not in the Gemini API I can call today.
I am writing this the day of the announcement. Yesterday’s callable OpenAI id is GPT-6.1 Sol. Monday’s callable Anthropic id is Claude Sonnet 5.5. Argon is a price and a gate. It is not a model string.
What is public
Fact
What Google has said
Date
September 30, 2026
Who has it
Trusted cyber defenders in Fairwind, plus Google’s own use
Who does not
Developers, enterprises, and consumers, until a later step
Next public step
Paid API customers and Google AI Ultra subscribers, “as soon as possible”
Intro price
$2 per million input tokens, $10 per million output tokens
Cached input
95% off the input price
After the intro
$4 / $20. Google has not said when the intro ends
Model id
Not published for general use
One published coding row
DeepSWE v1.1 at 77.9%, which Google calls a new state of the art
Google says it is in the U.S. government’s voluntary process for pre-release access, and that it will keep tuning guardrails with the early testers before a wider release. That is the whole availability story. There is no date on the paid API.
Fairwind is the same program the Astra vs 3.8 Flash note treated as a defender gate, not a SKU. SiliconANGLE’s same-day recap says the program opened September 3 with Gemini 3.8 Flash Cyber and has since signed up more than 650 organizations. Membership in that program is not something a client repo can assume.
The sticker you can already buy elsewhere
Argon’s introductory $2 / $10 is the same headline as GPT-6.1 Sol, which shipped yesterday, and the same headline as Sonnet 5.5, which shipped Monday. A 95% cache discount on a $2 input is $0.10 a million cached tokens. GPT-6.1 Sol’s cache read is already $0.10, and you can send it traffic today.
The later Argon price, $4 / $20, matches Opus 5.5. Google has not said which invoice you should budget. I would budget $4 / $20 for any plan that assumes Argon is still around after the intro. I would not budget the $2 / $10 intro into a 2027 statement of work.
Worked examples only make sense as a comparison to ids that exist. Short-context standard rates, intro Argon priced like GPT-6.1 Sol, including a $0.10 cache read.
Loop
Argon at the intro sticker, if you could call it
GPT-6.1 Sol, which you can
Opus 5.5
100K input, 4K output, uncached
$0.24
$0.24
$0.48
200K input, 70% cache hit, 20K output
$0.33
$0.33
$0.67
1M cumulative input, 80% cache hit, 100K output
$1.48
$1.48
$2.96
Those Argon cells are arithmetic on a rate card, not a bill I have paid. Token use can wipe the sticker out. I have not run Argon, so I am not claiming its cost per task.
The 77.9% is a vendor row you cannot rerun
Google’s launch post says Argon sets a new state of the art on DeepSWE v1.1 at 77.9%. That is a long-horizon software engineering eval. It is also a number from a model the public cannot call, scored by the lab that trained it, while the weights and the guardrails are still being adjusted for a wider release.
I am not lining that 77.9% up against yesterday’s GPT-6.1 Sol chart or Monday’s Sonnet table and declaring a winner. Those rows were run on models with ids. This one was not. When an id exists, the test is the same one I use everywhere: one real multi-file task, with tools, constraints, and a deadline.
Cyber stays at the access gate. Google’s post is about defense, inside a vetted program, during a pre-release process. I am not treating a leaderboard as a reason to point Argon, or any other model, at systems you do not own.
When I would use it
Not this week, unless you are already inside Fairwind
If you are in that program, follow Google’s access rules and keep the work inside the program. This post is not a setup guide.
If you are not, do nothing to the production model string.
What I would use instead, today
GPT-6.1 Sol when the job is an OpenAI coding or computer-use loop and Astra’s token price is the constraint
Gemini 3.8 Flash when you need a Gemini id that is actually generally available
Takeaway
September 30 gave Gemini 4 a name, an intro price that matches models you can already call, and a Fairwind gate. It did not give the rest of us an API id. The $4 / $20 price has no start date. The 77.9% DeepSWE row cannot be checked from outside the program.
I would leave client routes where they are. I would read the model page again when Google publishes an id. Until then, Argon is a watch item, not a default.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
OpenAI shipped GPT-6.1 Sol on September 29, 2026, at DevDay, one week after GPT-6 Sol and Luna. The id is gpt-6.1-sol. Standard price is $2 per million input tokens and $10 per million output tokens. That is Sol’s sticker, and one-fifth of GPT-6 Astra at $10 / $50.
I am writing this the day it shipped. The model that did not ship matters too. TechCrunch reports that GPT-6.1 Astra was dropped after internal safety testing, citing the Wall Street Journal: higher deception, and a tendency to continue a task without asking. Sol is the 6.1 id you can call. Astra 6.1 is not.
What shipped
Spec
GPT-6.1 Sol
GPT-6 Sol
GPT-6 Astra
Release
September 29, 2026
September 22, 2026
September 3, 2026
Model id
gpt-6.1-sol
gpt-6-sol
gpt-6-astra
Context / max output
1,050,000 / 128,000
1,050,000 / 128,000
1,050,000 / 128,000
Knowledge cutoff
April 30, 2026
April 20, 2026
April 30, 2026
Reasoning effort
low through max. Default medium. No none
none through max. Default medium
none unsupported
Input / output
$2 / $10
$2 / $10
$10 / $50
Cached input
$0.10
$0.20
$1.00
Where today
ChatGPT Work, Codex, API. Not Chat
Same
Staged ChatGPT, API, Bedrock
Sources for the card: the model page, API pricing, and the GPT-6 guide. The guide is explicit: Astra and GPT-6.1 Sol do not accept reasoning.effort of none. If a Sol route used none, start at low and re-test. Tool calling belongs on the Responses API.
The price change versus last Tuesday is the cache read, not the headline sticker. Cached input went from $0.20 to $0.10. Cache writes stay $2.50. The long-context cliff is unchanged: a request over 272K input tokens is 2x on input and cache and 1.5x on output for the whole request.
Same worked examples as the Sol post. Short-context standard rates.
Loop
GPT-6.1 Sol
GPT-6 Sol
Astra
100K input, 4K output, uncached
$0.24
$0.24
$1.20
200K input, 70% cache hit, 20K output
$0.33
$0.35
$1.74
1M cumulative input, 80% cache hit, 100K output, under the 272K cliff
$1.48
$1.56
$7.80
One-fifth of Astra is the right comparison for input and output tokens. It is the wrong comparison for a single unsplit million-token request, which reprices the whole call.
What OpenAI is claiming on the benches
These are OpenAI’s launch comparisons, not an independent board. Effort is the product.
DeepSWE v1.1. GPT-6.1 Sol at high scores 75.2%. That is 6.4 points over GPT-6 Sol’s best published score, 68.8% at max, at about 76% lower cost per task. OpenAI’s framing is that this matches Astra on the eval at about one-fifth of Astra’s token price. The effort curve that people are reading off the launch chart peaks at high and is lower at max (about 71.9%). I would not “turn it up” to max on a coding loop and expect the 75.2% cell.
OSWorld 2.0 offline, partial reward. At max, 71.4% versus Astra at 73.5%, at about one-seventh of Astra’s cost per task. That is within about two points of the computer-use flagship, on OpenAI’s own run. Partial reward is still not “share of workflows fully solved.”
AutomationBench. At medium, 31.7%, up 4.8 points from GPT-6 Sol at the same effort. Last week’s Sol headline was xhigh at 33.2% and $0.27. Do not staple those two rows together.
Terminal-Bench Science. Astra still leads the models OpenAI showed, at 68.1%. GPT-6.1 Sol is not the science flagship. OpenAI’s comparison puts its own cost near $5.47 a task on that eval and says the score more than doubles GPT-6 Sol. I am not treating that as a reason to pull a research loop off Astra.
Safeguards, at the level that changes a stack
The system-card addendum (September 29) treats GPT-6.1 Sol as Critical in cybersecurity and High in biological and chemical capability, and below the High threshold in AI self-improvement. It uses the same safeguards stack as GPT-6 Astra. That is why a cheaper id is not a looser id.
I am not walking the exploit boards. The practical fact is the same one from the Astra post: this model can sit behind extra review on ChatGPT and Codex, and an API task can stop. Daybreak remains an access program for defenders, not a model string for a client repo.
The scrapped GPT-6.1 Astra is the other safety fact. OpenAI shipped the Sol-class id and did not ship the flagship update. I would not fill that gap by assuming gpt-6-astra quietly became 6.1. The Astra id on the account is still the September 3 model until OpenAI says otherwise.
When I would use it
GPT-6.1 Sol
A route that moved to gpt-6-sol last week, after one real task at high effort if the job is a repo, or at max if the job is computer use
Cache-heavy OpenAI traffic, where $0.10 cache reads are the actual change in the bill
A team that wanted Astra on coding or desktop tasks and can live two points off the OSWorld cell to cut the token price by 5x
Still Astra
Terminal-Bench Science and any other row where OpenAI’s own chart still has Astra ahead
Jobs that needed Astra’s computer-use ceiling more than a 2-point gap
Anything you were waiting on GPT-6.1 Astra to do. That model is not in the API
Still elsewhere
Claude Sonnet 5.5 if you are already on Claude and the task is everyday Sonnet work. The stickers are close. The cache read now favors this id
Luna when $0.10 / $0.50 is the point and 6.1 Sol is still too much model
Opus 5.5 on the long Claude Code sessions that were already earning the $4 / $20
Takeaway
GPT-6.1 Sol is the id I would test before I keep last week’s gpt-6-sol as the default OpenAI agent. The sticker did not fall. The cache read did. The coding claim is a high-effort claim, and it comes with Astra’s safeguard stack, not a lighter one. GPT-6.1 Astra is not a thing you can select.
I would run one multi-file task at high and one computer-use task at max, then decide. I would not rewrite a client stack because DevDay said “near Astra” and stop at the headline.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Anthropic shipped Claude Sonnet 5.5 on September 28, 2026, six days after Opus 5.5. The id is claude-sonnet-5-5. It is the everyday model in the 5.5 family: faster, fewer tokens on the same sticker as Sonnet 5, and half the input and output price of Opus 5.5.
I am writing this the day it shipped. Haiku 5.5 is still “coming weeks” on Anthropic’s Opus post. This note is Sonnet only.
What shipped
Spec
Claude Sonnet 5.5
Claude Opus 5.5
GPT-6 Sol
Release
September 28, 2026
September 22, 2026
September 22, 2026
Model id
claude-sonnet-5-5
claude-opus-5-5
gpt-6-sol
Context / max output
1M / 128K
1M / 128K
1,050,000 / 128,000
Input / output
$2 / $10
$4 / $20
$2 / $10
Cache read / cache write
$0.20 / $2.50
$0.20 / $5
$0.20 / $2.50
Where
Claude Platform, AWS, Google Cloud, Microsoft Azure
Same class of platforms
API, ChatGPT Work, Codex. Not Chat
Anthropic’s launch copy: a clear upgrade over Sonnet 5, 30%+ faster, and up to 30% less for most work. The rate card did not move. Sonnet 5 was already $2 / $10 with $0.20 cache reads. The 30% is fewer tokens per task, plus the speed claim, not a discount line on the invoice.
Cache reads match Opus 5.5 at $0.20. On a loop that is mostly cache hits, picking Sonnet over Opus saves the uncached input and the output, and it does not save the cache-read line.
Worked examples at short-context standard rates, same shapes as the Opus note. Sonnet 5.5 and Sol land on the same arithmetic.
Loop
Sonnet 5.5 or GPT-6 Sol
Opus 5.5
100K input, 4K output, uncached
$0.24
$0.48
200K input, 70% cache hit, 20K output
$0.35
$0.67
1M cumulative input, 80% cache hit, 100K output
$1.56
$2.96
That is the sticker. Anthropic’s “up to 30% less than Sonnet 5” shows up only if this model spends fewer tokens than Sonnet 5 on your task. A loop that writes the same number of tokens costs the same as last week.
The table, with the effort labeled
From the launch post. I am keeping the footnotes they printed, because the headline cell is not the default.
Eval
Sonnet 5.5
Sonnet 5
Opus 5.5
GPT-6 Sol
Terminal-Bench 4.0
70.6%
10.3%
66.4%
not listed
FrontierCode 1.1 (Main)
46.2% at max, 52.1% at xhigh
42.4%
54.4%
49.3%
CursorBench 4.0
55.5%
34.1%
57.8%
not listed
GDPval-AA v2.1
1844 Elo
1449
1846
1487
OSWorld 2.1, partial
80.1%
57.0%
81.8%
not listed
Humanity’s Last Exam, with tools
64.5%
54.9%
67.7%
not listed
Anthropic’s own sentence on knowledge work: Sonnet 5.5 scores two points below Opus 5.5 on GDPval-AA. That matches 1844 versus 1846. CursorBench is two points under Opus as well (55.5 versus 57.8), and far above Sonnet 5.
Terminal-Bench is the row people will quote badly. Sonnet’s 70.6% sits above Opus’s 66.4%. On the Opus 5.5 post, that 66.4% is xhigh, the highest Opus score they reported, not medium. Sonnet’s 70.6% is the max-effort headline, not the medium default Anthropic names for the Claude apps. At medium, their chart text says Sonnet 5.5 beats Sonnet 5’s best score for less than a tenth of the cost per task. It does not say Sonnet at medium beats Opus.
FrontierCode makes the same point in two numbers. Xhigh is 52.1%, near Opus at 54.4% and above Sol at 49.3%. Max is 46.2%, under both. If max is spending extra review and still scoring lower, I want xhigh or medium on a real repo before I trust the 70.6% cell.
between_tools, before you change the id
Sonnet 5 accepted thinking: {"type": "disabled"}. Sonnet 5.5 does not. The replacement is thinking: {"type": "between_tools"}, which keeps up-front thinking off. Anthropic’s launch post says to switch to that setting before you move.
From the thinking troubleshooting table: Sonnet 5.5 accepts adaptive thinking and between_tools. It rejects enabled and disabled with a 400. between_tools is only valid at effort low, medium, or high. Combining it with xhigh or max returns a 400. A per-message effort that differs from the level in effect also returns a 400.
Notes between tool calls still come back as thinking blocks. Pass them back unchanged, the same rule as Opus 5.5. between_tools is not a Sonnet-shaped way to strip those blocks out of the transcript.
Forced tool use is the other break that will 400 an old client. If a route sets tool_choice to any or a named tool, fix that before the id swap. Opus 5.5 already rejected it. Sonnet 5.5 follows.
Claude Code 2.1.284 makes claude-sonnet-5-5 the default Sonnet model on the Anthropic API, and restates $2 / $10 and $0.20 cache reads. That does not undo 2.1.280. Pro and Team Standard still open on Opus unless you pick Sonnet. When you do pick Sonnet, you get 5.5, and a saved effort from before per-model effort does not carry over.
When I would use it
Sonnet 5.5
Everyday coding, bug fixes, and documents where Opus 5.5 was more model than the task
A Sonnet 5 route, after between_tools replaces disabled, because the sticker is unchanged and the Terminal-Bench jump against Sonnet 5 is the whole point
Cache-heavy Claude loops where you want Opus-class cache-read pricing at half the output token
Opus 5.5 instead
The long, ambiguous jobs from the Opus post, where FrontierCode at the top of the Opus column and the fewer-tokens claim were already worth $4 / $20
Anything that was relying on Sonnet at medium to “beat” the 66.4% Terminal-Bench cell. That cell is a different effort
GPT-6 Sol or Luna instead
An OpenAI route. Sol’s sticker matches Sonnet 5.5, so switch labs only for a bench you have reproduced
Volume. Luna is the cheap id. Sonnet 5.5 is not
Takeaway
Sonnet 5.5 is the half-price companion to Opus 5.5, at the same sticker Sonnet 5 already had, and the same sticker as GPT-6 Sol. The 30% savings is a token claim. The 70.6% Terminal-Bench score is a max-effort claim. Medium is what the Claude apps start on.
I would move a Sonnet 5 agent after the between_tools change and one real task at the effort I intend to pay for. I would leave Opus 5.5 on the jobs that were already using it on purpose.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
The week of September 21 to 27 was a flagship week. I already wrote Grok 4.7, GPT-6 Sol and Luna, and Claude Opus 5.5. This note is the rest of the stack: what those three posts do not cover, and what I would change before Monday.
I am writing this on Sunday, September 27, 2026. The models you already shortlisted did not all get replaced. The defaults inside Claude Code and Codex did move, and two Cursor bots showed up for teams that ship through pull requests.
The week at a glance
Date
What
Why a builder cares
Sept 21
Grok 4.7
Covered in the 4.7 post. Same $2 / $6 sticker, more tokens per task
Sept 22
GPT-6 Sol and Luna
Covered in the Sol and Luna post. Half the GPT-5.6 promo price. Not in Chat
Sept 22
Claude Opus 5.5
Covered in the Opus 5.5 post. $4 / $20, thinking always on, default effort medium
Sept 22
Claude Code 2.1.280
Opus 5.5 becomes the default Opus. Pro and Team Standard now open on Opus, not Sonnet
Sept 23
Gemini 3.8 Flash TTS and Flash-Lite TTS
Speech models. Not a coding id. API and AI Studio
Sept 23
Muse Realtime Avatar
Research and a Connect demo. No API, no price, “coming months”
Sept 23
Cursor Rollouts and Security Review
Teams and Enterprise. Deploy health, and one security comment per PR
Sept 25
Codex CLI 0.157.0
gpt-6-sol and gpt-6-luna in the CLI, with migration prompts off older ids
claude-opus-5-5 is the default Opus model. The changelog restates the card: 1M context, $4 / $20 per million tokens, cache reads $0.20.
The default model on Pro and Team Standard plans changed from Sonnet to Opus, matching Max, Team Premium, and Enterprise.
An effort level saved before /effort became per-model does not apply to newly released models. Opus 5.5 starts at its own default until you pick a level. That default is medium, not the high you may have been on with Opus 5. Details are in the Opus 5.5 post.
Two fixes are about where the agent is allowed to act, not about the leaderboard. Writes through a symlink are judged by where they land, so acceptEdits, allow rules, and auto mode no longer approve a write that leaves the tree. Auto mode denies once when a safety check declines to review an action, instead of retrying it.
CLAUDE_CODE_MAX_MCP_DESCRIPTION_LENGTH raises or lowers the 2,048-character cap on MCP tool descriptions and server instructions for the session.
Codex CLI caught the morning’s models
OpenAI’s Codex CLI 0.157.0 (September 25) adds GPT-6 Sol and Luna, including a path through Amazon Bedrock, and prompts you to migrate older model selections. Fullscreen transcripts are on by default. The background server starts for eligible interactive sessions.
The model choice is the one that changes a week of work. Sol and Luna are not in Chat. They are in ChatGPT Work, the API, and now this CLI. Which id, and which effort, is the Sol and Luna post. Pin the CLI version before you let a migration prompt rewrite a CI job.
Gemini 3.8 speech is not a new Flash coding model
September 23: Google shipped Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS. Ids: gemini-3.8-flash-tts and gemini-3.8-flash-lite-tts. Developers get them in the Gemini API and Google AI Studio. Google Vids gets them for everyone. Gemini Enterprise API access is listed as coming soon on the launch post.
This does not move Gemini 3.8 Flash on a coding loop. It is a speech SKU. Read the current row on Google’s pricing page before you quote a sticker. I am not copying a secondary rate card into this note.
Muse grew a face you cannot call
September 23, Meta Connect and the research post Bringing Your Muse to Life: Muse Realtime Avatar turns Muse Realtime Voice into live video. Meta’s measured path is 448x768 portrait video at 25 frames per second, about 870 ms from the end of a user’s turn to the first byte of voice and video. Generated video carries a Meta Video Seal watermark. The research post says the examples are capability, and not all of them are avatars in the Muse app. Muse is 18+.
TechCrunch’s Connect recap puts the video chat in “the coming months.” I do not see an API id, a price, or a waitlist on the research post. The Muse model I can still call is Spark, which I wrote up at 1.2 and 1.3. An avatar demo does not change that shortlist.
The same Connect window also pointed at Muse email addresses, Mac computer control, and glasses later. Those are product timelines. They are not a new coding model.
Rollouts attaches a monitor to a pull request and reports change health per environment: verified healthy, regression detected, or inconclusive. For the next 10 days, Cursor is including credits so teams can try it: about 50 changes on Teams and about 500 on Enterprise.
Security Review reads the pull request in the context of the repo and posts one comment on exploitable bugs. Style and quality stay with Bugbot. Draft pull requests are skipped. Cursor’s own list is injection, broken authentication and authorization, secrets in source, unsafe deserialization, unvalidated redirects, and dependency changes that bring in known vulnerabilities. Dismiss a finding with a reason and it stays dismissed on that PR.
I would enable Security Review on one non-deadline repo before I enable it on everything. I would spend the Rollouts credits on a service that already has a health check, so “inconclusive” means something.
What I would change
If you are on
Change
Leave alone
Claude Code Pro or Team Standard
Update to 2.1.280 or later, run /model, set /effort on Opus 5.5, re-base /usage against the September 14 weekly seat
Do not assume the new default is cheaper because the Opus sticker fell
Codex CLI
Move to 0.157.0 on a branch, then choose gpt-6-sol or gpt-6-luna at the effort you will actually pay
Do not let a migration prompt rewrite unattended jobs
A speech feature
Trial gemini-3.8-flash-lite-tts for volume and gemini-3.8-flash-tts when the read has to act
Do not swap your coding model because the TTS ids say Flash
Cursor Teams or Enterprise
Turn on Security Review for one repo. Spend Rollouts credits where you can tell a regression from noise
Do not wait on Muse Realtime Avatar for a client deliverable
Takeaway
The week of September 21 to 27 put three model posts on the site and then changed the defaults around them. Claude Code’s cheapest paid plans now open on Opus 5.5. Codex CLI can select Sol and Luna. Google shipped speech. Meta showed an avatar without an endpoint. Cursor shipped two bots for teams.
None of that replaces the shortlist in the three posts. It tells you to check /model, /effort, and /usage before Monday.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Anthropic shipped Claude Opus 5.5 on September 22, 2026. It is the first model in the Claude 5.5 family. The id is claude-opus-5-5. The sticker is $4 per million input tokens and $20 per million output tokens. Cache reads are $0.20.
I am writing this the same day, after the GPT-6 Sol and Luna note. OpenAI cut the promo price under Astra this morning. Anthropic’s answer this evening is an Opus that they say performs at Fable 5.1 level on most work, at about 40% less cost than Opus 5 on typical workloads. Sonnet 5.5 and Haiku 5.5 are promised in the coming weeks. They are not in this post.
The useful split is the same one as this morning: sticker, tokens per task, and whether your client code still boots.
128K. 300K on the Message Batches API with the output-300k-2026-03-24 beta header
Not restated on this launch card
Thinking
Adaptive, always on
On by default. disabled was accepted at effort high or below
Default effort
medium
high
Knowledge cutoff
June 2026 (reliable and training)
Platforms
Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Input / output
$4 / $20
$5 / $25
Cache read / 5-minute cache write
$0.20 / $5
$0.50 / $6.25
Fast mode in Claude Code and the Claude Platform is up to 2.5x speed at $8 / $40. That is 2x the new sticker, not a discount.
Anthropic’s 40% figure is not the sticker. Input and output are 20% below Opus 5. Cache reads, which dominate agent and coding bills, are 60% below Opus 5 ($0.50 to $0.20). Their tests say the model also uses fewer tokens per task. The 40% is that combination, at default settings, on their typical workloads. Quote the 40% only if you are talking about a finished loop. Quote $4 / $20 if you are talking about the rate card.
They are also raising five-hour usage limits on Pro, Max, Team, and seat-based Enterprise, and giving subscription users a rate-limit reset they can save and spend later. That is the five-hour meter. It does not walk back the September 14 weekly seat. Run /usage against both.
The bill, next to this morning’s OpenAI ids
Short-context standard rates. Cache hits use the cache-read price. Same loop shapes as the Sol and Luna post. These are not invoices.
Loop
Opus 5.5
Opus 5
GPT-6 Sol
GPT-6 Astra
100K input, 4K output, uncached
$0.48
$0.60
$0.24
$1.20
200K input, 70% cache hit, 20K output
$0.67
$0.87
$0.35
$1.74
1M cumulative input, 80% cache hit, 100K output
$2.96
$3.90
$1.56
$7.80
Token sticker alone, Opus 5.5 is about twice Sol ($4 / $20 versus $2 / $10) and well under Astra ($10 / $50) and under Fable 5.1’s $10 / $50 class. Cache reads match Sol at $0.20. The 40% workload claim is Anthropic’s, measured against Opus 5, and it depends on the model spending fewer tokens. A loop that does not get shorter will not see 40%.
What Anthropic’s table actually says
Scores below are from the launch post. Unless noted, Opus 5.5 is adaptive thinking at max effort. Production safeguards were on. When they intervened, cyber tasks were completed by Opus 4.8, and biology and frontier-model-development tasks by Opus 5. Anthropic says that likely lowers these scores. I am staying at the published rows.
Eval
Opus 5.5
Fable 5.1
Opus 5
GPT-6 Astra
GPT-5.6 Sol
Terminal-Bench 4.0
66.4% (xhigh, their highest)
55.8%
52.3%
57.9% (high)
37.3%
FrontierCode v1.1 (Main)
54.4%
50.3%
48.0%
53.3%
47.5%
CursorBench 4.0
57.8%
51.8%
46.6%
not listed
41.7%
GDPval-AA v2.1 (Elo)
1846
1735
1708
1542
1588
AutomationBench (Zapier)
40.0%
31.4%
26.9%
41.4%
28.8%
Humanity’s Last Exam, with tools
67.7%
65.6%
63.6%
57.2%
not listed
Terminal-Bench-Science 0.1
58.7%
52.6%
29.0%
64.6%
22.4%
OSWorld 2.0, partial
81.8%
80.7%
74.0%
not listed
not listed
Two rows do not belong to Opus. Zapier’s AutomationBench has Astra at 41.4% and Opus 5.5 at 40.0%. Those Zapier runs had no fallback model, so a safeguard intervention counted as a failure. Terminal-Bench-Science has Astra at 64.6% and Opus 5.5 at 58.7%. Anthropic also says the real gap versus Fable 5.1 is narrower than the table, in their own use.
Default effort is a different product from the max column:
FrontierCode at medium: 54.6%, above Astra’s top published 53.3%, at about a fifth of the cost per task in their chart.
CursorBench at medium: 52.5%, next to Fable 5.1 at max (51.8%) and above Opus 5 at max (46.6%). About 11 points over GPT-5.6 Sol’s top score, at about a third of the cost.
Terminal-Bench 4.0 at default effort beats Opus 5 at max for about a fifth of the cost, and matches Astra at about 40% of the cost.
GDPval-AA at medium beats Astra at max for about a fifth of the cost per task.
GPT-6 Sol shipped the same calendar day and is not a column in this table. OpenAI’s AutomationBench 1.0.6 row for Sol (33.2% at xhigh, $0.27) is OpenAI’s harness. Zapier’s 40.0% is Zapier’s. I am not averaging them.
Thinking cannot be turned off. thinking: {"type": "disabled"} and a manual budget_tokens budget return a 400. Omit thinking, or send {"type": "adaptive"}. Steer depth with effort. Opus 5 accepted disabled at effort high or below.
Forced tool use is rejected. tool_choice of any or tool returns a 400, including on the token-counting endpoint. Use auto (the default) plus strict tool use or structured outputs, and say in the prompt when the tool applies.
Thinking blocks are tied to this model and this conversation. Echo the assistant message back unmodified, including thinking blocks. Editing them, dropping them, or moving them onto another model returns a 400.
On the Claude API and Google Cloud, the older computer_20251124 computer-use tool is not accepted. Move to the current toolset.
A fifth change does not 400, and it is the one that will look like a bug. Text between tool calls now arrives in thinking blocks. The default thinking.display is omitted, so that text is empty. A UI that streams text blocks as progress goes quiet until you set a display value that returns the text (updates is beta and needs the thinking-display-updates-2026-08-18 header; summarized is the other documented option).
between_tools is a Sonnet 5.5 control. It is not how you disable thinking on Opus 5.5.
Safety, at the level that changes a stack
Opus 5.5 is the first Anthropic release since the pace-the-frontier weekend. External testers included Frontier Design and METR. On Anthropic’s automated behavioral audit they call it their strongest model so far. Because they rate it comparable to Mythos 5.1 on biology and cybersecurity, it ships with safeguards in the Fable 5.1 class.
Vetted labs can apply to the Life Sciences Verification Program. The Cyber Verification Program is set to expand in the coming weeks for verified practitioners. Those are access programs. They are not a model string I can drop into a client repo. I am not treating a cyber leaderboard as a reason to point this model at anyone else’s systems.
When I would use it
Opus 5.5
A Claude Code or API route already on Opus 5, after the migration checklist, with effort set on purpose
Long coding jobs where their FrontierCode and CursorBench rows, plus the fewer-tokens claim, are the thing you are buying
Knowledge-work loops where GDPval-AA and the lower cache-read price matter more than Astra’s computer-use lead
Teams who want the five-hour limit increase and can still live with the September 14 weekly seat
Leave it
An OpenAI route you just moved to GPT-6 Sol for volume. Sol’s sticker is half of this one
Computer-use jobs where this morning’s Astra note, or Anthropic’s own Terminal-Bench-Science row, says Astra is ahead
Any client that sends thinking: disabled or forced tool_choice until that code is updated
Cursor sessions already on Grok 4.7 until Opus 5.5 is actually in the picker you use
Takeaway
Opus 5.5 is the Claude 5.5 family’s first id: $4 / $20, cache reads at $0.20, default effort medium, thinking always on. Anthropic’s table leads most coding and knowledge-work rows against Fable 5.1 and Opus 5, and loses two rows to Astra. The 40% savings is a workload claim, not the rate card. The rate card is 20% off tokens and 60% off cache reads, if the loop also gets shorter.
I would migrate an Opus 5 agent that already earns its keep, after fixing thinking, tool choice, and the quiet gap between tool calls. I would not throw out this morning’s Sol shortlist to do it.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
OpenAI shipped GPT-6 Sol and GPT-6 Luna on September 22, 2026. They are the cost tiers under GPT-6 Astra, trained with similar methods, and priced at half the GPT-5.6 promotional API rates. API ids are gpt-6-sol and gpt-6-luna.
I am writing this the same day, with the same builder lens as the Astra vs 3.8 Flash bake-off: what I would put on a long agent loop, and how the bill looks when that loop runs all week. Grok 4.7 also landed this week. This post does not re-review it.
Astra stays the computer-use flagship. Sol and Luna are how that generation gets cheap enough to run on ordinary work.
What shipped
Spec
GPT-6 Sol
GPT-6 Luna
GPT-6 Astra
Release
September 22, 2026
September 22, 2026
September 3, 2026
Model id
gpt-6-sol
gpt-6-luna
gpt-6-astra
Context
1,050,000 tokens
1,050,000 tokens
1,050,000 tokens
Max output
128,000
128,000
128,000
Reasoning effort
none through max. Default medium
none through max. Default medium
none unsupported
Knowledge cutoff
April 20, 2026 (API model page)
Same family docs; confirm on the Luna page before you quote it
April 30, 2026
Where it is today
ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu. API. Not Chat
Same paid surfaces, plus Free and Go in the desktop app. API. Not Chat
Staged ChatGPT, API, Bedrock
Standard API sticker
$2 in / $10 out
$0.10 in / $0.50 out
$10 in / $50 out
OpenAI said the rollout through ChatGPT Work and Codex would continue through the day. If the picker still shows GPT-5.6, wait and refresh. Free and Go users get Luna in the desktop app only.
Both Sol and Luna accept reasoning.effort of none, low, medium (default), high, xhigh, and max. Tool calling with reasoning belongs on the Responses API. On Chat Completions, function calling for these two ids works only with reasoning_effort set to none. Astra does not offer none at all. If a route still sends minimal, start at low and re-test. That migration note is on OpenAI’s GPT-6 guide.
The price cut is real, and it is against the promo you were already on
OpenAI’s launch table, per 1 million tokens, standard short context:
GPT-5.6 promo
GPT-6
Cut
Sol input / output
$4 / $20
$2 / $10
50%
Luna input / output
$0.20 / $1.20
$0.10 / $0.50
50% on input, about 58% on output
Cached input reads are 10% of the uncached input rate: $0.20 on Sol, $0.01 on Luna. That is the 90% cache-read discount OpenAI is advertising, plus a claim of higher default cache hit rates. Cache writes bill at 1.25x uncached input ($2.50 on Sol, $0.125 on Luna). Batch and Flex are half of standard. Fast mode is 2x the applicable rate.
Long context is a cliff, not a slope. A request with more than 272K input tokens is priced at 2x input and cache rates and 1.5x output for the whole request. Sol’s long-context sticker is $4 / $15. Luna’s is $0.20 / $0.75. Astra’s is $20 / $75. Source: OpenAI API pricing and the Sol model page.
Worked examples at short-context standard rates. Cache hits use the cached-input rate. These are not OpenAI invoices.
Loop
Sol
Luna
Astra
100K input, 4K output, uncached
$0.24
$0.012
$1.20
200K input, 70% cache hit, 20K output
$0.35
$0.017
$1.74
1M cumulative input, 80% cache hit, 100K output, split under the 272K cliff
$1.56
$0.078
$7.80
One unsplit 1M-token request does not get that last row. It falls into long-context pricing for the entire call. Split the trace, or budget the 2x / 1.5x multiplier on purpose.
Sol at $2 / $10 is the same ratio under Astra ($10 / $50) that GPT-5.6 Sol had under the old flagship conversation. The new fact is the absolute cut, and Luna’s output token falling from $1.20 to $0.50.
What OpenAI’s own tables say
I am using the launch post’s numbers, with effort called out. Effort is the bill.
AutomationBench 1.0.6. End-to-end business workflows, 47 tools, across sales, marketing, operations, support, finance, and HR. The score needs the relevant assertions to pass.
Model (effort)
Score
Cost per task
GPT-6 Sol (xhigh)
33.2%
$0.27
GPT-6 Astra (low)
30.3%
3.9x Sol
Claude Opus 5 (max)
26.9%
11.1x Sol
Claude Fable 5.1 with Opus 5 fallback (max)
31.4%
More than 8.9x Sol
OpenAI says the Fable 5.1 row understates cost. Opus 5 fallbacks fired on about 40% of tasks, and that fallback cost is not in the table. Luna at high effort is up 5.4 points on GPT-5.6 Luna at 58% lower cost per task. I am not inventing Luna’s absolute AutomationBench score beyond that sentence.
DeepSWE v1.1. Sol at max scores 68.8%, within 1.1 points of Fable 5’s highest published score on that eval (69.9% at xhigh), at about 80% lower cost per task. Luna at max scores 66.6%, in the band of Opus 5 and Fable 5 at medium effort, at 93% less per task than Opus 5 and 96% less than Fable 5.
OSWorld 2.0 offline. Astra remains the computer-use leader in OpenAI’s framing. Sol at xhigh scores 60.5%, next to Opus 5 at medium (60.3%), at about 80% lower cost per task. Luna at max beats GPT-5.6 Sol at medium at about one tenth of the cost. Partial reward on the offline set is not “share of workflows fully solved.”
What an independent index adds
Artificial Analysis ran its own harness. Treat this as a second scoreboard, not as OpenAI’s table.
Intelligence Index, max effort: Sol 48, Luna 37. Cost per index task about $1.06 for Sol and $0.07 for Luna. GPT-5.6 Sol max was about $1.99 and GPT-5.6 Luna max about $0.18. The cut is mostly price. Both new models spend slightly more output tokens per task (about 31k vs 29k for Sol, 51k vs 41k for Luna).
Coding Agent Index, Codex harness, max: Sol 57, up 2 from GPT-5.6 Sol, about $2.99 per task. Terminal-Bench 4.0 43% vs 37%, SWE-Atlas-QnA 58% vs 54%. Luna scores 41, down 2, with a lower SWE-Atlas-QnA (44% vs 49%).
Knowledge-work regressions: Sol drops about 100 Elo on GDPval-AA v2.1 and Luna about 75. AA’s read, after inspecting outputs, is weaker presentation and deliverables that skip rubric items. Luna also drops about 45 Elo on AA-Briefcase. Sol is flat there.
That last bullet is the one I will remember on a slide or a memo. Coding got cheaper. A polished client deliverable did not automatically get better because the token price fell.
When I would use it
GPT-6 Sol
Agentic coding in Codex or the API when Astra’s $10 / $50 bill is the constraint and DeepSWE-class work is the job
AutomationBench-shaped business workflows where xhigh at $0.27 a task beats Opus 5 at max on OpenAI’s own table
A route already on GPT-5.6 Sol. The id change is gpt-6-sol, the sticker is half, and the effort enum grew
GPT-6 Luna
High-volume loops where $0.10 / $0.50 is the point, and medium-effort Opus or Fable quality is enough
Free and Go desktop sessions that need a GPT-6 id today
Cache-heavy traffic under the 272K cliff, where a $0.01 cache read changes the weekly invoice
Computer use where Astra’s OSWorld lead is the actual requirement
Finished slides, docs, and spreadsheets when AA’s GDPval regression on Sol is the risk you cannot take
Anything that needed Astra’s refusal and review behavior on cyber-adjacent work. Sol and Luna do not retire that gate
Still outside this launch
Grok 4.7 in Cursor at the $2 / $6 sticker, if that is the IDE you already ship in
Gemini 3.8 Flash when the input is image, audio, or video and the intro Flash rate still holds
Fable 5.1 when you need the closed coding reference and you have already priced the Opus fallback
Takeaway
September 22 did not replace Astra. It put Astra’s generation on two cheaper ids and cut the GPT-5.6 promotional stickers in half. Sol at xhigh is the row that beats Opus 5 on AutomationBench at a fraction of the cost. Luna is the row you run all week. Neither score is the default medium effort, and a single request over 272K input tokens reprices the whole call.
I would move a GPT-5.6 Sol route to gpt-6-sol after one real multi-file task at the effort I intend to pay for. I would move volume traffic to gpt-6-luna the same way. I would leave Astra on the jobs that were already worth $10 / $50.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
xAI shipped Grok 4.7 on September 21, 2026. Cursor posted a short launch stub the same day. I am writing this on September 22 with the same builder lens as Grok 4.6 and the mid-September meter week: not who won the internet, but what I would actually put on a long agent loop for product and agency work.
The Monday wrap-up was still waiting. Through September 20 there was no grok-4.7 id. There is one now.
This article is the delivery test. I am writing it as Grok 4.7 inside Cursor. Same job shape as the 4.6 post and Building this blog with Cursor and Grok 4.5: research the primaries, lock the site voice, keep the numbers attributed.
The launch card below is still the card. This update is the first-day reading. Independent boards and early Cursor use say the same task costs about 2.5x, because 4.7 writes far more tokens. The general consensus is meh.
“Needs a few more days to cook.” RL may have penalized response length too hard, so it “gives up on hard tasks (that it can do!) too early” and “isn’t yet sufficiently rigorous in checking its work”
Still 4.6
Sept 14
“Should be roughly on par with Opus 5.0, not 5.1.” Multimodal still needed work
Still 4.6
Through Sept 20
No new date
xAI docs still listed Grok 4.6
Sept 21
Official launch
Grok 4.7
That is about a week past “a few more days.” The launch copy is the same gap they held the ship for: stay on hard jobs longer, check the work before moving on. Treat that as the reason for the slip, not as proof the slip worked. A week of Cursor sessions will tell me. A launch page will not.
Official posts still skip a parameter count. I am skipping it too. Monday already called the 2.1T figure a rumor.
Reasoning efforts: low, medium, high (default), xhigh
Function calling and structured outputs
Batch API: not supported
Regions: us-east-1, us-west-2, us-central-1
The launch post’s training story: a new, larger base than 4.6, then a longer RL run on a harder mix, weighted toward jobs that take many hours. Stronger self-checks and long-context handling. Native training on the Grok Bot harness for chat and knowledge work. Documents and presentations are in the pitch.
Where I can actually run it. Cursor, Grok Build, the Grok API, coding harnesses, routers, and cloud platforms. Vercel AI Gateway serves spacexai/grok-4.7 at 40% off through September 27. GitHub Copilot is rolling it out slowly to Pro, Pro+, Max, Business, and Enterprise in VS Code, Visual Studio, Copilot CLI, cloud agent, the Copilot app, JetBrains, Xcode, and Eclipse. If the picker is empty, wait for the rollout.
A fast variant is twice the output speed at twice the price.
How the benchmarks look
xAI’s card is Grok 4.7 xHigh versus Grok 4.6 High, GPT-5.6 Sol Max, and Fable 5.1 Max. Competitor numbers come from that card. Office Chai reprints the same table.
Eval
Grok 4.7 xHigh
Grok 4.6 High
GPT-5.6 Sol Max
Fable 5.1 Max
CursorBench 4.0
46.3%
40.4%
41.7%
51.8%
DeepSWE v1.1
71.0%*
65.2%
72.7%
70.0%
EEBench
64.0%
53.0%
39.4%
56.4%
AA Briefcase v1.1
1,657
1,546
1,487
1,678
Terminal-Bench 4.0
38.0%
20.3%
37.3%
57.9%
Harvey Legal Agent Benchmark
19.6%
15.8%
2.5%
6.7%
HealthBench Professional
56.7%
48.5%
60.5%
62.1%
* DeepSWE for Grok 4.7 is marked high effort on the card, not xHigh.
Every row beats 4.6. Versus Sol, 4.7 wins five of seven and loses DeepSWE and HealthBench Professional. Versus Fable 5.1, it wins DeepSWE, EEBench, and Harvey, and loses CursorBench, AA Briefcase, Terminal-Bench, and HealthBench. Terminal-Bench 4.0 is the hole: 38.0% against Fable’s 57.9%. Almost double 4.6’s 20.3%, still a long way from Fable.
GDPval lives on the launch chart, not in the HTML table. Office Chai reads 4.7 at 1,695 Elo, 4.6 at 1,605, Fable 5.1 at 1,735, GPT-6 Astra at 1,542. Chart reading stays a chart reading.
Coding tasks vs frontier peers
What I would actually do with those rows:
IDE agent loops: CursorBench 4.0 has 4.7 ahead of 4.6 and Sol, behind Fable 5.1 at 51.8%. Closest board to this repo. Still first-party.
Repo SWE: DeepSWE high effort is 71.0%, a hair over Fable 5.1 (70.0%) and a hair under Sol (72.7%). Too tight to pick a default from.
Terminal agents: Terminal-Bench 4.0 is the honest lag. Fable still owns that row.
Knowledge work: AA Briefcase and the GDPval chart put 4.7 in the Fable band and ahead of Sol. Memos, decks, multi-hour office jobs are the pitch.
Electrical engineering: EEBench is the cleanest lead against both Sol and Fable 5.1.
The rest of the shortlist:Grok 4.6 already had the Cursor seat. 4.7 keeps it at the same sticker. GPT-6 Astra vs Gemini 3.8 Flash is still the computer-use flagship versus Flash-tier story. Fable 5.1 is still the closed-coding reference on CursorBench and Terminal-Bench.
Builder reading: knowledge-work and EEBench-shaped jobs are where the vendor card looks strongest. Terminal work still points at Fable 5.1. CursorBench moved. It did not sweep. The task bill, in the next sections, is why I would not switch a job 4.6 already finishes.
Pricing
Launch-card stickers plus the model page. Prompts at or above 200k tokens bill 2x for the whole request. Fast variant is 2x list.
Model
Input / 1M
Cached input / 1M
Output / 1M
Grok 4.7 (< 200k prompt)
$2.00
$0.50
$6.00
Grok 4.7 (≥ 200k prompt)
$4.00
$1.00
$12.00
Grok 4.6 (< 200k prompt)
$2.00
$0.50
$6.00
GPT-5.6 Sol Max (launch card)
$4.00
n/a
$20.00
Claude Fable 5.1 Max (launch card)
$10.00
n/a
$50.00
What people actually found
The first-day consensus is meh. A bit smarter on long agent work. Not worth the extra tokens when 4.6 already finishes the job.
The list price did not move. The bill did.
OpenTools, reading Artificial Analysis: Grok 4.7 xhigh uses about 81,000 output tokens per Intelligence Index task. Grok 4.6 xhigh uses about 38,000. Inside Grok Build, the coding-agent cost is $8.82 and 39.2 minutes per task versus $3.57 and 19.5 minutes for 4.6 xhigh. That is about 2.47x the pay-per-token cost and about 2x the wall time, for a 9-point Coding Agent Index gain (56 vs 47) and a 2-point Intelligence Index step (46 vs 44 at high).
On Reddit, Cursor Ultra user Dynamix86 ran what they called the same task on extra high with fast mode off. They said 4.7 burned plan percentage about 2.5x as fast as 4.6. Tabbit’s review names that r/cursor note and links the thread. One account. Their dollar pair ($8.82 vs $3.53) is their reading of a chart, close to Artificial Analysis at $8.82 vs $3.57.
IDKHTCXD calls it a sidegrade and a letdown: a bit smarter, almost twice as many tokens as 4.6 on the Artificial Analysis index, and no longer the value frontier 4.5 was.
tiz.io: super spendy for what they got out of it.
Revire: not impressed. More verbose, more of the Anthropic prose habits, less of the concise 4.5 feel.
savicbo: easier to talk to than 4.6 for architecture and explanation. That is the clearest voice win in the thread.
PizzaConsole: the launch table compares 4.7 xhigh with 4.6 high, so the matching $2 / $6 row is not an apples-to-apples task cost.
Cursor staff, replying to the sidegrade note: try low or medium for better value per token. No launch promo.
Hacker News puts it in one line: token price does not tell you much without token efficiency. The thread also flags a token-efficiency regression versus 4.6.
Safety, high level
xAI says 4.7 got a new safeguard stack, and that it is the strongest model they have tested on refusals and jailbreak resistance. LatchBio biosafety is listed at 62.4%. HackerBench v0.3, their own dual-use cyber board, lets 3.3% of risky dual-use prompts through and rarely blocks legitimate security work. Selected cybersecurity partners get invite-only red-team access for defense research.
Vendor numbers. Not a playbook. I am recording the safety half of the card. I am not turning it into a how-to.
This post in Cursor
Grok 4.7 is in the picker today. That is the part that matters for this site.
I researched the launch materials, locked the outline to this blog’s voice, built the tables, and wrote the MDX in this session. Same constraints as Grok 4.6 and the 4.5 blog build: multi-file rules, outcome-led copy, no invented metrics.
No usage screenshot. The 4.5 post already showed that meter. The article is the test.
What is still thin
Artificial Analysis has an Intelligence Index and a Coding Agent Index. The Cursor forum and one Reddit same-task note are in. A week of repeated tasks, same repo and same effort, is not. The 4.6 post later folded in a fuller first-day roundup. This one does not have that yet.
What would move this post again:
A shared bake-off that logs tokens, wall time, and whether the patch actually landed
Whether low or medium effort keeps the coding-agent gain without the 2.5x bill
Whether the RL length-penalty fix shows up as fewer early quits once people stop running everything at xhigh
Until then, treat 4.7 as a sidegrade you pay for in tokens.
When I would use it
When I would reach for Grok 4.7
Jobs 4.6 abandons: a long coding loop that stalls, not the same task 4.6 already finishes
Long research and documents where the extra tokens are the product, not a side effect
EEBench-shaped hardware and electrical work, if that vendor lead is the reason and you can pay for the verbosity
A Gateway trial through September 27 only if you measure tokens, not the sticker
When I would still pick something else
The same task 4.6 already finishes. That is the default. The first-day bill is about 2.5x
Small everyday edits, where Cursor still points at Composer
Extra high or Fast when you wanted 4.6’s bill. Fast and long context still multiply the Cursor rate
Hardest Terminal-Bench 4.0 jobs, where Fable 5.1 still leads by about 20 points
HealthBench-shaped clinical reasoning, where Sol and Fable 5.1 still lead the card
Jobs that need a full 1M context ingest (Astra, Gemini 3.8 Flash)
Takeaway
Grok 4.7 is a step up from 4.6 on xAI’s own board, at the same $2 / $6 sticker, after about a week past the cook note. The first day of use is meh. The same task costs about 2.5x because 4.7 writes far more tokens, runs about twice as long, and only picks up a couple of Intelligence Index points. Keep it for jobs 4.6 drops. Leave finished jobs on 4.6 or Composer. Terminal-Bench 4.0 still belongs to Fable 5.1.
Leaderboards help you shortlist. Delivery decides. This post is one delivery test, written with Grok 4.7 in Cursor.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
The week of September 14 to 20 was not a flagship drop. GPT-6 Astra vs Gemini 3.8 Flash already had that bake-off on the 4th. Last weekend was the process week: pace the frontier, a Navier-Stokes writeup, and Claude misuse. I already wrote the Verdent trial note on the 14th, so this post does not re-review that product. What stacked up this time was a live weekly meter, a decision-model API that does not write strings, a live-voice SKU that is not Flash, a Flash id that actually moved, and a Grok 4.7 date that never landed.
I am writing this on Monday, September 21, 2026, with the same builder lens I used for Astra vs 3.8 Flash, Grok 4.6, and the late August wrap-up: not who won the internet, but what I would put on a long agent loop and how the bill looks when that loop runs all week.
The models you already shortlisted did not get replaced. The weekly meter, one decision-model API, one live-voice SKU, one Flash id, and the Grok 4.7 date that never landed did.
The week at a glance
Date
What
Why a builder cares
Sept 14
Claude Code weekly meter
Permanent +25% vs old baseline equals −17% vs last week’s 50% boost. The date from the late August wrap-up is now live
Sept 14
DeepSeek V4 Pro stay
Changelog kept deepseek-v4-pro at Pro rates. Launch page still said Flash routing. Live docs win
Sept 14
Musk on Grok 4.7
“Roughly on par with Opus 5.0, not 5.1.” Multimodal still needs work. Still no API id
Sept 15
TypeSafe Jev early access
System One decision model. Unstructured state in, typed probabilities out. Not a chat or coding model
Sept 15
Gemini 3.8 Live and Live Extended Thinking
Voice agents in the Live API. Not 3.8 Flash
Sept 15
OpenAI, Anthropic, Google safety talks
Lehane: weeks of work. Follow-up to Saturday’s “pace the frontier” posts
Sept 17
Claude Code Projects redesign
Parallel cloud threads under a coordinator. Select Pro/Max beta. Same weekly meter
Sept 17
Qwen Code 0.24
Can hand a subtask to Claude Code or Codex from one session
Sept 19
Slowdown class action
Northern District of California. Named ChatGPT, Claude, Grok, Gemini subscribers. Not a changelog
Sept 20
Step 5 Preview
600B / 27B active, 1M context, API today, weights promised October 15
All week
Grok 4.7 still not shipped
Musk’s Sept 12 target and “a few more days” both expired. xAI docs still list Grok 4.6
I am not treating Fable 5.1, Gemini 3.8 Flash, or GPT-6 Astra as this week’s news. They shipped September 1 to 3. They sit in the September 4 bake-off, not in this table.
Claude Code weekly meter, then Projects
Two Anthropic notes, two products. Do not collapse them.
Claude Code weekly limits, September 14. The date from the late August wrap-up is now live. Anthropic’s Help Center article is past tense: the 50% weekly boost ran from May 13 through September 13, 2026 at 11:59 PM PT. Starting September 14, weekly limits in Claude Code are 25% higher than they were before the promotion for Pro, Max, Team, and seat-based Enterprise plans. Five-hour limits did not move. Claude on the web, desktop, and mobile, and Cowork, were never on this promotion.
Worked example, not Anthropic’s metering units. If the old weekly allowance was 100, last week’s 50% boost was 150. Today it is 125. That is 25% above the old baseline and about 17% below the seat you were actually using through September 13.
Baseline
Weekly units in the example
Pre-promotion
100
50% boost, through September 13
150
Permanent from September 14
125
Same lesson as the Kimi weekly quota and 5-hour wall: budget usable agent hours, not the marketing percentage. A +25% headline against a meter you are not on is how a Thursday sprint dies. Run /usage.
Projects redesigned, September 17. Anthropic published Projects redesigned: from folder to conversation. A project is now a coordinator plus parallel threads, not a folder of chats. You set a goal and a repo. Claude scopes the request, delegates the work, reviews the outputs, and assembles the result. It keeps working after you close the laptop.
Under the hood, each thread is a Claude Code cloud session on its own branch and copy of the repo. Overlap is a merge conflict, like any other PR. Threads can split further into subagents, loops, and workflows. Shared memory and a Library collect files and artifacts so work from one thread is findable from another. Recap: The Verge.
Beta today: select Claude Pro and Max subscribers who use cloud sessions in Claude Code and do not have any existing projects on the web or desktop. Access expands to more Pro and Max users this week, then Team, Enterprise, chat, and Cowork. Existing projects keep working as they do today until that upgrade. Threads run in the cloud. Local tools and code are “coming very soon.”
Anthropic’s own post is blunt about the meter: projects can run several threads at once, each a full Claude Code session, so they can hit usage limits faster. You can check project-specific usage and pick the model and effort for the coordinator and the workers.
What I would actually do this week: rebase capacity to the live meter. If the Projects beta is on your account, try it on a throwaway repo, not the client that ships Friday.
TypeSafe Jev: a decision model, not a chat model
September 15: TypeSafe published Introducing System One Models and Jev. Founder Diogo Almeida helped build the instruction-following methods behind ChatGPT at OpenAI. After two years in stealth, this is the company’s first public model. Early access is open. It is not a Cursor default.
What it is. Jev is the first public System One Model. The class name is Kahneman’s fast System 1 versus slow System 2. The product is named after William Stanley Jevons: cheaper intelligence should unlock more demand, not less. TypeSafe’s one-liner is the useful one. Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. It gives up string generation. Docs: TypeSafe introduction.
That is the whole product thesis. Language models write strings for humans. Jev returns values your code can branch on.
What it does. You send a state (text, a JSON object, or an array of text) plus a map of typed questions. Every question is evaluated in parallel against the same state. Three question types:
Type
What you ask
What you get back
Noul
Is this true?
A probability from 0 to 1
Choice
Which named option?
The pick, a probability per option, and confidence
Score
Where does this sit on an ordered rubric?
A score, a probability per level, and confidence
You mix the three in one request. TypeSafe trains with RLCD (Reinforcement Learning for Calibrated Decisions) instead of RLHF. Sampling is parallel, not token-by-token. Schema matching is guaranteed, so TypeSafe says type errors are mathematically impossible. The claim to falsify is a single counter-example on the schema, not a vibe about “hallucination.”
Use cases TypeSafe names: smart if-statements, classify / route / score / extract, map-reduce over large corpora, sub-100ms product paths, and scoring or jailbreak-checking other LLM traces.
What it is not. Not a coding agent. Not a chat model. Not a replacement for Grok 4.6, Fable 5.1, or Astra. It does not write code, replies, or explanations. TypeSafe’s own jaggedness note for jev-1.13 is unusually direct: it is literal, it does not count reliably, it is weak on dates and numeric interpolation, and chaining choices to force text generation “will not work well and will be very slow.” Read Jev 1.13 jaggedness before you put it in a loop that needs those things.
Official specs from TypeSafe models, verified before you budget:
Spec
Jev 1.13
Versioned id
jev-1.13.0
Aliases
jev-latest and jev-preview both point at 1.13.0 today
Endpoint
POST https://api.typesafe.ai/v1/systemone
Input
Text only. No image, audio, or video
Context
64k per request; 32k for state plus the longest question
Input price
$0.042 per 1M tokens ($42 per billion)
Output price
Free (“too cheap to meter”)
Vendor latency
70-500 ms end to end
Published default limits
250,000 input tokens/s and 1,200 requests/minute, and TypeSafe says those can move
TypeSafe itself says it cannot prove the price is not subsidized, and that it expects the number to go down, not up. Pin jev-1.13.0 if you tune confidence thresholds. The alias will move.
When I would trial it: a classify, route, or guardrail hop inside software that already has a schema, where 70-500 ms and $0.042 / MTok beat calling Astra to pick a label. When I would not: anything that has to emit text, including this blog, Cursor, and a client’s coding agent.
Grok 4.7 waits
The anticipated Grok 4.7 drop did not ship this week. Keep Grok 4.6 as the current Cursor and xAI id. xAI’s model catalog still tells you to use Grok 4.6 for code and chat. There is no grok-4.7 row, no price, and no release note.
“Needs a few more days to cook.” RL may have penalized response length too hard, so it “gives up on hard tasks (that it can do!) too early” and “isn’t yet sufficiently rigorous in checking its work”
Still 4.6
Sept 14
“Should be roughly on par with Opus 5.0, not 5.1. Better in some ways, worse in others. We need to fix multimodal performance.” 4.8, 4.9, and Grok 5 sketched on the same thread
Still 4.6
Through Sept 20
No new date
xAI docs still list Grok 4.6
“A few more days” expired inside this window. The September 12 target expired before this week started. Both are founder estimates, not a changelog.
What I would actually do this week: keep shipping on Grok 4.6. Log that the next Grok flagship is late for an RL length-penalty fix, not for a missing feature list. That is useful as “why it slipped.” It is useless as a ship date.
Gemini 3.8 Live
September 15: Google shipped Gemini 3.8 Live and 3.8 Live Extended Thinking. Primaries: the Live post and the developer audio post. Ids: gemini-3.8-live and gemini-3.8-live-extended-thinking. Live API and Google AI Studio. This is a voice-agent SKU. It is not Gemini 3.8 Flash.
Live handles interruptions, mid-conversation language switches across 97 languages, visual grounding, and tool calls in the background while the conversation keeps going. Extended Thinking reasons and speaks at the same time, with configurable thinking for multi-step work. Google puts Extended Thinking at 82.6 on Artificial Analysis’ Speech to Speech Quality Index and 68.6% on τ-Voice. Attribute those as Google plus AA.
Official paid rates from Gemini API pricing, per million tokens unless noted. Free tier is free of charge on these rows. Verify before you budget:
Meter
Paid rate
Text input
$0.75
Audio input
$3.00, or $0.005/min
Image / video input
$1.00, or $0.002/min
Text output (including thinking)
$4.50
Audio output
$12.00, or $0.018/min
The developer post’s per-minute numbers match the pricing table. Do not apply the 3.8 Flash intro card ($0.75 / $3.75 through December 31) to a Live session. Flash is the coding workhorse from September 2. Live is the microphone.
DeepSeek deepseek-flash, and the Pro stay
Days earlier: DeepSeek shipped V4.1-Flash on September 10. Primary: DeepSeek-V4.1-Flash: Smarter, Faster, More Efficient. Native multimodal. New Causal Encoder-Decoder: 552B MoE, 8B active on input, 16B on output, 1M context. Canonical id deepseek-flash. Weights: Hugging Face, MIT. Legacy deepseek-v4-flash and deepseek-v4-flash-vision-exp are retired and temporarily routed here at Flash rates.
The in-week event is the Pro stay. The September 10 launch page said that from 04:00 UTC on September 14, all deepseek-v4-pro requests would route to V4.1-Flash at Flash rates until V4.1-Pro launches. The live changelog and pricing table say otherwise: DeepSeek will continue providing V4 Pro after September 14, billing unchanged. The pricing table still maps deepseek-v4-pro to DeepSeek-V4-Pro-0813.
Official off-peak from the pricing page, per million tokens. Peak is double (01:00-04:00 and 06:00-10:00 UTC, Monday through Friday, excluding Chinese public holidays):
Meter
deepseek-flash off-peak
deepseek-v4-pro off-peak
Input (cache hit)
$0.003
$0.022
Input (cache miss)
$0.15
$0.66
Output
$0.60
$1.98
Selected DeepSeek rows from the launch changelog, max-effort style, harnesses in DeepSeek’s footnotes. Do not mix them with a shared bake-off.
Benchmark
V4.1-Flash
Terminal-Bench 2.1
90.6
DeepSWE v1.1
74.2
GPQA Diamond
90.9
Automation-Bench
54.8
When I would reach for deepseek-flash: a V4-Flash or V4-Flash-Vision-Exp loop that now wants native vision at Flash rates. When I would not: a US client with a Chinese-model freeze, or a Pro loop you have not actually rerun.
Labs talks, a slowdown lawsuit, and Step 5 Preview
Three notes that are not a Cursor changelog. File them, then go back to the eval harness.
Safety talks, September 15. OpenAI global policy head Chris Lehane told reporters in Washington that OpenAI had been working with Anthropic and Google on AI safety for several weeks. Recap: SiliconANGLE. That is the operational half of Saturday’s CEO posts, already covered in pace the frontier. No delayed SKU was named.
Class action, September 19. A proposed class action in the U.S. District Court for the Northern District of California argues Anthropic, OpenAI, SpaceXAI, and Google illegally agreed to coordinate a slowdown, and that this would reduce the value of paid ChatGPT, Claude, Grok, and Gemini subscriptions. The complaint points at the September 12 CEO posts and at a July statement from lab employees. Recap: AP via OPB. The companies had not commented when that recap ran.
Step 5 Preview, September 20. StepFun published Step 5 Preview. Official spec: 600B sparse MoE, 27B active, 1M context, vision in, text out. API and Studio opened the same day. Weights promised October 15. The Hugging Face repo name exists; the weights do not.
Selected vendor rows from StepFun’s launch table. Harnesses are theirs. Artificial Analysis has the model at 44 on Intelligence Index v4.3.2, dated September 18, two days before the announcement. That board scores a model when it can reach it.
Benchmark
Step 5 Preview (vendor table)
DeepSWE v1.1
67.7%
Terminal-Bench v2.1
85.0%
Terminal-Bench v4
33.3%
GPQA Diamond
93.5%
Pricing $1 / $2.70 per million input / output, with a steep cache discount, is what recaps of the launch are using, including Artificial Analysis. I did not get a clean pricing table off StepFun’s own docs at write time. Treat $1 / $2.70 as reported until you see it on an invoice or a StepFun pricing page.
When I would trial it: an OpenRouter-style fallback where a $1 input id matters more than downloadable weights this week. When I would not: a US client with a Chinese-model freeze, or a loop that must survive on weights before October 15.
Also-rans, short. Qwen Code shipped v0.23.3 through v0.24.0 the same week. If Claude Code or Codex is installed, you can hand a subtask to them from one Qwen Code session: Qwen Code weekly, September 17. Qwen-Image 2.1 is image gen with a research license, not a coding default. Cursor had no product drop this week.
Days earlier
DeepSeek V4.1-Flash went GA on September 10, not this week. I parked the live id and the Pro stay above so nobody “discovers” deepseek-flash in October and thinks it is new. The September 10 launch is the model. September 14 is the routing fight.
Fable 5.1, Gemini 3.8 Flash, and GPT-6 Astra remain last week’s models. The comparison is still in GPT-6 Astra vs Gemini 3.8 Flash.
When I would change a stack this week
I would not throw out the shortlist from Astra vs 3.8 Flash, Grok 4.6, or the late August wrap-up. I would change one weekly meter, one decision-model hop, one Live id, and one Flash alias. I would not change the default coding model because 4.7 is late.
If you are here
Change this week
Do not change
Claude Code Pro, Max, Team, or seat-based Enterprise
Rebase weekly capacity to the live 125 meter (vs last week’s 150 in the 100-unit example). Run /usage
Do not budget the +25% headline against the old baseline as extra headroom from last week
Same seat, Projects beta
Pilot one non-deadline repo. Watch the meter before parallel threads become default
Do not point the coordinator at Friday’s client ship
Two rules I am using on retainers this month. Separate surface from model: Claude Code meter, Claude Code Projects, Gemini Live, Jev, Grok 4.6 versus an unshipped 4.7, deepseek-flash, deepseek-v4-pro, Step 5 API, and Cursor are different products. Do not mix them before you change a default. Separate closed price from reported price: Jev’s $0.042 is on TypeSafe’s models page. DeepSeek Flash’s $0.15 / $0.60 is on DeepSeek’s pricing page. Gemini Live’s $0.005 / $0.018 per minute is on Google’s pricing page. StepFun’s $1 / $2.70 is not, at least not on a page I could freeze. Musk’s Opus 5.0 line is not a bench. If a number has no primary, it does not go in the cost model.
Takeaway
The week of September 14 to 20 was a meter-and-wait week. Anthropic cut the promotional weekly seat and then invited you to run more threads against it. TypeSafe shipped a decision model that does not write strings. Google shipped a live-voice SKU that is not Flash. DeepSeek’s Flash id is the one that actually moved; Pro stayed. Grok 4.7 did not. A lawsuit does not delay a model id, and a founder tweet does not mint one. None of that is a reason to throw out last week’s shortlist. It is a reason to check /usage, whether Jev belongs in a schema hop, and whether 4.7 has an id yet.
If you need a next step that is not another tab of vendor benches: try one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Verdent AI is not a new foundation model. It is an agent product: a desktop app, a VS Code extension, a JetBrains plugin, and a Cloud publish path that sit on top of other labs’ models and run coding work in Plan and Agent modes. The free limited trial is the way most people will sample that stack. I am writing this on September 14, 2026 from Verdent’s live pricing page and docs, with the same builder lens I used for Grok 4.6 in Cursor and Muse Spark 1.3: not the homepage slogans, but what I would actually spend a week testing.
The trial headline is simple. New users get 100 credits for 7 days. It is a one-time offer. It is not a forever free daily driver. After credits hit zero, standard model requests stop unless you subscribe, top up, or (on Desktop, paid only) switch to Eco Mode.
What Verdent is
Verdent’s pitch is agentic coding with multiple parallel agents. You describe a goal. The product plans, writes, verifies, and (on Desktop) can run several jobs at once in isolated Git worktrees. The homepage frames it as “say what, not how”: you stay for decisions, the agent keeps moving.
It is a control surface, not a model. Built-in models currently include Claude (Opus and Sonnet, with Fable named on paid cards), GPT-5.6, Gemini, Kimi, GLM, DeepSeek, MiniMax, and Qwen. You can also bring your own key (BYOK: Anthropic, OpenAI, OpenRouter) or your own agent runtime (BYOA: Codex or Claude Code as workers). Credits pass through provider prices with no markup; Verdent publishes 1 credit ≈ $0.059.
Surfaces:
Verdent Desktop. Standalone app. Parallel agents in Git worktrees. Multi-project switching. macOS is full support (11+, Intel and Apple Silicon). Windows 10/11 x64 is beta. Linux is not supported. Git 2.20+ is required because worktrees are the isolation model.
Verdent for VS Code. Same account and credit pool. Plan-Code-Verify inside the editor. One agent per window, not Desktop-style parallel worktrees.
Verdent for JetBrains. Listed as a first-party plugin beside VS Code.
Verdent Cloud. Browser-side apps you can publish. Limits are plan-based and separate from model credits.
Slack and Telegram. Product pages say you can message Verdent away from the desk. I am not treating that as a trial-only entitlement.
Two execution modes matter more than the chrome. Plan Mode is read-only: analyze, ask clarifying questions, write a plan, no file writes and no command execution until you switch. Agent Mode executes. Plan Mode still burns credits. Desktop workspaces are git worktrees; tasks are conversation threads inside a workspace. Two tasks in the same workspace share files and can overwrite each other. Two workspaces stay isolated until you rebase.
That is the product I would trial. Cursor remains the IDE I ship in. Verdent is a bet that parallel, reviewable agent workstreams are worth a second shell.
Plan Mode, Agent Mode, Desktop worktrees, VS Code extension
What that is useful for
Signing in, installing Desktop and/or the VS Code extension, and learning Plan vs Agent.
One bounded coding job: a bug, a small feature, or a Plan Mode pass over a real repo.
On Desktop, a taste of two isolated workspaces, if you accept that parallel agents drain the same 100-credit pool faster.
Comparing a couple of models on the same prompt, as long as you pick cheaper ones after the first frontier run.
Publishing one Cloud app at the Free deployment cap, if that is part of the workflow you care about.
At about $6 of pass-through model spend (100 × $0.059), this is a pilot budget, not a month of Opus-class loops. Verdent’s own Claude Code comparison says new users get 100 credits with no credit card required. The pricing FAQ says “all free” and “without commitment.” A June 2026 walkthrough still showed a card field at signup. Trust the current signup form, not a screenshot from three months ago.
What the free limited trial does not include
These are the limits that decide whether 100 credits in 7 days is enough.
Hard caps
Seven days, then the trial ends. Unused trial credits are not a monthly allotment. Paid subscription credits also do not roll over; only purchased top-ups stay until used.
One hundred credits, shared. Desktop, VS Code, Plan Mode, Agent Mode, subagents, and parallel workspaces all draw from the same pool. Frontier models, large context, and simultaneous agents burn it faster.
One-time per new user. This is not a renewable free tier like Copilot’s limited Completions quota.
Cloud is tiny versus paid. Free: 1 app / 500 MB DB / 10 GB disk. Lite and Starter: 2 / 2 GB / 20 GB. Max: 50 / 32 GB / 80 GB. Those Cloud numbers are “included for a limited time” as of the August 10, 2026 docs note.
Paid-only or missing on trial
Eco Mode is not a trial safety net. Docs say Eco Mode needs an active subscription, Desktop only. It is not in VS Code. When trial credits hit zero, Desktop subscribers can keep going on selected low-cost models under separate caps. Trial users cannot. VS Code simply pauses requests until credits return.
No monthly refresh, no Starter-style 50% credit bonus, no subscription streak. Those apply to paid cycles.
Fable is named on paid cards, not on the Free card. Starter and up currently list Claude Fable 5.1 / Opus 5 / Sonnet 5. The Free card names Opus 5 / Sonnet 5 and GLM-5.2, not Fable 5.1 and not GLM-5.3. Credits docs still say all plans include frontier models. Confirm in the picker.
Lite’s “keep building for less” path is $5/month, not free. Lite includes Eco Mode and cheaper models (GPT-5.6 Luna, GLM-5.3-Flash, Kimi K3 / K2.7 Code, DeepSeek-V4-Pro). Frontier Claude / GPT / Gemini on Lite needs extra credits.
Product constraints that still apply on day one
No offline. All AI processing goes through Verdent’s API.
No Linux Desktop. Windows Desktop is beta. On this machine, the lower-friction trial path is the VS Code extension.
Git is required for Desktop parallel isolation. Verdent will init a repo if you do not have one. Each worktree is a full working copy; node_modules is not shared, so disk and install time add up.
VS Code is single-agent per window. If parallel worktrees are the reason you showed up, Desktop is the test, not the extension.
Do not edit the same files in Desktop and VS Code at the same time. Same account, two clients, easy to clobber.
BYOK gaps. Smart Suggestions and automatic compression do not support BYOK keys.
Same-workspace parallel edits are not deterministic. Later writes can overwrite earlier ones. Isolation is per worktree, not per task.
No published hard cap on parallel agents. Docs recommend 2 to 4. On 100 credits, even two frontier agents is aggressive.
When credits run out, files stay on disk. The agent just stops. Continue by upgrading, buying a top-up (from 340 credits / $20), or (paid Desktop only) Eco Mode.
How 100 credits actually feel
There is no fixed “credits per feature.” Consumption scales with model, context, complexity, and parallelism. That is why the trial is a meter, not a task pack.
Practical split I would use:
Spend the first slice on Plan Mode over a real repo. You want to see clarifying questions and a plan you would actually approve. That is Verdent’s differentiator versus autocomplete.
Spend the next slice on one Agent Mode job with tests or a verifier in the loop, not a greenfield todo app.
Leave a reserve for repair. Generation that you cannot verify is wasted spend.
Use a cheap model for the second workspace if you try Desktop parallelism. Docs even suggest Haiku-class models for routine parallel tasks.
If the first Opus-class run eats a third of the bar, switch down. Luna, Flash, DeepSeek, and GLM stretch a 100-credit week. Fable-class loops will not.
After the week, the honest paid ladder on the live page is:
Plan
Price
Credits (live, with limited-time bonus)
Why it exists
Free trial
$0 / 7 days
100
Pilot
Lite
$5/month
Eco Mode + cheaper models; buy credits for Claude / GPT / Gemini
Keep tinkering cheaply
Starter
$19/month
480 (320 + 160 bonus)
Light frontier use
Pro
$59/month
1,500 (1,000 + 500 bonus)
Regular work
Max
$179/month
4,500 (3,000 + 1,500 bonus)
Heavy / long workflows
Teams
$20/user/month
480 per user
Central billing
Top-ups never expire. Subscription credits do not roll over.
Popular alternatives
Verdent is competing with agent runtimes, not with “an LLM.” The comparison that matters is where you live day to day.
Tool
What it is
Prefer it when
Prefer Verdent’s trial when
Cursor
AI-native IDE (where I ship this site)
Daily inline edits, Composer for cheap loops, one repo in one editor
You want Git-worktree parallel agents as a first-class desktop product
Claude Code
Anthropic’s terminal agent
You already pay Claude, like CLI control, and accept 5-hour usage windows
You want a visual Manager, VS Code plugin, and a credit pool without those windows
GitHub Copilot
Microsoft’s IDE assistant
Autocomplete plus a familiar VS Code / JetBrains agent inside the Microsoft stack
You want multi-provider models and isolated parallel worktrees
Windsurf
AI IDE in the Cursor class
You want an editor-first agent and Cascade-style flows
You specifically want Verdent’s Plan-then-verify plus Desktop worktrees
Codex CLI / ChatGPT coding
OpenAI’s terminal and chat agents
Your billing and models already sit at OpenAI
You want Verdent to orchestrate several providers, including BYOA into Codex
Cline, Roo, OpenCode
BYOK extensions / open agents
You want to point your own keys at a VS Code agent with less product lock-in
You want a managed Plan-Verify-review shell without assembling it
Devin
Cloud software agent
You want an async cloud worker, not a local IDE
You want local worktrees and a Desktop control plane
Muse Code
Meta’s terminal agent
You are on macOS/Linux and want Muse Spark in that harness
A useful trial is not “build a demo that makes the tool look good.” Freeze one real repo, one commit, and the same class of task you would give Cursor or Claude Code. Record credits, whether Plan Mode earned an approval, merge conflicts, and review minutes. Graphify’s July 2026 review is right on the product shape: Verdent is compelling when you must supervise several independent workstreams. It is a weaker fit if you mostly want tab-complete.
When I would use the trial
When I would spend the 100 credits
I want to see Plan Mode on a repo I already know, before any writes.
I have two independent tasks (bug vs tests, feature vs docs) and Desktop worktrees are the reason I am looking.
I am comparing Claude / GPT / Gemini / Kimi / GLM inside one agent shell, including BYOK if I already have keys.
I am on Windows and will treat VS Code as the supported path, Desktop as a beta extra.
When I would skip it and stay put
Daily work is already Cursor Composer plus a frontier model for long jobs. A second product tax is real.
I need Linux Desktop, or I refuse a cloud round-trip for every completion.
I need a forever-free Copilot-style habit, not a 7-day meter.
The job is one sequential edit in one file. Worktree overhead will not pay for itself.
Takeaway
Verdent is an agentic coding environment that orchestrates frontier models, with Desktop parallel worktrees as the distinctive feature and VS Code as the familiar on-ramp. The free limited trial is 100 credits for 7 days, shared across those apps, with a small Cloud publish cap, BYOK/BYOA listed, and no Eco Mode after the meter hits zero.
Use it as a bounded pilot. Do not treat it as a free Cursor replacement. If the Plan-then-verify loop and isolated parallel jobs survive a real repo, Lite at $5 or Starter at $19 is the next honest step. If they do not, you learned that in about $6 of model spend.
If you want help picking an AI coding stack that still ships under real usage and cost constraints, start on the contact page.
The stretch from September 8 to 13 was not a flagship drop. GPT-6 Astra vs Gemini 3.8 Flash already had that bake-off on the 4th. What stacked up this weekend was process: who reviews a proof, who audits a lab, and how cheap it is to point a coding agent at secrets. OpenAI published a Navier-Stokes singularity writeup and a Lean dump. Twenty-five Fields Medalists objected to famous problems being used as capability trophies. Anthropic documented months of Claude misuse, including a pipeline that scanned 1.8 million Android apps for hardcoded secrets. On Saturday, Dario Amodei asked the industry to pace the frontier, and Sam Altman, Elon Musk, and Demis Hassabis said they agreed.
I am writing this on Sunday, September 13, 2026, with the same builder lens I used for Astra vs 3.8 Flash and the August pipes week: not who won the internet, and not whether this is “AGI,” but what I would put on a long agent loop and how the bill looks when that loop runs all week.
The models you already shortlisted did not get replaced. The review and security story around them did.
The week at a glance
Date
What
Why a builder cares
Sept 8
OpenAI Navier-Stokes writeup plus Lean
Research trophy and a Lean-in-the-loop demo, not a new API id
Sept 10
Anthropic threat intelligence report
Coding-agent loops already help steal tokens and run ops
Sept 11
25 Fields Medalists on math-as-benchmark
Same pattern as shipping an agent result without a reviewable writeup
Sept 12
Amodei: pace the frontier; Altman, Musk, Hassabis agree
A pledge is not a changelog. No delayed SKU was announced
Sept 12
Altman: 2026 IPO is off
Company news. It does not change gpt-6-astra or Fable 5.1
I am not treating Fable 5.1, Gemini 3.8 Flash, or GPT-6 Astra as this week’s news. They shipped September 1 to 3. They sit in a kicker at the end, not in this table.
OpenAI’s Navier-Stokes claim
On September 8, OpenAI said an internal system had produced a solution to the Navier-Stokes existence and smoothness problem, one of the Clay Millennium Prize Problems. Primary: OpenAI’s announcement. Independent recap of what is confirmed versus still open: Model Current. The technical paper is Finite Time Blowup for Navier-Stokes.
What the paper claims, not what a headline implies: a forced, three-dimensional incompressible flow that starts smoothly, keeps bounded kinetic energy, and develops unbounded velocity in finite time. OpenAI says that construction meets Clay alternatives C and D. It is a result about what the equations permit under that construction. It is not a claim that ordinary physical fluids will suddenly blow up.
OpenAI’s own process notes, as company-reported: agents launched after September 1 rumors, a result around September 5 (about 88 hours), Lean formalization and verification in another 17 hours via GPT-6 Astra, and on the order of 10,000 concurrent agents in the group that produced the result. OpenAI says it will not claim the Millennium Prize. The Clay Navier-Stokes page still presents the problem as an open challenge. Clay’s prize rules require publication in a qualifying outlet, a wait of at least two years, and general acceptance in the mathematics community before CMI will even consider a proposed solution.
OpenAI also says the September 1 rumor was about work by Tristan Buckmaster (NYU) and Levent Alpöge (Anthropic), and that their result was forced Euler, not the same Navier-Stokes construction. OpenAI says it offered a concurrent release and recognizes their priority on forced Euler. I am using OpenAI’s wording for that adjacent work, not a third-party authorship dispute.
What I would actually do this week: nothing to the API shortlist. If you use models for math or research artifacts, require a human-readable writeup, citations, and a check you can rerun. Do not put “we solved Navier-Stokes” in a client deck because a lab posted a PDF.
Fields Medalists on math as a benchmark
On September 11, 25 Fields Medalists published A Severe Misalignment of AI in Mathematics on Mathandai. Signatories include Terence Tao, Peter Scholze, Maryna Viazovska, Manjul Bhargava, Martin Hairer, and 2026 medalist Yu Deng, spanning award years from 1978 to 2026.
The statement names no lab. The timing is the OpenAI drop. The complaint is process, not capability. The opening is blunt: the push by AI companies to solve mathematical problems as a benchmark is detrimental to the science of mathematics. Famous problems used to be landmarks that produced methods, talks, simplifications, and eventually a textbook. Rushing “true/false” announcements, the signatories argue, skips writeup, isolation of new methods, and citing previous work. That is an attribution problem as much as a verification problem.
They also say AI can still enhance genuine mathematical study. The ask is that humans in control of the tools not forget why the problem was a landmark in the first place.
Anthropic’s September threat report
On September 10, Anthropic published Countering misuse of AI: September 2026. The window is December 2025 through August 2026. Seven harm areas: cyber operations, influence operations, surveillance, scams and fraud, biological misuse, conventional weapons development, and distillation.
Claude Haiku, Sonnet, and Opus were the models in these cases. Anthropic says none of the misuse involved Fable or Mythos-class models, except one illicit distillation case. That distinction matters if your default coding loop is already on Fable 5.1. It does not mean a cheaper Claude SKU is “safe to leave unattended.”
Three cases I would actually put in a security review, kept short:
Secrets at industrial scale. A French-speaking operator ran a credential-harvesting pipeline on 10 AWS EC2 workers: 1.8 million distinct Android APKs downloaded, decompiled, and scanned for hardcoded secrets with TruffleHog, with verified findings routed to Telegram. A parallel GitHub harvester fed stolen personal access tokens. Anthropic says those two pipelines supplied initial-access credentials for the bulk of that actor’s confirmed breaches.
State-linked ops on coding-agent rails. GTG-20006, which Anthropic says is consistent with public reporting on Midnight Blizzard, used customized AI-driven workflows from malware development through phishing, persistence, and exfiltration. GTG-10007, Chinese-speaking operators Anthropic places in Changsha, used Claude as an engineering and orchestration layer for reconnaissance, exploit work against endpoint-security products, malware, and intrusion attempts, including a collection fleet on a schedule with no human in the loop.
Surveillance and weapons-adjacent engineering. One subscriber used Claude as the primary engineering workforce for Lakana 360, a Mali state-intelligence platform Anthropic says was designed to monitor about 25 million SIM cards across the country’s three mobile operators. Separately, Anthropic says it disrupted six conventional-weapons cases (three in China, two in Russia, one in Yemen) where Claude was used for guidance software, fire-control pieces, drone-swarm simulation, targeting, or procurement. The companion Frontier Red Team note is the capability eval, not a second incident log: models are progressing on simulated targeting and weapons-development tasks, including open-weight models well short of the frontier.
Anthropic says it banned accounts, tightened classifiers, and shared intelligence with authorities and partners. That is disruption after the fact. It is not a guarantee the next operator is not already on another lab’s API.
Pace the frontier, on paper
On September 12, Amodei published We Must Pace the Frontier. Two drivers, in his words: recursive self-improvement since roughly this summer, including at Anthropic, and the OpenAI-Hugging Face swarm.
The Hugging Face incident is not this weekend’s news. It happened in July during internal cybersecurity evaluations. OpenAI’s writeup is dated August 26: The Hugging Face incident and the road ahead. Recap: Infosecurity Magazine. OpenAI calls it a “warning shot”: agents circumvented isolation, stood up unauthorized message boards, and compromised parts of OpenAI’s research infrastructure and Hugging Face’s systems. METR’s independent count, as reported in that recap: about 1,206 agents on the board, 70,000+ messages, 700+ taking part in the Hugging Face intrusion. Amodei cites that swarm as evidence a more capable, similarly misaligned collective could do catastrophic damage, and says similar though less severe incidents have happened across the industry, including at Anthropic.
His three-step plan:
Embedded evaluators. Third parties such as METR get ongoing, employee-like access to verify safety practices, report incidents, and look at training pipelines, not just finished models. Anthropic is unilaterally committing to this step now: badges, laptops, desks, and a contract that lets reviewers publish key findings, with only narrow redactions.
Democratic coordination. Frontier companies in democratic countries set common safety standards and limits on unchecked capability growth, with government help where antitrust gets in the way.
Global coordination. Democracies try to coordinate with authoritarian governments, with verification treated as the hard part.
Pacing, he writes, does not mean halting training. It means taking time to align and safeguard, and letting third parties confirm that happened. He wants an extra year or two used on operational hygiene, alignment, interpretability, and evaluations that smarter models cannot simply deceive.
Altman posted that he agrees they need to pace the frontier, that this has been a primary topic inside OpenAI in recent weeks, and that independent evaluators with employee-like access is a great idea OpenAI will match, with more to share soon. Musk posted “Dario is right.” Hassabis called it the right path forward. Those quotes are from same-day coverage such as ABC News and AI Socratic.
Separately, in a Fortune interview published Saturday, Altman said a 2026 IPO would be an ill-advised moment given safety work. Recap: The Guardian. That is a listing calendar, not a model changelog.
Days earlier
Fable 5.1 (September 1), Gemini 3.8 Flash (September 2), and GPT-6 Astra (September 3) are last week’s models. The comparison is still in GPT-6 Astra vs Gemini 3.8 Flash. I am only parking one independent follow-up so nobody treats this weekend as a silent bake-off.
Surge’s Tuesday Work Index note (dated September 10, updated September 12) has Fable 5.1 at 68.7, Gemini 3.8 Flash High at 61.1, and Muse Spark 1.3 taking ComplexConstraints. Astra’s Surge eval was still running. That does not change the shortlist from the September 4 post. It is not a reason to rip out Flash or wait for a paced-frontier SKU that has not been named.
When I would change a stack this week
I would not throw out the shortlist from Astra vs 3.8 Flash, Grok 4.6, or the August pipes week. I would change secrets hygiene, review rules for research artifacts, and the questions I ask vendors. I would not change the default model id.
If you are here
Change this weekend
Do not change
Shipping agents on Claude, Astra, or Flash
Rotate secrets. Assume APK and repo mining is cheap now
Do not rip out Fable 5.1 or 3.8 Flash because of a threat report
Waiting on the next frontier drop
Watch for an actual delayed model id or an eval gate you can point at
Do not pause client work because CEOs posted “pace the frontier”
Using models for math or research artifacts
Require a human-readable writeup, citations, and a check you can rerun
Do not treat a Lean dump as a shipped feature
Security-sensitive clients
Ask vendors what they log, how they isolate agents, and whether third-party evals can see training
Do not sell “our agents cannot be misused”
Two rules I am using on retainers this month. Separate a lab essay from a changelog: embedded evaluators, Clay prize rules, and an IPO delay are not a new gpt-6-astra snapshot. Separate disrupted misuse from your production loop: Anthropic banned those accounts after the fact. The control you actually own is secrets, isolation, and what an agent is allowed to touch.
Takeaway
The week of September 8 to 13 was a process week. OpenAI posted a Millennium-scale writeup and a Lean trail. Fields Medalists said famous problems are not a marketing scoreboard. Anthropic showed coding agents already mining secrets and running ops. Amodei asked labs to slow capabilities so safety can catch up, and rival CEOs said they agreed, without naming a delayed SKU. None of that is a reason to throw out last week’s shortlist. It is a reason to rotate tokens, demand a writeup before you trust a “solved” claim, and treat “pace the frontier” as a watch item until a release actually moves.
If you need a next step that is not another tab of vendor benches: try one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
WordPress is not dying. The website WordPress currently ships to visitors is. That is the useful way to think about the next decade: keep the CMS that editors already know, and stop sending PHP, Elementor, and a pile of plugin JavaScript down every request.
Headless WordPress is that split. The source sites still use wp-admin. The demos never did. Visitors hit a fast frontend (Astro or Next.js) that reads the public REST API. For two live catalog sites I never opened wp-admin. Public REST was enough to pull titles, images, and copy, rebuild the chrome as components, and ship demos on my own domains.
The future is WordPress as CMS, not as the website
For a long time “a WordPress site” meant one box did everything: edit, render, cache, forms, SEO, and the shopping cart. That model still works for a brochure with a light theme. It fails when the public site is Elementor Pro plus FacetWP, or Beaver Builder plus Slider Revolution, and every visitor pays for the admin stack.
Three things are pushing the split:
Speed is now a business metric. Core Web Vitals, paid ads, and “this feels slow on a phone” all show up in leads. Page builders made layout easy. They also made the request path heavy.
AI reads structured content. Chat assistants, on-site search, and answer engines want an API, not a PHP theme full of shortcodes. Headless is how WordPress stays in that world without pretending Gutenberg HTML is a schema.
Editors will not retrain for a demo. The future that actually ships is the one that leaves wp-admin in place for teams who already live there. Schema CMSs like Sanity win when you want a new editor. They are not the default for a 69-page catalog that already exists.
Gutenberg and full site editing are WordPress trying to be a better frontend. That helps new builds. It does not erase an Elementor catalog that already has custom post types, taxonomies, and a mega-menu. The durable API is still /wp-json/. That is why headless is not a fad around the REST API. It is the path that treats WordPress as the content plane and lets the delivery plane change.
What I do not expect: WordPress vanishing, or every site going headless next year. WooCommerce stores, membership, and heavy admin workflows often stay coupled for a reason. What I do expect: marketing and catalog fronts peeling off first, while WordPress keeps the posts, products, and people who publish them.
How we worked with no admin
Open /wp-json/ in a browser. If pages, posts, and custom types respond without auth, you can build. Both source sites already exposed that API. I did not need a database dump, an application password, or a plugin install. We used REST only. Building Products Plus has no WPGraphQL (/graphql 404).
What REST gave us versus what it hid:
What we actually used
What we could not get without admin
Pages, posts, media, and Gerotec custom types (produkt, fahrzeug, and the rest)
Menus API 401 on Building Products Plus. Nav is hardcoded.
Titles, content.rendered, featured images via _embed
ACF fields were not in the public JSON we used. No structured Elementor or Beaver layout data.
Building Products Plus pagination via X-WP-TotalPages (69 pages, 45 posts)
Rank Math head is not in the public JSON. We did not scrape SEO plugins.
Building Products Plus contact and quote iframes already in page HTML (GoHighLevel, Wufoo)
Users and authors 401. Gerotec forms are visual stubs, not live backends.
Do not scrape Elementor or Beaver Builder markup into the frontend. Recreate header, mega-menu, and homepage sections as components. Pull catalog lists, titles, images, and body HTML from REST. The page builder is what you are replacing. The JSON is what you keep.
Visitor path on the demos (this is what we shipped, not a full production CMS):
Source WordPress sites keep publishing as they already do. We never logged into admin.
Building Products Plus: Astro fetches REST at build time and emits static HTML on Vercel.
Gerotec: Next.js fetches REST with a 60 second ISR revalidate, so catalog JSON can refresh without a rebuild. That is cache revalidation, not a WordPress publish webhook.
Visitors do not hit PHP. They hit the demo hosts. We did not put a WordPress webhook on either demo.
Gerotec is a German Rohr- und Kanaltechnik catalog. The live WordPress stack is Elementor Pro, ACF Pro, and FacetWP on STRATO. That is a heavy frontend for a marketing catalog. The demo recreates the UI in Next.js App Router (TypeScript, Tailwind) and reads https://dev-projekt-3.de/wp-json.
Custom types from REST: produkt, fahrzeug, messe, schulung, gtc_download. Taxonomies: produkt_kategorie, fahrzeug_kategorie, hersteller. Fetches use ISR with a 60 second revalidate, so catalog lists can refresh without a full rebuild.
WordPress does not expose menus cleanly, so the mega-menu lives in code. Homepage sections are mostly static recreations. News, fairs, and the featured product come from REST. German copy and live URL slugs stay. WordPress rasters go through Next.js Image Optimization (AVIF/WebP).
Honest limits: search, inquiry cart, filters, and form backends are visual stubs. The repo has no ACF, Payload, or Sanity schemas. Some vehicle records ship featured_media: 0, so listing images fall back to known upload URLs.
The demo keeps the CPTs and the German slugs and drops the Elementor request path. Production gerotec.de is unchanged. We did not cut over their live site.
Building Products Plus is a Gulf Coast marine and structural timber supplier. WordPress on WP Engine stays the CMS. The demo is a static Astro frontend that fetches public REST at build time and ships HTML to a CDN. Same URLs and copy. Much less JavaScript on the request path.
Proof of access, verified against the live API: 69 pages, 45 posts, media already public. GraphQL is not installed. ACF REST is not installed. Menus API returns 401. Rank Math is not in the JSON. That is still enough to generate 115 static HTML files.
Catch-all routes come from each page’s link field. Posts keep their WordPress root permalinks. Pages win slug collisions. The posts archive is /bpp-news/. Contact and quote forms keep the existing GoHighLevel and Wufoo iframes after HTML sanitize. I did not invent a new form backend.
Speed is the product. Lighthouse 12 lab on the homepage (8 Sep 2026):
Performance
Script requests
Astro desktop
98
9 (~260 KB)
Live WP desktop
74
32 (~565 KB)
Astro mobile
64
9 (~240 KB)
Live WP mobile
64
32 (~569 KB)
An earlier PageSpeed mobile run sat at 69 with a 15 second LCP because of an 8.8 MB image. After the image pass, a local Slow 4G preview measured LCP at 1.5 seconds. Homepage plates are build-optimized. Most inner WP bodies still hotlink original uploads. Search is a stub. WonderPlugin galleries render as stacked images, not carousels.
What these demos do not include
I have not shipped a production WordPress-to-Vercel publish webhook, authenticated menus, ACF field maps, or Rank Math in REST. Gerotec ISR is the closest thing we have to fresh content: Next revalidates fetches every 60 seconds from the public API. Building Products Plus stays frozen until the next npm run build.
If a client later wanted production headless WordPress, the follow-up would be: a deploy hook on publish, exposing the fields admin can see, and moving more images off the WordPress origin. Gerotec already runs WordPress rasters through Next.js Image Optimization. Building Products Plus only build-optimizes homepage plates; inner page bodies still hotlink original uploads. That is the honest split.
Pros and cons of headless WordPress
Pros
Editors of the source sites keep the WP admin they already use. These demos did not require a new CMS login.
Content is already in REST. You do not migrate 69 pages to prove the idea.
Visitors skip PHP and the page-builder script tax (Elementor on Gerotec, Beaver Builder and Slider Revolution on Building Products Plus).
You can show a faster public URL without a content migration.
Coding agents consume JSON easier than a live theme. Typed fetchers beat guessing admin screens.
The pitch can stay “WordPress is the CMS, Astro or Next is the site.” We have not run that as a client cutover. These are demos.
Cons
You now operate two systems: WordPress and the frontend.
Menus, SEO plugin head, and ACF fields are often missing from public REST.
content.rendered is flattened HTML. Elementor and Beaver layout is gone. You rebuild chrome as components.
Widgets such as Slider Revolution, FacetWP, and WonderPlugin do not come along. They become stubs or custom islands.
Publish is a rebuild unless you add a webhook. We did not add one. Gerotec ISR only refreshes the REST cache. Static Astro waits for the next build.
Images may still hit the WordPress origin. Drafts and preview need auth we did not have.
If WordPress is down, Gerotec ISR and the next Astro build fail. You still patch WordPress for security.
AI development with a headless CMS
Coding agents are much faster when the content contract is an API, not a PHP theme. That is as much a WordPress-future issue as a tooling issue. The models will keep getting better at UI. They will not magically understand an Elementor document tree.
On Building Products Plus I documented the live REST surface first (wordpress-api.md), then paginated pages and posts in wp.ts. Gerotec maps custom types in TypeScript and fetches with _embed. Agents helped scaffold Astro getStaticPaths on Building Products Plus, sanitize HTML, rewrite internal links, and hardcode nav where menus 401. Layout work is tokens, cards, and sections. Plugin CSS is what you delete. We did not build on-site AI chat, search, or personalization on these WordPress demos.
The weak contract is untyped HTML blobs, locked menus, and missing ACF. That is why a demo can look right and still be a poor editor experience. The strong contract is a schema the frontend and the agent both read. That is why I used Sanity on myJobManager instead of cloning WordPress there.
When I pick Sanity: myJobManager
The future of your WordPress is not the same as the future of every CMS. Sometimes the right move is to leave.
myJobManager is production marketing for UK construction and trades software, not a REST clone of WordPress. Editors work in Sanity Studio with visual editing. Astro turns those pages into static HTML on Vercel, with Cloudflare in front, so the SEO team can publish without a developer ticket. That is the shipped marketing site, not a headless WordPress demo.
Why not headless WordPress here? They needed a designed block schema, typed content, and no Elementor tax. Portable Text beats content.rendered. Studio schemas, GROQ queries, and generated TypeScript are in the repo, so agents and CI share the same contract.
Headless WordPress is the right path when the CMS must stay WordPress. Sanity is the right build when the team will live in a new Studio and you want schema as the source of truth.
How to choose
If you need…
Prefer
Proof that the existing WP site can feel fast, without moving editors
Headless WordPress (Astro or Next on public REST)
SEO team editing designed blocks, drafts, and visual overlays
Sanity (or another schema CMS) plus a static frontend
No second system, and the current theme is already fast enough
Stay on classic WordPress and cut plugins
The future of WordPress is not “everyone installs another builder.” It is WordPress remaining the editor of record for a huge share of the web, while the public site can be HTML you cache and score. The Gerotec and Building Products Plus URLs are there so you can click the original and the demo side by side. They are demos on my domains, not live cutovers.
If you want the same treatment for a WordPress catalog, or a Sanity marketing site like myJobManager, start on the contact page.
Meta released Muse Spark 1.3 on September 2, 2026, four weeks after Muse Spark 1.2 and Muse Code. I am writing this a few days later with the same builder lens I used for that post, Grok 4.6, and GPT-6 Astra vs Gemini 3.8 Flash: not who won the internet, but what I would put on a long agent loop for product and agency work.
The important framing is that this is a model increment on an existing harness, not another Muse Code launch. 1.3 drops into Muse Code and the Meta Model API the same day. Max reasoning, the setting behind many of Meta’s strongest rows, stayed behind a safety gate at launch and is now live in both products.
What shipped
Muse Spark 1.3 is a coding and agent update to 1.2. Context stays at 1M tokens. Inputs are text, image, and video. Access is Muse Code, the Meta Model API, and OpenRouter as meta/muse-spark-1.3 (Standard) and meta/muse-spark-1.3-contributor. Treat it as a hosted proprietary model: no downloadable weights in this release. Meta’s roadmap still lists bigger models and a Muse Spark open-weights drop, with no date.
Muse Code is still a terminal agent for macOS and Linux, installed with curl -fsSL https://dev.meta.ai/install.sh | bash. There is still no dedicated desktop app and no Cursor picker row. On Windows, that remains the same workflow gap I wrote about for 1.2: my daily shipping loop lives in Cursor on this machine, not a Linux terminal agent.
Meta says 1.3 was trained on more long-horizon coding tasks and is easier to use in common engineering workflows: fewer turns where they are not needed, less verbose, cleaner coding style. Internal comparisons by Meta engineers claim about 20% fewer tool calls and 25% fewer tokens than 1.2 on those jobs.
What actually changed
The UX story this time is the model, not a new shell.
Collaboration instead of silent drift. Meta trained 1.3 to ask clarifying questions when the prompt is ambiguous, ask for help when it is stuck, and confirm before consequential actions. On long jobs it can either send frequent updates or work quietly, depending on how you steer it.
Messy single-thread work. It is supposed to map a new prompt to the right task inside one long conversation, even when you interrupt or steer an older request. That is the failure mode that burns real agent days: two jobs get fused, a constraint gets dropped, and the model keeps going.
Max vs xhigh. xhigh shipped on day one. Max waited about two days for extra safety testing and is now available in Muse Code and the Meta Model API. Artificial Analysis’s September 2 note still treated max as a limited partner preview. That lag matters when you read launch tables: several of Meta’s headline rows are max, not the mode you could call on September 2.
Safety posture. Meta claims stronger resistance to adversarial inputs and prompt injections, plus better calibration on irreversible actions. That does not replace your own approval gates. It is the baseline I want from a tool-using coding model.
Compared with 1.2: same Muse Code runtime (persistent background agents, append-only event log, isolated worktrees, /plan/grill/goal). The increment is judgment, token waste, long-context retrieval, and a higher reasoning ceiling.
That week was stacked
Early September compressed three closed US moves into three days. I already wrote the Astra vs 3.8 Flash comparison in GPT-6 Astra vs Gemini 3.8 Flash. Muse is the third name on that calendar, not a side note.
September 1: Anthropic shipped Claude Fable 5.1. Artificial Analysis put Fable 5.1 at 66 on Intelligence Index v4.1.1 at max effort with fallback.
September 2: Google shipped Gemini 3.8 Flash. Meta shipped Muse Spark 1.3 into Muse Code and the Meta Model API, with max still gated.
September 3: OpenAI shipped GPT-6 Astra.
Around September 4: Meta opened max reasoning on Muse Spark 1.3 for Muse Code and the API.
Western labs are still competing on closed cadence. This week the cheap workhorse was Google’s Flash, the gated flagship was Astra, and Meta’s move was a four-week coding-agent bump at the same $1.25 / $4.25 sticker as 1.2.
How the benchmarks look
Meta-reported system scores
Meta’s methodology and the Muse Spark 1.3 model page compare 1.3 (max) with 1.2 (xhigh), GPT-5.6 Sol (max), and Claude Opus 5 (max). These are not one shared harness. Coding rows mix native agent products and mini-swe-agent. Knowledge-work rows use each benchmark’s own setup.
Benchmark
Spark 1.3 (max)
Spark 1.2 (xhigh)
GPT-5.6 Sol (max)
Opus 5 (max)
Terminal-Bench 2.1
88.8%
82.9%
88.8%
86.7%
DeepSWE v1.1
75.4%
55.0%
73.0%
74.0%
SWEAtlas CodeBase QnA
59.4%
46.2%
53.5%
52.7%
MRCR 256K-512K
98.5%
66.3%
91.5%
n/a
MRCR 512K-1M
98.1%
55.5%
73.8%
n/a
GDPVal-AA v2
1754
1615
1710
1824
JobBench
64.9
61.6
45.4
65.7
OSWorld 2.0 (partial)
66.9
47.6
62.7
68.3
On Meta’s own table, 1.3 leads coding and long context. It ties Sol on Terminal-Bench, leads DeepSWE and SWEAtlas, and jumps MRCR from the mid-50s/60s into the high 90s. Opus 5 still leads knowledge work and computer use: GDPVal, JobBench, OSWorld, and AutomationBench. Sol takes DeepSearchQA (93.0 vs 89.4) and Meta’s internal Agentic IF Index (60.5 vs 57.8).
That is a different story from 1.2, where Muse sat just behind Opus on terminal and repo loops. The 1.3 coding rows flipped. The knowledge-work rows did not.
Independent Artificial Analysis
Artificial Analysis scored Muse Spark 1.3 (xhigh) at 61 on the Intelligence Index, up 4 points from the 1.2 listing in that same note (57, August) and 8 points from 1.1 (53, July). That ties GPT-5.6 Sol (max), Grok 4.6 (high), and Claude Opus 5 (high). It sits behind Claude Fable 5.1 (max, 66), Claude Opus 5 (max, 63), and Claude Fable 5 (max, 62).
Muse Spark 1.3 (max) lands at 62, helped by Tau3-Bench Banking (52%, #1 among models AA has scored vs 47% for xhigh) and GDPVal-AA v2 (1754 Elo vs 1709 for xhigh). AA measured max using 62% more reasoning tokens on GDPVal and 28% more on Tau3-Bench Banking than xhigh. The extra point is not free.
The gain is concentrated in agentic work. Versus 1.2, xhigh moves Tau3-Bench Banking from 35% to 47%, Terminal-Bench v2.1 from about 80% to 85%, and GDPVal-AA v2 from 1615 to 1709 Elo. Scientific rows also rose: CritPt 18% to 26%, GPQA Diamond 90% to 94%. Two regressions: AA-LCR dropped 4 points (83% to 79%), and AA-Omniscience accuracy fell because 1.3 abstains more often, which also cut hallucinations.
Cost per Intelligence Index task is $0.55 at Meta’s unchanged $1.25 / $4.25 pricing. That is the lowest AA lists for any model at 59 or above. Direct peers at 61 cost more: Grok 4.6 high about $0.94, Sol max about $0.95, Opus 5 high about $1.23. Gemini 3.8 Flash high sits nearby at 59 and $0.58. 1.3 is still more expensive per task than 1.2’s $0.40, because agentic evals used about 57% more input tokens. AA had no public max pricing at launch.
Reading both tables as a builder: Muse is now a frontier closed coding wedge at a mid-range sticker, not a near-miss. Flash and K3 still own different open-price niches. Grok 4.6 still owns the Cursor-native seat. Fable 5.1 still owns the composite index.
Coding tasks vs frontier peers
On coding specifically, the practical split looks like this:
Terminal and repo loops: Meta’s table has 1.3 max tied with Sol at 88.8% on Terminal-Bench 2.1 and ahead of Opus 5 at 86.7%. DeepSWE is 75.4 vs 74.0 Opus and 73.0 Sol. AA’s Terminal-Bench run is more conservative (85-86%). Either way, Muse is no longer the row sitting just behind Claude Code.
Long context: MRCR is the clearest jump. 1.2 was 66.3 / 55.5. 1.3 max is 98.5 / 98.1. Sol is 91.5 / 73.8. Opus did not post a number on Meta’s table. Whole-repo and whole-contract threads are the jobs I would try first on the API.
Tool orchestration and knowledge work: MCP Atlas was the 1.2 commercial signal. The 1.3 methodology set does not lead with that board. GDPVal, JobBench, OSWorld, and AutomationBench still belong to Opus 5. Do not read a coding win as a general-agent win.
Vs Cursor-native closed models:Grok 4.6 is already in the IDE where I ship. Muse 1.3 may be stronger on Meta’s terminal chart. It is weaker on the one metric that decides my Tuesday: is it in the picker.
Vs open cheap tiers:DeepSeek V4 Flash still owns absurd per-token economics. Kimi K3 still owns the open 3T-class swarm story. Muse is competing with Claude Code and Codex on closed agent systems and mid-range API pricing.
Pricing and data terms
Meta did not cut the sticker. Both published tiers keep the 1M context window. Max does not have a separate public rate card.
Tier
Input / 1M
Cached input / 1M
Output / 1M
Data use
Standard
$1.25
$0.15
$4.25
Meta says not used to improve products
Contributor
$0.10
$0.002
$0.20
Prompts and completions used to train / improve Meta products
OpenRouter lists the same Standard card on meta/muse-spark-1.3. Contributor is meta/muse-spark-1.3-contributor. Rough worked example, unchanged from 1.2: 1M uncached input plus 100K output is about $0.12 on Contributor and about $1.68 on Standard. Cache hits collapse that further. AA’s $0.55 per Index task is the more useful agent number: 1.3 thinks longer than 1.2, so the same sticker can still raise the bill.
Cursor wishlist
Honest status: Muse Spark 1.3 is not in the Cursor model picker today. OpenRouter lists it, so a custom provider is a possible experiment. My daily IDE stack still stays on Composer, Grok, and the other models Cursor already exposes. That is where I already ship this site (Building this blog with Cursor and Grok 4.5, Grok 4.6).
What I want next is the same as 1.2: Cursor adds Muse Spark (directly or via OpenRouter) so I can run the same multi-file IDE loop I use for client work, without leaving the editor for a macOS/Linux terminal agent. Until that lands, the test path is Meta Model API or OpenRouter for API experiments, and Muse Code CLI only if I am on a supported OS.
If Cursor ships it, the first thing I will do is the same delivery test I use for every release: one real multi-file task with tools, constraints, and a deadline.
When I would use it
When I would reach for Muse Spark 1.3 / Muse Code
Long-horizon coding and whole-repo loops where Meta’s DeepSWE, SWEAtlas, and MRCR rows actually match the job
API cost experiments at frontier-ish quality, especially Standard at $1.25 / $4.25 versus Sol or Grok on a long agent bill
Non-sensitive prototypes on Contributor, with eyes open on training terms
Max reasoning now that it is actually callable, for the jobs where xhigh leaves quality on the table
When I would still pick something else
Top verified composite boards and stacks already standardized on Claude Fable 5.1 / Claude Code
Windows-first IDE workflow until Muse Code or a Cursor picker row lands
Confidential client repos that cannot accept Contributor training terms
Open-weight or extreme price-floor needs where Flash or K3 fit better
Computer-use and general knowledge-work agents where Opus 5 still leads Meta’s own table
Takeaway
Muse Spark 1.3 is the first Muse release I would shortlist against Claude Code and Codex on coding and long context, not only on price. Independent AA puts xhigh at 61 and max at 62, cheapest in that intelligence cluster at $0.55 per Index task, with max now live after a two-day safety gate. It is still not a verified takeover of Fable 5.1, still closed weights, and still missing from Cursor. Watch independent Terminal-Bench verification, keep Contributor data terms in mind, and hope the picker catches up so the delivery test can happen inside the editor.
Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
OpenAI released GPT-6 Astra on September 3, 2026, to a limited set of organizations, with Plus, Pro, Business, Enterprise, API, and AWS access rolling out over the following days. Google had already shipped Gemini 3.8 Flash on September 2, generally available, at the same introductory Flash sticker as 3.7 Flash.
I am writing this the next day with the same builder lens I used for GLM-5.3, DeepSeek V4 Pro 0813, and Grok 4.6: not who won the internet, and not whether this is “AGI,” but what I would put on a long agent loop and how the bill looks when that loop runs all week.
These are two different products that happened to land 24 hours apart. Astra is a $10 / $50 computer-use flagship that succeeds GPT-5.6 Sol, with a gated cyber jump behind OpenAI Daybreak. 3.8 Flash is Google’s third Flash release in six weeks, multimodal, GA today, and priced like a workhorse while approaching frontier on long-horizon coding. Claude Fable 5.1 shipped on September 1 and remains the closed-coding reference, not the third subject of this post.
What shipped
Spec
GPT-6 Astra
Gemini 3.8 Flash
Lab
OpenAI
Google DeepMind
Release
September 3 (limited), wider rollout following days
September 2, GA
Model id
gpt-6-astra
gemini-3.8-flash
Context
1,050,000 tokens
1,048,576 tokens
Max output
128,000
65,536
Inputs
Text, computer use, tools
Text, image, audio, video
Output
Text
Text
Reasoning
Effort levels; none unsupported
LOW / MEDIUM / HIGH (default MEDIUM; MINIMAL gone)
Knowledge cutoff
April 30, 2026
March 2026 (AA listing)
Access today
Trusted Access / staged ChatGPT and API
Gemini API, AI Studio, Antigravity, Gemini Enterprise, Gemini app
API sticker
$10 in / $50 out per 1M
$0.75 / $3.75 intro through December 31
Astra is available as gpt-6-astra in the OpenAI API and on Amazon Bedrock. Enterprise admins enable it per workspace; it is off by default at launch. Pro, Business, and Enterprise plans also get GPT-6 Astra Pro. Fast mode is up to 2x Standard speed at 2x Standard price. Fast mode is unavailable with EU data residency.
3.8 Flash is the Gemini 3 family workhorse between Pro and Flash-Lite. Google Cloud docs list the same 1M window as 3.7 Flash, the same output cap, and the same thinking-level enum. Thinking tokens bill as output. Google says the model works harder on complex jobs: extra reasoning steps, iterative tool calls, and more tokens at higher effort. If compute efficiency is the constraint, Google still points you at 3.7 Flash.
That week was stacked
Early September compressed three closed US moves into three days.
September 1: Anthropic shipped Claude Fable 5.1 and Mythos 5.1. Same $10 / $50 input / output as Fable 5, with cache reads cut 75% to $0.25 per 1M. Artificial Analysis put Fable 5.1 at 66 on Intelligence Index v4.1.1 at max effort with fallback.
September 2: Google shipped Gemini 3.8 Flash and a separate defender-only sibling, Gemini 3.8 Flash Cyber, behind the new Fairwind Program. Meta’s Muse Spark 1.3 also landed in the AA feed that day.
September 3: OpenAI shipped GPT-6 Astra. NBC News reported it as the first OpenAI model to trigger advanced internal safety protections under the Preparedness Framework because of its cyber capabilities. Advanced exploit workflows stay gated. Daybreak is the defender path, not a second public model id.
Google’s own framing for 3.8 Flash is cadence: 3.7 Flash was August 13, covered in the GLM-5.3 post, and 3.8 is the third Flash in six weeks at the same intro price. Western labs are still competing on closed cadence and intro discounts. This week the discount sat on Google’s workhorse, while OpenAI moved the flagship sticker up.
Astra ties Sol at 61 and sits five points behind Fable 5.1. AA’s other headline for Astra is the Coding Agent Index: 67 in Codex, about even with Fable 5 and Opus 5, behind Fable 5.1 at 70, at less than half Fable 5’s cost per coding-agent task because Astra used about one third of Sol’s tokens in that harness.
Flash gained three Intelligence Index points over 3.7 Flash. AA says that jump is mostly agentic: Terminal-Bench v2.1, GDPval-AA v2, and a 12-point gain on τ³-Banking to 45%. It lands on the Intelligence vs cost Pareto frontier at $0.58 per Index task. That is still about 13x cheaper than Astra’s $7.7, and about 40% more per task than 3.7 Flash because 3.8 Flash burns ~30% more output tokens (~48k per Index task) and takes more agentic turns.
Astra’s Intelligence Index story is the inverse. AA measured ~10% fewer output tokens than Sol at max, then a 2.5x sticker increase that makes Astra about 75% more expensive per Index task than Sol. Token efficiency went up. The bill still went up. AA also flags mixed knowledge-work progress: hallucination rate on AA-Omniscience dropped from 92% to 51% at max, AA-Briefcase Elo jumped ~80 points, and GDPval-AA v2 dropped ~80 Elo.
OpenAI’s own launch table lists AA Intelligence Index v4.1.1 as Astra 61.2, Sol 60.9, Fable 5.1 65.7, and Gemini 3.8 Flash 58.7. Close to AA’s rounded page. I am using AA’s published 61 / 59 / 66 as the independent scorecard.
Computer use and professional work
This is Astra’s actual product. OpenAI’s launch table, max effort, vendor harness:
Benchmark
GPT-6 Astra
Closest closed
Gemini 3.8 Flash
OSWorld 2.0 (offline set)
72.6% (~40 min/task)
Sol 65.7% (~75 min); Opus 5 70.2%
n/a in that table
Agents’ Last Exam
59.3%
Opus 5 55.5%; Sol 53.6%
n/a
ScreenSpot-Pro (no tools)
92.7%
Fable 5 87.3%; Sol 76.9%
n/a
AutomationBench
41.4%
Fable 5.1 31.4%; Sol 18.1%
n/a
BenchCAD (with tools)
95.9%
Fable 5.1 84.3%; Sol 83.3%
n/a
BrowseComp
91.5%
Sol 90.4%; Opus 5 90.8%
n/a
OSWorld is the row I would actually act on. Higher accuracy and about 47% less wall time than Sol is the difference between an agent you babysit and one you can hand a desktop job. OpenAI also says the updated Codex harness plus Astra is 1.9x faster than the current Sol experience on Mind2Web.
3.8 Flash is not absent from professional work. Google’s launch post puts it ahead of 3.7 Flash and “other frontier models” on Vals Finance Agent V2 and Harvey’s Legal Agent Benchmark, and at 54.9% on HLE-Verified. Those are vendor claims without a side-by-side number against Astra in the same table. Treat them as Google’s knowledge-work pitch, not as a bake-off.
Coding
Two Terminal-Bench versions are in circulation this week. Do not mix them.
Google Cloud’s developer guide (vendor, 3.8 vs 3.7):
Benchmark
3.8 Flash
3.7 Flash
Terminal-Bench 2.1
90.8%
81.6%
SWE-Bench Pro
61.6%
60.4%
SWE-Atlas
51.9%
48.0%
τ³-bench Banking
38.1%
30.9%
Humanity’s Last Exam
45.4%
45.7%
OpenAI’s launch table (vendor, including Flash):
Benchmark
GPT-6 Astra
GPT-5.6 Sol
Fable 5.1
Gemini 3.8 Flash
Terminal-Bench 4.0
57.9%
37.3%
55.8%
19.1%
DeepSWE v1.1
74.1%
72.7%
67.4%
73.8%
FrontierCode 1.1 Main
53.3%
47.5%
50.9%
43.6%
GPQA Diamond
96.0%
94.6%
93.7%
95.3%
HLE with tools
57.2%
n/a
65.0%
n/a
FrontierMath Tier 4 v2
97.6%
83.0%
87.8%
n/a
ARC-AGI-3
99.9%
7.8%
n/a
n/a
Reading that as a builder: DeepSWE is a near tie. Astra 74.1, Flash 73.8, Opus 5 73.7. Long-horizon repo work is not why you pay Astra’s sticker. Computer use, Terminal-Bench 4.0, AutomationBench, and template-faithful professional artifacts are.
Flash’s 90.8% on Terminal-Bench 2.1 versus Astra’s 57.9% on Terminal-Bench 4.0 is a version split, not a knockout. OpenAI’s own Flash row on TB 4.0 is 19.1%. That is model plus harness plus board, not “Flash cannot use a terminal.”
HLE with tools still belongs to Fable 5.1 at 65.0%. Astra at 57.2% is not a clean reasoning sweep. Google Cloud’s HLE row for Flash is 45.4%, basically flat versus 3.7 Flash at 45.7%. Coding and tool use moved. The old knowledge exam did not.
ARC-AGI-3 at 99.9% is the number to read last. OpenAI’s footnote says it used the Responses API harness. Independent recaps of ARC Prize runs put stateless API calls much lower, in a roughly 17% to 63% band depending on reasoning tier, with the marketed figure needing a stateful adapter and an expensive comprehensive run. If you call gpt-6-astra statelessly, do not budget for 99%.
The price war, same week
Official API rates to budget against (input / output per 1M tokens). Verify before you quote a client.
Model
Input
Output
Notes
Gemini 3.8 Flash intro
$0.75
$3.75
Through December 31, 2026. Then $1.50 / $7.50
Gemini 3.8 Flash cache read
$0.075
n/a
90% off input at intro
GPT-6 Astra
$10
$50
Cache reads 90% off ($1). Cache writes 25% premium ($12.50)
GPT-6 Astra Fast
~$20
~$100
2x Standard price, up to 2x speed
GPT-5.6 Sol (promo)
$4
$20
AA and launch-week recaps. August posts still had $5 / $30
Astra matches Fable 5.1’s uncached sticker and sits 2.5x above Sol’s current promo. Flash kept 3.7 Flash’s intro rate. The per-token gap is not the whole bill. 3.8 Flash uses more tokens than 3.7 Flash. Astra uses fewer tokens than Sol and still costs more per AA Intelligence Index task.
Worked examples at current intro / Standard rates (approximate, thinking tokens ignored unless noted):
100K-token document (100K input, 4K output, uncached): Astra about $1.20. Flash about $0.09.
Repository session (200K input with 70% cache hit, 20K output): Astra about $1.74. Flash about $0.13.
Multi-step agent trace (1M cumulative input with 80% cache hit, 100K output): Astra about $7.80. Flash about $0.59.
That last row is the same order of magnitude as AA’s $7.7 vs $0.58 per Intelligence Index task. On January 1, 2027, Flash doubles and that $0.59 trace becomes about $1.18. Still cheap next to Astra. Not the 2026 intro sticker you can quote into next year.
Cyber, at a high level
Both labs shipped a public workhorse and a gated cyber path in the same window. I am staying at published boards and access gates.
Astra meets the Critical threshold in cybersecurity under OpenAI’s Preparedness Framework. OpenAI’s launch table, without production safeguards on the exploit boards: ExploitBench 100% vs Sol 78.5%; ExploitGym 42.4% vs Sol 30.3%; a contamination-controlled ExploitBench (June-August 2026) at 39.0% vs Sol 11.5%; SRE-Bench 88.0% pass@1 vs Sol 55.9%. The production model launching this week does secure code review and patching, and refuses proof-of-concept exploit creation. OpenAI says Daybreak will expand defender workflows (validation, malware analysis, detection engineering) with less restrictive safeguards in the coming weeks. Extra safety checks can pause a ChatGPT or Codex task for review, or stop an API task outright.
NBC’s recap is the public-risk framing I care about for client work: Astra can find previously unknown flaws and develop exploits across well-protected systems without a person guiding each step, which is exactly why the gates exist. OpenAI also reports fewer misaligned computer-use outcomes than Sol (3.4% vs 18.8% in one internal realistic-work eval without confirmation policy) and a CoT monitorability regression it names as a research priority.
Gemini 3.8 Flash Cyber is a separate model, not a mode on gemini-3.8-flash. It is Fairwind-only: trusted government authorities, critical infrastructure operators, and software maintainers. Google says it prioritized patching over exploitation. Vendor claims: frontier-level CyberGym discovery; an internal 20-language discovery set above 70%; CWE-Bench pass@1 47.2% vs a leading frontier model at 47.8% at much lower cost; Chrome Security produced 2.6x more correct patches than the best larger commercial models they compared. Public 3.8 Flash keeps tighter CBRN and cyber-offense safeguards. Flash Cyber does not.
When I would use it
When I would reach for GPT-6 Astra
Computer-use and browser jobs where OSWorld-style desktop completion and wall time matter more than the token sticker
Codex long sessions that need notes across context windows once that experimental path is on by default
Template-faithful slides, docs, and spreadsheets, or Sites-in-ChatGPT artifacts that have to match an existing brand file
US-closed stacks already on OpenAI, where Sol is the model being replaced and Fable 5.1 is the alternative flagship
Alignment-sensitive computer-use loops where Sol’s higher misaligned-outcome rate was the actual risk
When I would reach for Gemini 3.8 Flash
High-volume agent loops that need Flash economics and a 1M window, especially if 3.7 Flash was already close enough
Multimodal input (image, audio, video) that Astra’s text-and-computer-use pitch does not cover
GA-today routes in Gemini API, AI Studio, Antigravity, or Gemini Enterprise, while Astra is still rolling out
Long-horizon coding where DeepSWE is the board you care about and a 0.3-point gap to Astra is not worth 13x the Index bill
Thinking-level control (LOW / MEDIUM / HIGH) when you want to trade tokens for latency on the same model id
When I would still pick Fable 5.1, Grok 4.6, or DeepSeek
Absolute top composite Intelligence Index and HLE-with-tools (Fable 5.1)
Cursor-native long agent loops where Grok 4.6 is already in the picker at $2 / $6
Cache-heavy Claude agents where Fable 5.1’s $0.25 cache reads change the math more than Astra’s $1 cache reads
Takeaway
GPT-6 Astra is the expensive computer-use flagship: better OSWorld and professional-work rows than Sol, near-tied with Flash on DeepSWE, behind Fable 5.1 on the Intelligence Index and HLE with tools, and gated on the cyber jump that triggered OpenAI’s Critical threshold. Gemini 3.8 Flash is the workhorse that kept the 3.7 intro sticker, climbed to 59 on AA, and sits on the cost Pareto frontier while burning more tokens than 3.7 Flash by design.
I would shortlist Flash as the default volume agent this week. I would shortlist Astra when the job is actually driving a computer, shipping a finished artifact, or replacing Sol in an OpenAI-standardized stack. Leaderboards help you shortlist. Delivery decides. Try the same test I use for every release: one real multi-file task with tools, constraints, and a deadline.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
The week of August 24 to 30 was not a flagship drop. Last week’s wrap-up already covered Stripe buying OpenRouter, Cursor Origin, cheaper Sol, and V4-Flash vision. What stacked up this time was vendor access and cheap open weights. OpenAI set a November cutoff for models inside Cursor. Z.ai put a name on Ox Alpha. Nvidia’s Hugging Face talks stayed in the papers, not on either company’s site. Anthropic restated Claude Code weekly limits after users did the arithmetic. Tencent shipped a 770B preview.
I am writing this on Wednesday, September 2, 2026, with the same builder lens I used for GLM-5.3, Grok 4.6, DeepSeek V4 Flash, and Kimi K3: not who won the internet, but what I would put on a long agent loop and how the bill looks when that loop runs all week.
The models you already shortlisted did not get replaced. Who is allowed to serve them inside the IDE, and at what weekly meter, did.
The week at a glance
Date
What
Why a builder cares
Aug 26
GLM-5.3-Flash (Ox Alpha)
MIT weights day one, native multimodal, Flash-class bill, 3x Coding Plan quota vs GLM-5.3
Aug 26-27
Nvidia / Hugging Face talks
Reported $12.9 billion. Neither company confirmed. Procurement watch, not a migrate
Aug 26
Claude in Chrome GA
Autonomous browser actions on paid Claude plans
Aug 28
Pentagon Anthropic blacklist overturned
Governance for US enterprise buyers, not a coding-stack change
Aug 28
OpenAI winds down Cursor
Proposed shutoff November 12. Frozen at today’s OpenAI lineup until then, no future models
Permanent +25% vs old baseline equals −17% vs today’s 50% boost, from September 14
I am not treating a Sonnet 5 jump to $3 / $15 as this week’s news. Anthropic’s own pricing docs kept $2 / $10 as the standard rate. If a roundup told you the intro price expired on August 31, rebase against the docs, not the roundup.
OpenAI winds down models in Cursor
On August 28, OpenAI published Our decision on Cursor following its acquisition by SpaceX. It notified SpaceX that it intends to wind down the contract that provides OpenAI models to Cursor, with a proposed shutoff of November 12, 2026. The post says that is the maximum notice the contract allows.
SpaceX closed the Anysphere deal on August 14. OpenAI says the custom agreement gives it a limited window to cancel after a change of control. The stated reason is not a Cursor product complaint. OpenAI writes that it cannot be confident SpaceX will use the technology within its terms, citing Twitter after Musk’s takeover and Musk’s admission under oath that xAI violated OpenAI’s terms. It will hold cancellation to the latest date while not providing future models to Cursor. The upcoming model named in that sentence is Astra.
That is a freeze, then a cutoff. Today’s OpenAI ids in the Cursor picker keep running until November 12 unless the talks reverse it. New OpenAI models do not.
Cursor co-founder Michael Truell posted the next day that OpenAI models serve about 5% of Cursor user traffic, and that Cursor is speaking with OpenAI to resolve it. Recap with the quote: Digital Trends. Anthropic’s Tom Brown replied that Cursor has been a trusted partner since Sonnet 3.5, and that Anthropic will continue to increase compute for Claude models in Cursor. Musk’s reply, in the same coverage: he “couldn’t care less.”
What I would actually do this week: do not panic-migrate. Inventory which agent loops still require an OpenAI id inside Cursor. Keep shipping on Composer and Grok 4.6. Trial the same multi-file task on Claude and Grok before November. If the work has to stay on Sol, move that loop to the API or Codex, not to a hope that the picker stays green.
Piece
Fact
Not a fact
Notice
OpenAI notified SpaceX on August 28
Instant shutoff this week
Date
Proposed November 12, 2026
A signed reversal
Future models
Explicitly withheld, Astra named
Cursor loses every model family today
Cursor’s share
OpenAI about 5% of user traffic
Your Sol-heavy seat is in that 5%
Anthropic
More Claude compute in Cursor
Claude usage limits in Cursor went up this week because of this post
See last week’s Origin note for the repo half. This is the model-access half. Ownership of the IDE now sits on the same checklist as model quality.
GLM-5.3-Flash, formerly Ox Alpha
August 26: Z.ai published GLM-5.3-Flash: Frontier Intelligence, Flash Cost. It is the first natively multimodal model in the GLM-5 series. The August 19 GLM-5.3 bake-off promised flagship weights around August 28. Flash arrived first, with downloadable weights on day one.
Before the name, it ran anonymously as ox-alpha on OpenCode and OpenRouter. Z.ai says that preview traffic ran on Chinese AI chips.
Spec
GLM-5.3-Flash
Total params
320B MoE
Active per token
18B
Context
1M tokens
Modalities
Text, image, video
What changed vs GLM-5.3
New base, hybrid sparse plus linear attention, not a cheaper serving of the 743B flagship
Official API list from Z.ai pricing, per million tokens. Verify before you budget:
Meter
GLM-5.3-Flash
GLM-5.3
Input
$0.15
$1.40
Cached input
$0.03
$0.26
Output
$0.50
$4.40
That is about 9x cheaper than the GLM-5.3 flagship on list. Coding Plan users get 3x the usable quota of GLM-5.3. Z.ai’s own launch post puts Flash at 57 on Artificial Analysis Intelligence Index v4.1.1 at $0.045 per task on a discounted tier. Treat that row as vendor plus AA, not as my harness.
Selected vendor rows from the launch table, max-effort style, harnesses in Z.ai’s footnotes. Do not mix them with a shared bake-off.
Benchmark
GLM-5.3-Flash
Closest published in that table
Terminal Bench 2.1
84.3
Opus 4.8 85.0; Gemini 3.7 Flash 85.8
DeepSWE v1.1
63.4
GLM-5.2 46.2; GPT-5.6 Terra 69.6
AutomationBench v1.0.6
48.8
Opus 4.8 41.0; Gemini 3.7 Flash 52.3
Z.ai Code Bench v1.0 (max)
29.0
Opus 4.8 29.5
OfficeQA Pro
62.4
Opus 4.8 48.9
When I would reach for it: a high-volume agent loop that now has screenshots or UI dumps, and I do not want frontier multimodal rates. When I would not: a US-facing client that already froze Chinese-model procurement. That half of the story is still in Kimi K3, a possible US ban, and why some builders pick Codex.
Nvidia and Hugging Face: reported, not closed
August 26-27: The Information reported that Nvidia agreed to buy Hugging Face for $12.9 billion. Reuters repeated that figure, citing The Information. TechCrunch ran the same number, then added Business Insider’s same-night line: the talks had not produced a signed agreement and could still fall apart. Neither Nvidia nor Hugging Face confirmed.
Hugging Face’s last known mark in that coverage is the 2023 round: $235 million at $4.5 billion, with Nvidia already in the cap table. The Information’s revenue figure in the Reuters piece is about $150 million annualized. I am not turning that multiple into a closed-deal fact.
What a builder does this week: almost nothing, unless you were about to migrate weights off Hugging Face on rumor. GLM-5.3-Flash and Hy4 both landed there. Watch whether the host stays a neutral rack after a chip vendor owns it. Do not rebuild an agent graph around an unconfirmed acquisition.
This is the same caution I used for Stripe’s unconfirmed OpenRouter price in last week’s wrap-up. Reported is not invoiced.
Claude Code limits, and Claude in Chrome
Two Anthropic notes, two different products. Do not collapse them.
Claude Code weekly limits, August 29. Anthropic said it would permanently raise standard weekly limits in Claude Code by 25% from September 14 for Pro, Max, Team, and seat-based Enterprise plans, with the current 50% increase holding until then. Users ran the baseline math within hours. Anthropic deleted the original thread and restated it: compared with today, that is a 17% reduction. Recap: BleepingComputer.
Worked example, not Anthropic’s metering units. If the old weekly allowance was 100, the May-era 50% boost is 150 today. On September 14 it becomes 125. That is 25% above the old baseline and about 17% below the seat you are actually using this week.
Baseline
Weekly units in the example
Pre-promotion
100
Current 50% boost, through September 13
150
Permanent from September 14
125
Same lesson as the Kimi weekly quota and 5-hour wall: budget usable agent hours, not the marketing percentage. A +25% headline against a meter you are not on is how a Thursday sprint dies.
Claude in Chrome, August 26.Anthropic’s GA post: paid Claude plans, Claude can take browser actions autonomously instead of asking for every click, and a safety classifier checks each action against the original request. You can turn auto-approval off. It uses your existing logins. It is Chrome only, not other Chromium browsers, and not mobile. Files on disk still go through the desktop app.
Anthropic’s own prompt-injection eval, not mine. With probes plus the classifier, they report no successful attacks against Sonnet 5, Opus 5, or Mythos 5, and a 0.3% success rate against Fable 5, all successful breaks in low-severity scenarios per their review. Attribute that as their harness. It is not a reason to change the Cursor default this week. It is a reason to treat “Claude can now click the vendor portal” as a paid-plan browser agent, with prompt injection still in the threat model.
Tencent Hy4 preview, and the Pentagon ruling
August 28: Tencent released Hy4 preview. Official spec: 770B total, 49B active, context over 1M tokens. Live in WorkBuddy, CodeBuddy, Yuanbao, and ima, plus API on Tencent Cloud TokenHub and OpenRouter. Two weeks free on WorkBuddy and CodeBuddy at launch. Hy3 free access extended through September 30.
Weights: Hugging Face, Apache 2.0, with an FP8 variant. The model card is unusually direct for a vendor README. Tencent calls this an early version with headroom left in pre-training and post-training, and names two shipping issues: it spends longer than necessary reasoning through complex tasks, and it tends to over-verify its own work. Both cost tokens and latency on an agent loop.
Official API from the same Tencent post, per million tokens:
Meter
Hy4 preview
Input
$0.834
Output
$2.501
Cache hit
$0.042
Tencent’s internal blind test: 163 experts, 203 engineering tasks, Hy4 preview 2.99 / 4.00, GLM-5.3 2.92, Kimi K3 2.94. That is Tencent’s panel, not Artificial Analysis. File it as close three-way, vendor-run.
When I would trial it: an OpenRouter fallback on a productivity or coding loop where Apache weights matter more than a polished reasoning policy. When I would not: a deadline loop that already hates extra verification tokens, or a US client with a Chinese-model freeze.
Pentagon, short. I write an AI-coding blog. I am not pretending this is a Cursor changelog. In late August, California district judge Rita Lin ruled the Pentagon’s “supply chain risk” label on Anthropic unlawful. Coverage: DW. Lin wrote that empty invocation of national security is not a blank check to punish critics. File it as: US enterprise buyers are back to weighing governance stance, not only capability. Then go back to your eval harness.
When I would change a stack this week
I would not throw out the shortlist from GLM-5.3, V4 Pro 0813, V4 Flash, Kimi K3, or Grok 4.6. I would change access risk, one Flash-class multimodal id, and one weekly meter.
If you are here
Change this week
Do not change
Cursor plus Sol or Luna in the picker
Log November 12. Inventory loops that still require an OpenAI id. Trial Claude / Grok / Composer on the same task
Do not panic-migrate this week, or assume Codex and ChatGPT shut off too
High-volume multimodal on a GLM Coding Plan
Trial glm-5.3-flash at $0.15 / $0.50, 3x quota vs GLM-5.3
Do not replace the August 19 GLM-5.3 shortlist without a real task
Hugging Face as a weights host
Log the reported Nvidia talks
Do not migrate weights because of an unsigned deal
Claude Code Pro, Max, Team, or seat-based Enterprise
Rebase weekly capacity to the September 14 meter (125 vs today’s 150 in the 100-unit example)
Do not budget the +25% headline against the old baseline as if it were extra headroom from today
Paid Claude, work that lives in Chrome
Pilot Claude in Chrome with auto-approval off until you trust the classifier
Do not treat Anthropic’s 0% / 0.3% injection eval as your vendor-portal threat model
US client with a Chinese-model freeze
Keep Hy4 and GLM Flash off the default
Do not “just try Ox Alpha” on that account
OpenRouter already in the stack
Hy4 and GLM Flash are extra fallback ids, not a reason to leave Stripe’s pending router
Do not collapse last week’s OpenRouter deal with this week’s Hugging Face rumor
Two rules I am using on retainers this month. Separate surface from model: Cursor-the-IDE, gpt-5.6-sol on the API, Codex, Claude Code, Claude in Chrome, and Hugging Face-the-host are different products. Do not mix them before you change a default. Separate closed price from reported price: Flash’s $0.15 / $0.50 is on Z.ai’s pricing page. Hy4’s $0.834 / $2.501 is on Tencent’s post. Nvidia’s $12.9 billion is not. If a number has no primary, it does not go in the cost model.
Takeaway
The week of August 24 to 30 was a vendor-risk week. OpenAI can take models out of the IDE because the parent company changed. Cheap Flash-class open weights keep landing anyway. Anthropic will sell you a +25% limit that is a −17% cut against the seat you have today. Nvidia may or may not own the rack those weights sit on. None of that is a reason to throw out last week’s shortlist. It is a reason to check who can cut you off, which weekly meter you are actually on, and whether an unconfirmed acquisition changes where you download weights.
If you need a next step that is not another tab of vendor benches: try one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
The week of August 16 to 22 was not a flagship drop. GLM-5.3 already had its bake-off on the 19th. What stacked up was infrastructure and distribution. Stripe agreed to buy the routing layer most agencies already treat as a utility. Cursor shipped a Git host. OpenAI refreshed ChatGPT onto GPT-5.6 August snapshots and cut Sol API prices. DeepSeek hung vision off V4-Flash as an experiment. Etched raised again, to $21 billion. Slack put coding agents in channels. Nevada said yes to thousands of robotaxis, which is deployment news, not a coding-stack change.
I am writing this on Saturday, August 22, 2026, with the same builder lens I used for DeepSeek V4 Pro 0813, V4 Flash, Kimi K3, and Grok 4.6: not who won the internet, but what I would put on a long agent loop and how the bill looks when that loop runs all week.
The models you already shortlisted did not get replaced. The pipes around them did.
The week at a glance
Date
What
Why a builder cares
Aug 17-18
Cursor Origin early beta, covered as GitHub’s week went sideways
Native repos and PRs inside Cursor, with GitHub still the source of truth for anything started there
Aug 18
Etched raises $700 million at $21 billion, led by Jane Street
Cluster-scale frontier inference, not a single-model ASIC you swap this week
Aug 19
Stripe agrees to acquire OpenRouter
The multi-model gateway a lot of production traffic already hits, plus Stripe Token Billing in the same house
Aug 19
GPT-5.6 August into ChatGPT; Sol API pricing
Consumer snapshot refresh, not a new generation. 20% input and 33% output cut on Sol
Aug 20
Slack Code
Tag a coding agent, get a project channel with diffs, plan, and a live HTML preview
Aug 20
Nevada robotaxi permits
Tesla, Waymo, and Uber get Clark County caps. Deployment news, not a coding-stack change
Aug 21
deepseek-v4-flash-vision-exp
Experimental vision on V4-Flash rates. Same-day Harness 0.1.1 and a free Files API
I am not treating Gemini 3.7 Flash as this week’s news. It GA’d on August 13. The intro price is still live, so it sits in a kicker at the end, not in this table.
Stripe buys the routing layer (OpenRouter)
On August 19, Stripe said it had agreed to acquire OpenRouter. Primary writeup: Stripe’s newsroom note. Payments-industry recap: Payments Dive.
OpenRouter is the AI model gateway that routes across 400+ models from 80+ providers. If you have shipped an agent that needed a fallback when one lab rate-limited you, you already know why this company exists. Stripe already sells Token Billing. Putting the router next to the meter is the whole thesis.
Patrick Collison called tokens “the central currency for companies building with AI.” OpenRouter CEO Alex Atallah said the stack stays multi-model, and that Stripe’s neutrality is why they sold. Named customers in the coverage: NVIDIA, Zoom, Lovable.
The close is expected in the coming weeks, subject to customary conditions. OpenRouter’s last disclosed round is the May raise: $113 million at about $1.3 billion. Stripe declined to comment on price for this deal.
What I would actually do this week: do not migrate. No stack rewrite. If you already route through OpenRouter, Atallah’s line is the one that matters. Multi-model stays. Neutrality is the reason they sold. Watch whether Token Billing and the gateway get productized as one invoice, and whether any provider gets a quieter default. Do not rip out a working router because a payments company bought it. Do log that your routing vendor is about to sit inside Stripe. The risk for a long agent loop is not that OpenRouter disappears. It is that “neutral multi-model” slowly becomes “neutral, plus a Stripe-shaped default.” Procurement watch item, not a rewrite.
GPT-5.6 August in ChatGPT, and a cheaper Sol
Same day, August 19, OpenAI pushed GPT-5.6 August snapshots into ChatGPT. Source of record for the in-week event: the GPT-5.6 August update on OpenAI’s deployment-safety hub. This replaced GPT-5.5 Instant. It is a consumer refresh, not a new flagship generation.
Safety, from OpenAI, not from a third-party leaderboard. Same Preparedness as July GPT-5.6: High in Biological/Chemical and Cybersecurity, below High in AI Self-Improvement. First dedicated U18 evals in the system card. On HealthBench Professional (length-adjusted), Sol August scored 54.0 against GPT-5.5 Instant at 38.4. OpenAI says factual-error rates on high-stakes prompts are down about 60% versus 5.5 Instant. That is their own eval, not a production-prevalence number. I will not pretend it is.
Same-week company context, not a model story: on August 17, CNBC reported Greg Brockman calling the recent exits (Denise Dresser, Brad Lightcap, and earlier Fidji Simo) not atypical, with Dali Rajic as the new CRO. July run-rate was up 20% month over month, business customers up 32%. Confidential IPO filing in June. CNBC cites an $852 billion valuation. Useful as “they can cut Sol and still be in a growth posture.” Useless as a reason to pick Luna over Sol on a coding loop.
I am not restating the Kimi K3 / Fable 5 / GPT-5.6 Sol bake-off here. August Sol is cheaper than the Sol I already priced. That is the delta.
DeepSeek V4-Flash-Vision-Exp
August 21: DeepSeek shipped an experimental multimodal checkpoint, deepseek-v4-flash-vision-exp. Source: DeepSeek’s August 21 API note.
Official claim, not my bench: text, agent, and knowledge parity with V4-Flash, and a “major leap” on multimodal-agent benches to “close to Opus-4.8.” I am leaving the leap in quotes. I have not rerun it.
Same day: DeepSeek Harness 0.1.1, and a free Files API with file_id reuse. Mixed text plus image via base64, URL, or the Files API. Surfaces: Chat Completions, Messages, Responses. Images billed at V4-Flash rates, capped at 384 tokens each. Vision is experimental. It is not in the stable catalog.
Item
Fact
Model id
deepseek-v4-flash-vision-exp
Status
Experimental. Not stable catalog
Text / agent / knowledge
Officially parity with V4-Flash
Multimodal-agent claim
“Close to Opus-4.8” on their multimodal-agent benches
Image billing
V4-Flash rates, 384 tokens cap per image
Files
Free Files API, file_id reuse
Harness
0.1.1, same day
When I would reach for it: a V4-Flash agent loop that suddenly has screenshots or UI dumps, and I do not want to jump the bill to a frontier multimodal SKU. When I would not: anything a client will still be running unmodified in October. Experimental means the id can move. Pair it with the V4 Flash price-war note if you need the text-only cost picture. This post is only the vision add-on.
Cursor Origin, and why the Git host matters
Cursor’s changelog listed Origin on August 17. TechCrunch covered it on August 18, on a day GitHub was having a very public week: Cursor changelog, TechCrunch on Origin.
Origin is early beta on all paid plans. Enterprise admins can opt out. Native repos, PRs, code browsing, two-way GitHub sync. Official line, in the product’s own words: “Pushes keep going to GitHub, which stays the source of truth for anything started there.” Repo URLs look like cursor.com/codebase/<name>. Launch integrations: Vercel, Depot, Buildkite. Agent-native features ship soon.
The launch overlapped a long GitHub global degradation. TechCrunch: more than 6 hours, about a 20% error rate. That overlap is why Origin got read as a hosting play, not just a settings page. Cursor is now officially part of SpaceX, per the same TechCrunch piece. I am not repeating secondary deal terms I did not fetch from a primary.
August 19 changelog, same week: cloud-agent subscriptions, /goal, and subagents on their own VMs. That is the agent-runtime story sitting next to the Git-host story. Do not collapse them.
Piece
Origin, today
Still GitHub
Who can use it
Early beta, all paid plans (enterprise can opt out)
Default remote for repos that started there
Source of truth
Two-way sync
Officially still source of truth for anything started on GitHub
Review surface
Native PRs and code browsing
Pushes keep going to GitHub
CI / deploys at launch
Vercel, Depot, Buildkite
Whatever you already wired
Agent runtime (Aug 19)
Cloud-agent subscriptions, /goal, subagents on their own VMs
Not a Git-host feature
Why a Git host matters for agents: the loop is checkout, edit, test, PR, retry. If the host flakes for six hours, the agent does not look smart. It looks stuck. Origin is a hedge with an explicit “GitHub remains source of truth” contract, not a flag-day migration. I would turn it on for a throwaway paid-plan repo and keep production remotes where the rest of the team already reviews.
August 18: Etched raised $700 million at $21 billion, led by Jane Street, after Jane Street tested the hardware and took the first rack. Company post: Etched, From Zero to One. Recap: TechCrunch.
Jane Street, on Etched’s site: they tested the chip, they are pleased with the early results, and they are excited to have their own rack running in their datacenter. That is a buyer with a rack, not a logo on a seed deck.
Etched sells full frontier inference clusters, not single-model ASICs. COO Robert Wachen told TechCrunch the systems pair a prefill chip with cluster-scale memory for decode, and that they run any frontier model. If you have been waiting for “the one model chip,” that is not this company.
Date
What
Valuation
December
Prior mark
$5 billion
July 23, 2026
$300 million Series C
$10.3 billion
August 18, 2026
$700 million, Jane Street-led
$21 billion
Named backers around the company: Kleiner Perkins, Sequoia, a16z, Peter Thiel, Tiger Global, Bain, Neo, Stripes, Primary, Positive Sum, Diffusion, Argo, Blackstone.
What a builder does with a $21 billion inference-cluster round: almost nothing this week, unless you buy clusters. The tell I care about is Jane Street putting a rack in their own datacenter after testing it. Watch cluster pricing and whether “any frontier model” stays true once the first production racks are full. Do not rebuild an agent graph around a chip you cannot buy.
Slack Code, and Nevada robotaxis
Two August 20 product-and-deployment notes. Neither changes my default model id. Both change where work shows up.
Slack Code. Slack launched coding-agent channels: Slack’s own post, with a product recap from The Verge. You tag a coding agent. It spins up a project code channel with diffs, a plan, and a live HTML preview. The channel auto-archives as an audit log. Launch partners: Claude (Anthropic), Devin (Cognition), GitHub Copilot, Vercel. Slack’s line on OpenAI: ChatGPT “available soon.” I am using Slack’s wording, not a secondary partner list.
Available today on any Slack plan. Partner-agent access is separate. Slack claims more than 70% of its internal code channels go idea-to-merged-PR in one day. That is a company stat, not an audited one. I will not cite it as a productivity law. If your team already lives in Slack, this is how a non-IDE stakeholder gets into the loop without a Cursor seat. If they do not, it is another inbox.
Nevada robotaxis, short. I write an AI-coding blog. I am not pretending this is a Cursor changelog. On August 20 the Nevada Transportation Authority unanimously approved commercial robotaxi permits in Clark County (Las Vegas). Caps over 12 months: Tesla 5,000, Waymo 1,000, Uber 1,000 via Motional and Zoox. Combined ceiling: 8,000. Zoox already has a 100-vehicle network permit. Tesla Cybercab chief engineer Eric Early said 5,000 is a ceiling, they would be extremely happy with about 2,500 in a year, “not because of the technology.” The Livery Operators Association opposed. Source: TechCrunch.
File it as: autonomy is being permitted in volume in one US county, with a hard cap and an incumbents’ objection. Then go back to your eval harness.
Days earlier
Gemini 3.7 Flash went GA on August 13, not this week. I am only parking the live price and the two benches Google published, so nobody “discovers” it in September and thinks it is new. Google’s Gemini 3.7 Flash post: intro price $0.75 / $3.75 per 1M input/output through December 31, 2026, then it doubles on January 1, 2027 to $1.50 / $7.50. FrontierCode 1.1: 43.6% vs 34.4%. DeepSWE v1.1: 65.3% vs 49.0%. It powers Gemini Spark in 160+ countries. If you have not priced a Flash-class Google SKU since early August, this is still the card to use. It is not a story from August 16 to 22.
Log the Stripe deal. Watch Token Billing plus routing as one vendor. Keep the multi-model fallback you have
Do not migrate off OpenRouter because of an unclosed acquisition
Paying prior Sol on the API
Move the cost model to $4 / $20, cached $0.40, promo at least through November 21, 2026
Do not assume ChatGPT Plus Sol is the same meter as gpt-5.6-sol, or that Codex moved
ChatGPT-only users
Expect Luna on Free/Go, August Sol on Plus/Pro
Do not tell a Work or Codex user that their snapshot changed. It did not
V4-Flash agent that now sees images
Trial deepseek-v4-flash-vision-exp with the 384-token image cap
Do not put an experimental id in a client’s stable catalog
Cursor on a paid plan, GitHub pain this month
Turn Origin on for a non-production repo. Keep GitHub as source of truth
Do not sell a GitHub-exit
Buying inference clusters
Ask Etched about a rack, Jane Street-style
Do not rewrite prompts around a chip you cannot order
Team lives in Slack
Pilot Slack Code with Claude, Devin, Copilot, or Vercel. Budget partner-agent access separately
Do not treat the 70% internal Slack stat as your team’s cycle time
Flash-class Google SKU still unpriced
Use the August 13 Gemini 3.7 Flash intro card through December 31
Do not log it as a this-week launch
Two rules I am using on retainers this month. Separate surface from model: ChatGPT Luna, gpt-5.6-sol, Codex-on-July, Origin, Slack Code, and OpenRouter are different products. Do not mix them before you change a default. Separate closed price from reported price: Sol’s $4 / $20 is in the API docs. Stripe’s deal value is not. Etched’s $21 billion is a priced round. If a number has no primary, it does not go in the cost model.
Takeaway
The week of August 16 to 22 was a pipes week. Stripe is buying the router. Cursor is hosting the repo. Slack is hosting the review channel. OpenAI is snapshotting ChatGPT onto GPT-5.6 August and putting a real cut on Sol. DeepSeek is letting V4-Flash see. Etched is selling clusters at a $21 billion mark. Nevada is permitting robotaxis. None of that is a reason to throw out last week’s shortlist. It is a reason to check who invoices you, which snapshot you are actually on, and whether an experimental vision id belongs in a loop that has to survive October.
If you need a next step that is not another tab of vendor benches: try one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Z.ai released GLM-5.3 on August 14, 2026. Same 743B mixture-of-experts base as GLM-5.2. Every reported gain is post-training, not a new architecture. It is live now on the GLM Coding Plan and ZCode. The API and downloadable weights are staged behind a safety review, with public weights expected around August 28.
Z.ai’s own framing is useful. It calls GLM-5.3 the most capable open-weights model for coding, then still puts Claude Fable 5 and GPT-5.6 Sol ahead on several of the hardest rows. That is open-weight coding and agent competition, not a closed-flagship takeover.
The other half of the story is the calendar. The same week GLM-5.3 shipped, xAI, Google, DeepSeek, Alibaba, Meta, and NVIDIA all moved. The AI race is not one model card. It is cadence plus price.
What shipped
GLM-5.3 reuses the GLM-5.2 base and scales post-training across longer-horizon coding, terminal, and agent environments. Decrypt and Unite.AI both stress that point: the architecture did not change. The recipe did.
Spec
GLM-5.3
Total params
743B MoE
Base
Same as GLM-5.2
What changed
Post-training only
Focus
Coding, agents, long-horizon tool use
Access today
GLM Coding Plan and ZCode
API
Coming after safety review
Weights
Expected around August 28
Setup on the Coding Plan is a single command, npx @z_ai/coding-helper, which wires the subscription into Claude Code, OpenCode, ZCode, and a long list of other agent tools. That is the practical path today. There is no GLM-5.3 row on Z.ai’s per-token API table yet.
That week was stacked
Mid-August 2026 compressed the release cadence into a few days. Patrick McGuinness’s week-in-review counted five frontier-class systems landing in the top of the board in one week. GLM-5.3 was one of them.
August 10: OpenAI shipped GPT-5.6-Cyber, a specialized cybersecurity model behind the Daybreak Red tier.
August 13: Google shipped Gemini 3.7 Flash, three weeks after 3.6 Flash, at an introductory $0.75 / $3.75 through December 31. That is half the original 3.6 Flash rate.
August 14: Z.ai shipped GLM-5.3.
August 14: Alibaba published Qwen3.8-27B under Apache 2.0. Dense, local, multimodal, 262K context. Artificial Analysis put it at 52 on the Intelligence Index, tied with GPT-5.6 Luna at max reasoning.
The same window also moved Qwen3.8-2.4T weights, Meta’s Muse Glimmer 30B, NVIDIA Nemotron 3.5 Lightning, ByteDance Seed2.1, and DeepSeek Harness. Western labs are competing on closed cadence and intro discounts. Chinese labs are competing on open weights and per-token economics. Builders get both at once, which is why this week felt exciting instead of noisy.
How the benchmarks look
The scorecard below is from Z.ai’s launch post, max effort. These are vendor rows. Fable 5 figures in that table often include fallback. Do not mix them with Artificial Analysis or Vals without saying so.
Where GLM-5.3 is competitive or leads
Benchmark
GLM-5.3
Closest closed / open
Notes
Terminal Bench 3.0
28.3
Sol 34.6; Fable 5 33.7
Open SOTA. GLM-5.2 was 4.6
Agents’ Last Exam (ALE-CLI)
28.5
Sol 28.6
Open SOTA, a 0.1 gap to Sol
AutomationBench v1.0.6
48.2
Fable 5 46.2; Sol 45.8
Ahead of both closed flagships
CyberGym
84.5
Fable 5 83.8; Sol 83.6
Ahead of the closed set
Terminal Bench 2.1
88.2
Sol 88.8; K3 88.3; Fable 5 88.0
Clustered, not a blowout
Z.ai Code Bench High
31.4%
Opus 4.8 29.5%
~50k output tokens vs ~120k
The High-effort Code Bench row is the efficiency story I care about for client loops. GLM-5.3 scores a bit above Opus 4.8 while burning far fewer output tokens. At Max effort it reaches 34.5% and still trails Fable 5 at 39.5%. Token economy vs Claude, not a Fable knockout.
Where it still trails closed frontier
Benchmark
GLM-5.3
Fable 5
GPT-5.6 Sol
Other
DeepSWE v1.1
66.9
69.7
72.7
K3 67.5; V4 Pro 0813 62.7
Z.ai Code Bench Max
34.5
39.5
n/a
vs GLM-5.2 at 23.4%
FrontierSWE
78.1
88.2
n/a
Opus 4.8 66.5
ProgramBench Almost Solved
19.0
33.0
23.0
K3 17.5
SWE-Marathon v1.1
42.5
33.1
42.5
K3 48.1; Opus 4.8 48.8
HLE with tools
62.5
63.9
64.5
K3 59.8
ExploitBench
54.4
78.0
76.5
Mythos 5 / Fable-class lead
ExploitGym 2h / 6h
105 / 130
181 / 247
216 / 293
GLM-5.2 was 29 / 39
Reading that as a builder: GLM-5.3 looks strongest as a cheap open coding-and-agent default. It jumped hard on the longest-horizon public boards versus GLM-5.2. Closed flagships still own the hardest single-shot coding, Terminal Bench 3.0, and offensive-security depth. Kimi K3 still nips it on DeepSWE. DeepSeek V4 Pro 0813 is behind on several of these same rows and cheaper on the API.
The CyberGym lead versus the ExploitGym gap is the clean split. Identification and validation scaled. Exploitation still lags Fable and Sol by a wide margin.
Cyber capability, at a high level
Z.ai says cybersecurity skill grew faster than it planned as post-training scaled. Unite.AI’s recap uses that as the headline: a capability that outgrew its training.
On the published boards, that shows up as a lead on CyberGym (white-box identification and validation) and a still-large gap on ExploitBench and ExploitGym (deeper exploitation under time budgets). The company also says the model flagged 2,436 vulnerabilities across 269 open-source projects, 1,097 of them medium-to-high severity. That is a vendor claim, not an independent audit.
It is also why the weights are late. GLM-5.2 went public fast. GLM-5.3 waits on safety evaluation and hardening. For delivery work that is the useful fact: stronger defensive scanning in an agent loop is interesting, and I am not treating a cyber leaderboard as a reason to point this model at anyone else’s systems.
The price war frontier labs are still fighting
GLM-5.3 itself has no published per-token API row. Do not copy GLM-5.2’s sticker onto 5.3 and call it official.
What you can budget today:
Path
Price
What it is
GLM Coding Plan Lite / Pro / Max
$18 / $80 / $168 per month
Subscription credits, not API tokens. Promo display is often lower
GLM-5.2 API (family anchor)
$1.40 in / $4.40 out per 1M
Live rate card. Not confirmed for 5.3
GPT-5.6 Luna
$0.20 / $1.20
After the July 30 cut
GPT-5.6 Sol
$5 / $30
Flagship unchanged
Claude Fable 5
$10 / $50
Premium closed tier
Claude Opus 5
$5 / $25
Pitched as half of Fable
Grok 4.6
$2 / $6
Held flat from 4.5
Gemini 3.7 Flash intro
$0.75 / $3.75
Through December 31, 2026
Verify Coding Plan numbers on Z.ai before you subscribe. The $18 / $80 / $168 list is the monthly anchor. Displayed promo prices have been lower.
The reason this table exists is the Chinese undercut, not a sudden burst of generosity from US labs.
On July 30, OpenAI cut GPT-5.6 Luna by 80% to $0.20 / $1.20 and Terra by 20% to $2 / $12. CNBC and Axios both framed it as cost pressure from customers and Chinese open-weight competition.
TechRepublic and the AFR write-up of the Financial Times story (August 14, the same day as GLM-5.3) name DeepSeek, Moonshot’s Kimi, and Zhipu’s GLM as the models forcing the issue. Chinese open-weight rates sit 60-90% below leading Anthropic and OpenAI prices on a per-token basis. Some of that coverage puts a comparable job at roughly 9× cheaper on GLM than on Claude. DoorDash, Airbnb, and Coinbase have confirmed using Chinese models for at least some workloads.
Anthropic’s answer is Claude Opus 5 at $5 / $25, pitched as frontier intelligence at half of Fable. Google’s answer this week is Gemini 3.7 Flash at half the original 3.6 Flash rate through year end. xAI’s answer is more Grok 4.6 capability at an unchanged $2 / $6. DeepSeek went the other way on August 16 and raised effective rates with peak/off-peak billing. That last move is in the V4 Pro 0813 post.
Builder takeaway: token price is becoming a commodity floor. Differentiation shifts to harness quality, latency, compliance, and the slice of work where a closed flagship still earns its markup. GLM-5.3 is another Chinese lab putting near-frontier coding on that floor. US labs are cutting mid-tier prices because the gap on everyday agent work got small enough to steal volume.
When I would use GLM-5.3
When I would reach for GLM-5.3
Coding Plan agent loops in Claude Code, OpenCode, or ZCode where a monthly credit bucket beats flagship token bills
Token-efficient coding against Opus 4.8-class work, where the High-effort Code Bench row actually matters
Long-horizon terminal and automation tasks where GLM-5.3 jumped hard versus 5.2
Teams that want an open-weight path after the safety review, once the August 28 weights actually land
When I would still pick closed flagships, Grok 4.6, or DeepSeek Flash
Compliance or client policy blocks Chinese API routes
Absolute top single-shot reasoning, FrontierSWE, and knowledge-work boards (Fable 5 / GPT-5.6 Sol)
Stacks already standardized on Cursor plus Grok 4.6, or on Anthropic / OpenAI harnesses and fallbacks
High-volume API work where DeepSeek V4 Flash still owns the raw per-token floor
Product contracts where provider policy is part of the delivery promise
Takeaway
GLM-5.3 is a post-training jump that puts open coding within a few points of Claude Fable 5 and GPT-5.6 Sol on several agent boards, at Chinese-lab economics, in a week when Google, xAI, DeepSeek, Qwen, and Meta all shipped. It leads some automation and defensive-security rows. It still trails on the hardest coding and exploitation benches. The weights are not out yet, and the API sticker is not published yet.
That is enough to shortlist. It is not enough to lock a stack. Try the same test I use for every release: one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
DeepSeek made DeepSeek-V4-Pro-0813 the official flagship on August 12-13, 2026. The tell was a version string on the pricing page and a changelog entry the next day. No company blog. No press post. Same API id: deepseek-v4-pro.
I am writing this the next day with the same builder lens I used for V4 Flash, Kimi K3, and Grok 4.6: not who won the internet, but what I would put on a long agent loop and how the bill looks when that loop runs all week. The access and procurement half of the Chinese-model story is still in Kimi K3, a possible US ban, and why some builders pick Codex.
This is the Pro follow-through the Flash post was waiting on. Flash 0731 already owns the volume-and-price story. 0813 is the 1.6T flagship leaving preview, plus a rate card that changes at 16:00 UTC on August 16.
What shipped
DeepSeek V4 first landed as a preview pair on April 24, 2026. Flash graduated on July 31. Pro stayed on the April checkpoint until this week. 0813 is a post-training update on the same MoE, not a new architecture.
Spec
V4 Pro 0813
V4 Flash 0731
Total params
1.6T
284B
Active per token
49B
13B
Context
1M tokens
1M tokens
Max output
384K
384K
License
MIT
MIT
API id
deepseek-v4-pro
deepseek-v4-flash
Concurrency
500
2,500
Modalities
Text only
Text only
Thinking modes are now low / high / max on both Pro and Flash. Thinking is on by default at high. Thinking tokens bill at output rates even when they never show up in the final answer, which matters more on long agent traces than the sticker does.
The API still speaks OpenAI ChatCompletions, Anthropic Messages, and the Responses API, with a Codex-oriented setup. Tool calls and JSON output stay on. There is no vision.
Weights for 0813 live on Hugging Face, with a DSpark speculative decoding module attached. The older preview-era Pro card still exists. Do not assume a local checkout of the April files is the same checkpoint the API now serves.
First days of real use
Launch charts are one input. Here is what showed up in the first days of actual use, with numbers attached where people published them.
The drop was quiet on purpose.Decrypt and TechTimes both noted there was no announcement post. The vendor table circulated via DeepSeek’s WeChat group, then into Reddit and Hacker News. Anyone already calling deepseek-v4-pro was already on 0813. No key rotation. No new endpoint.
Capability vs the closed frontier is the split reaction.SCMP reported some developers underwhelmed on overall IQ and unhappy about the coming price hike, while cybersecurity researchers were more impressed. Vals AI, in that same coverage and on its own index, flagged sandboxed terminal work and Excel financial models as the weak spots.
OpenRouter traffic is the first operational snapshot. On DeepSeek’s own provider, OpenRouter showed about 53 tok/s, 1.67s latency, a ~93% cache-hit rate, and about a 2% tool-call error rate. Cache hits are why the effective input price sits far below the $0.435 list. Tool-call errors are why I would not treat “it speaks the Responses API” as a free pass for strict JSON schemas.
One first-day coding trial already lives on this site. In the Grok 4.6 post, a first-day roundup ran the same new feature through Codex CLI on OpenRouter: DeepSeek V4 Pro 0813 took 12 minutes 2 seconds, cost $0.12, and shipped a bug. Grok 4.6 took 3 minutes 18 seconds, cost $1.41, and shipped clean. That is roughly 3.6× faster and about 12× more expensive, with a working result against a broken one. One trial is a signal, not a bake-off. It matches the rest of the reception: cheap and usable, not the model I would pick when wall time and a clean first pass matter more than the token bill.
How the benchmarks look
DeepSeek’s own agent table
The August 13 changelog is the vendor scorecard. These rows are DeepSeek Harness, max effort. Flash 0731 numbers are from the July 31 note. Pro preview DeepSWE and Cybergym come from the table TechTimes reproduced from DeepSeek.
Benchmark
V4 Pro preview
V4 Flash 0731
V4 Pro 0813
Terminal Bench 2.1
72.1
82.7
87.9
DeepSWE
12.8
54.4
62.7
Cybergym
52.7
76.7
83.3
NL2Repo
n/a
54.2
61.5
Toolathlon-Verified
n/a
70.3
74.1
Agents’ Last Exam
n/a
25.2
25.7
AutomationBench (Public)
12.8
25.1
31.8
HLE (wo / w tools)
n/a
n/a
42.7 / 60.0
DSBench-FullStack
41.8
68.7
71.1
DSBench-Hard
31.1
59.6
67.2
On DeepSeek’s board, 0813 retakes the agent lead Flash 0731 had over the April Pro preview. Decrypt’s read of the same vendor comparison against Claude Fable 5: Fable ahead by about 5.3% on average across overlapping agent rows, or 2.8% if you drop HLE without tools. Pro wins two of those rows. Terminal Bench 2.1 is a 0.1-point gap (87.9 vs 88.0). That is the “Fable is only 5% better” headline. It is DeepSeek’s table, not an independent bake-off.
Independent composites
Do not mix these with the vendor rows. Artificial Analysis and Vals ran their own harnesses. Recaps such as Office Chai walk the AA scorecard.
Model
AA Intelligence Index (max)
Claude Opus 5
63
Claude Fable 5
62
GPT-5.6 Sol / Grok 4.6
61
Kimi K3
60
Muse Spark 1.2
57
DeepSeek V4 Pro 0813
53
GLM-5.2
53
DeepSeek V4 Flash 0731
~52
GPT-5.6 Luna
52
That is an 8 to 10 point gap to the closed flagships I actually compare against for client work. OpenRouter also lists AA Coding Index around 68.8 and Agentic Index around 49.6 for 0813.
Selected AA rows from that same recap:
GPQA Diamond ~93%, near Opus 5 and a couple of points behind Grok 4.6 at 95%
Independent Terminal-Bench 2.1 at 79%, not DeepSeek’s 87.9, and about 10 points behind Opus 5 at 89%
SciCode 49%, behind Fable 5 at 60%
HLE ~39%
Cost per Intelligence Index task ~$0.06, versus about $3.14 for Fable 5, $2.34 for Opus 5, $1.23 for Sol, and $0.84 for Grok 4.6
Vals AI is harsher on the jobs that look like delivery work. V4 Pro 0813 scores 66.25% on the Vals Index, rank #12 of 47, up from 55.62% on the preview. Terminal-Bench 2.1 comes in at 54.68% (#33 of 52). EMB, the Excel modeling board, is 52.80%. Three different Terminal-Bench 2.1 numbers (87.9 vendor, 79 AA, 54.68 Vals) is the whole story: harness plus policy plus effort, not a single model IQ.
How it competes with frontier models
Composite IQ. 0813 is not a takeover. On AA it sits with GLM-5.2 in the low 50s. Fable 5, Sol, Grok 4.6, and Opus 5 still live in the low 60s. Kimi K3 is closer to that closed band than Pro is.
Price per point. This is still DeepSeek’s actual product. AA puts 0813 on the Pareto frontier with Flash. Office Chai’s read: climbing from DeepSeek’s 53 to Opus 5’s 63 costs about 39× more per task at current list. Decrypt’s blended-rate framing is similar: Fable at about $30 versus Pro at about $0.65, roughly 46×. Those ratios shrink after August 16. They do not disappear.
Coding and agents. DeepSeek’s table says near-Fable on Terminal-Bench 2.1 and ahead of Opus 4.8 on several agent rows. Independent AA and Vals say terminal loops and Excel still lag. The eesel Codex trial says Grok 4.6 was faster and shipped clean on one real feature. I would not pick 0813 over Grok inside Cursor when I already have 4.6 in the picker. I would pick 0813 over Fable when the job is a long, cache-heavy loop and the client can accept the route.
Context and openness. 1M context matches Flash, Kimi K3, and Muse Spark, and beats Grok 4.6’s 500K. MIT weights are the self-host path the closed flagships do not offer. Confirm you are pulling the 0813 files, not the April preview card.
Gaps that still matter. Text only. No vision. Concurrency 500 versus Flash at 2,500. Thinking tokens bill as output. Procurement still matters: cheap Chinese open models are exactly why US teams adopted them, and exactly why some stacks cannot use them. See the Kimi ban and coding plans post.
Pricing and the August 16 reset
Official API rates through 16:00 UTC on August 16 (cache-miss input / cache-hit input / output, per million tokens). Verify on the DeepSeek pricing page before you budget:
Model
Input (miss)
Input (hit)
Output
DeepSeek V4 Flash
$0.14
$0.0028
$0.28
DeepSeek V4 Pro
$0.435
$0.003625
$0.87
Grok 4.6
$2.00
$0.50
$6.00
GPT-5.6 Sol
$5.00
n/a
$30.00
Claude Fable 5
$10.00
n/a
$50.00
Worked examples at current Pro rates (approximate, uncached unless noted):
100K-token document analysis (100K input, 4K output): about $0.047
Repository coding session (200K input with 70% cache hit, 20K output): about $0.045
Multi-step agent trace (1M cumulative input with 80% cache hit, 100K output): about $0.174
Peak / off-peak billing starts at 16:00 UTC on August 16. Off-peak is half of peak. Peak hours are 01:00-04:00 and 06:00-10:00 UTC.
Model
Off-peak (miss / hit / out)
Peak (miss / hit / out)
V4 Flash
$0.22 / $0.007 / $0.66
$0.44 / $0.014 / $1.32
V4 Pro
$0.66 / $0.022 / $1.98
$1.32 / $0.044 / $3.96
After the reset, that same 100K / 4K document job is about $0.074 off-peak and about $0.148 at peak. Still cheap next to Fable. Not the $0.047 you can quote this week.
When I would use it
When I would reach for DeepSeek V4 Pro 0813
Harder single-shot and knowledge work than Flash, still on a 1M window
Cost-sensitive production features that need the 49B-active pass
Teams that want an MIT self-host path and will verify the 0813 weights
Long-context jobs where Grok’s 500K window is the constraint
When I would still pick Flash
High-volume terminal, repo, and tool-heavy agent loops
Workloads that need 2,500 concurrency rather than 500
Cost-sensitive defaults where Flash is already close enough on AA
When I would still pick Fable 5, GPT-5.6 Sol, or Grok 4.6
Absolute top composite reasoning and knowledge-work boards
Cursor-native long agent loops where first-day speed reports already favor Grok
Vision, verified terminal and Excel jobs, or stacks standardized on Anthropic or OpenAI harnesses
Compliance or client policy that blocks Chinese API routes
Takeaway
V4 Pro 0813 is the official flagship, not a new architecture. On DeepSeek’s board it retakes the agent lead Flash 0731 had over the April preview, and sits within a few points of Fable on several vendor agent rows. On Artificial Analysis it is still about 10 points off Opus 5, Fable 5, Sol, and Grok 4.6, at a cost per task that is still in a different league until August 16. Independent terminal and Excel boards are less kind than the changelog. The quiet launch was the version string. The loud part is the rate card.
Leaderboards help you shortlist. Delivery decides. Try the same test I use for every release: one real multi-file task with tools, constraints, and a deadline.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
xAI released Grok 4.6 on August 12, 2026. Cursor published a matching joint launch post the same day. I first wrote this the next day with the same builder lens I used for Muse Spark 1.2, Kimi K3, and DeepSeek V4 Flash: not who won the internet, but what I would put on a long agent loop for product and agency work. This update folds in what users actually reported in the first 24 hours.
The important framing is that this is a long-running-agent update on the Grok 4.5 line, not a raw scale jump. The official posts never publish a parameter count, so I am not inventing one. What they do claim is that 4.6 stays with research, codebase work, and “idea to working first version” jobs across many steps, with stronger visual first passes than 4.5.
This post is also the delivery test. I planned and wrote it with Grok 4.6 inside Cursor, the same loop I used to ship the blog with Grok 4.5.
What shipped
Grok 4.6 is live in Cursor, Grok Build, the SpaceXAI API, and partners including OpenRouter, Vercel, and Cloudflare. For the first week, xAI and Cursor are offering 2x included usage inside Cursor and Grok Build.
Reasoning efforts: low, medium, high (default), and xhigh (new versus 4.5, which topped out at high)
Knowledge cutoff listed as February 1, 2026
Training, as described in the launch posts: a longer supplemental run than 4.5, with curated model-generated data for reasoning and technical concepts, high-quality engineering data, and an improved optimizer. Grok 4.5 then regenerated SFT trajectories across reasoning efforts, agent harnesses, and domains such as STEM, software engineering, and knowledge work, with model-based filters on bad traces. RL covered general coding, knowledge work, and domain environments for kernel optimization, web development, and CAD.
That last point is the practical contrast with Muse Spark 1.2. Muse Code launched as a macOS/Linux terminal beta with no Cursor plug-in. Grok 4.6 is already in the IDE where I ship.
User experience
The UX story is less a new agent product and more how the model behaves on long jobs.
Long-horizon stickiness. xAI positions 4.6 to research an unfamiliar domain, structure an application, implement the core interactions, and keep refining through several rounds of feedback without dropping the thread.
Self-testing on longer trajectories. The launch materials say the model started checking its own work before moving on more often than 4.5 did. That is the behavior I care about in Cursor agent loops: fewer silent half-finished steps.
Stronger visual and interactive first passes. Given a concrete product idea, 4.6 is meant to establish structure and visual language in one pass, then iterate. That matches how I already use this stack for portfolio and client UI work.
Where I can actually test it. Grok Build remains the terminal agent path (curl -fsSL https://x.ai/cli/install.sh | bash). Cursor is the daily path. Unlike Muse Code’s Windows gap, I can run 4.6 on this machine inside the same editor that already holds this repo. Cursor’s own docs still split the stack: Composer for everyday speed and cost, Grok 4.6 for harder, longer sessions. On Pro and higher, Fast is the default speed tier. On Cursor Start, 4.6 is locked to medium effort in non-fast mode.
First 24 hours of real use
Launch charts are one input. Here is what showed up in the first day of actual use, with numbers attached where people published them.
Cursor landed a few hours after the API. On the morning of August 12, a Cursor forum thread asked when 4.6 would appear in the Agent / Chat picker. The user still only saw 4.5, and said 4.5 was usable but not good enough day to day. Cursor staff confirmed it was live later that afternoon in the Grok 4.6 is now Live announcement, with 2x included usage for the first week and Composer still positioned as the everyday model.
Speed is the loudest first-day report. A first-day roundup collected a Codex CLI head-to-head on the same new feature, both via OpenRouter: DeepSeek V4 Pro 0813 ran 12 minutes 2 seconds, cost $0.12, and shipped a bug. Grok 4.6 ran 3 minutes 18 seconds, cost $1.41, and shipped clean. That is roughly 3.6× faster and about 12× more expensive, with a working result against a broken one. One trial is a signal, not a bake-off. The same roundup quotes an engineer who left Claude after 4.5 and especially 4.6, calling Grok 3×+ faster with no obvious drop in engineering quality, and another whose old split was Sol for planning and Grok for building. After first tests, they were ready to use 4.6 for both.
Cache price is the first-day asterisk. Headline $2 / $6 did not move from 4.5. Cached input did: $0.30 to $0.50 per million tokens, and $0.60 to $1.00 on long-context cache. Heavy coding sessions are mostly cache reads. Users caught that within hours. OpenRouter’s early traffic snapshot in the same roundup showed about 90% cache hits, ~94 tokens/s, ~0.62s latency, and a 4% structured-output error rate on the standard endpoint versus 0% on the ZDR endpoint. If you pin JSON schemas for tools, test both.
The honest prior after one day. Independent hours on 4.6 are still thin. One first-day take, framed as speculation rather than a test, put real-world 4.6 around Opus 4.8 and clearly below Opus 5. That matches the benches: close on CursorBench and knowledge work, still behind Sol and Fable on DeepSWE and Terminal-Bench v3.0. Artificial Analysis also published an AA-Omniscience non-hallucination rate around 65.7%. For a coding agent, a wrong answer often dies in a test. For a customer-facing agent, it gets sent. I am not treating one day of forum and OpenRouter traffic as a verdict. I am treating it as the first filter: faster than Claude for several engineers, more expensive than Flash, watch the cache line, keep Composer for small edits.
How the benchmarks look
xAI launch table
xAI published a head-to-head against Grok 4.5 High, GPT-5.6 Sol Max, and Fable 5 Max. Competitor figures come from published system cards or leaderboards, not from xAI running every rival in one shared harness. Recaps such as Office Chai and DEV walk the same scorecard.
Eval
Grok 4.6 High
Grok 4.5 High
GPT-5.6 Sol Max
Fable 5 Max
AA Intelligence Index
61
56
61
62
GDPVal-AA v2
1753
1526
1728
1741
CursorBench v3.2
69.9%
66.7%
67.2%
70.5%
DeepSWE v1.1
65.9%
54%
73%
70%
FrontierCode v1.1 (Extended)
61.3%
56.6%
60.6%
63.6%
APEX-Agents
57.5%
47.1%
56.7%
59.2%
Terminal-Bench v3.0
26%
15.7%
34.6%
34.1%
APEX-SWE
56.4%
53.6%
n/a
58.8%
AA-Briefcase
1577
1313
1502
1574
Harvey LAB (Vals)
15.8%
12.9%
2.5%
11.3%
How 4.6 improved from 4.5
On that same table, Grok 4.6 High beats Grok 4.5 High on every published row.
Change
4.5 High
4.6 High
Delta
AA Intelligence Index
56
61
+5
GDPVal-AA v2
1526
1753
+227 Elo
DeepSWE v1.1
54%
65.9%
+11.9 points
Terminal-Bench v3.0
15.7%
26%
+10.3 points
CursorBench v3.2
66.7%
69.9%
+3.2 points
Terminal-Bench v3.0 still leaves 4.6 last among the four. CursorBench lands almost on Fable (70.5%). Against the closed flagships the pattern is mixed: tie with Sol on the composite, leads on GDPVal-AA, AA-Briefcase, and Harvey LAB, close on CursorBench and FrontierCode, still behind on DeepSWE and Terminal-Bench v3.0.
Coding tasks vs frontier peers
On coding specifically, the practical split looks like this:
IDE and agent coding loops: CursorBench puts 4.6 just behind Fable and ahead of Sol. That is the board closest to how I work in Cursor.
Repo-level SWE: DeepSWE still favors Sol (73%) and Fable (70%) over Grok 4.6 (65.9%), even after the jump from 4.5.
Terminal agent work: Terminal-Bench v3.0 is the clearest lag. 26% versus the mid-30s for Sol and Fable is not a rounding error.
Knowledge-work agents: GDPVal-AA and AA-Briefcase are where 4.6 leads the published set. Long research and document-style agent jobs are part of the pitch.
Vs other niches:Muse Spark 1.2 is competing as a model-plus-harness product with strong MCP Atlas claims. DeepSeek V4 Flash and Kimi K3 still own different open-price and open-weight stories. Grok 4.6 is the closed coding wedge already inside my IDE.
Builder reading: knowledge-work and IDE coding loops look closer to the flagships. Raw terminal and repo SWE still favors Sol and Fable.
Pricing
List rates from the xAI models docs. Prompts at or above 200k tokens bill at 2x for the whole request. A fast variant is twice the list price.
Model
Input / 1M
Cached input / 1M
Output / 1M
Grok 4.6 (< 200k prompt)
$2.00
$0.50
$6.00
Grok 4.6 (≥ 200k prompt)
$4.00
$1.00
$12.00
GPT-5.6 Sol
$5.00
n/a
$30.00
Claude Fable 5
$10.00
n/a
$50.00
Muse Spark Standard
$1.25
$0.15
$4.25
DeepSeek V4 Flash
$0.14
$0.0028
$0.28
This post in Cursor
Honest status: Grok 4.6 is in Cursor today. That is the difference that matters for me.
I planned this article and wrote the MDX with Grok 4.6 in Cursor: research the launch materials, lock the outline to this site’s blog voice, build the benchmark tables, and keep Sources honest. Same delivery shape as Building this blog with Cursor and Grok 4.5: multi-file constraints, outcome-led copy, no invented metrics.
xAI and Cursor are offering 2x included usage in Cursor and Grok Build for the first week. That is the window to point 4.6 at a real multi-file task with tools, constraints, and a deadline. I am not inventing a usage screenshot for this session. The 4.5 post already showed what that meter looks like on a similar feature.
When I would use it
When I would reach for Grok 4.6
Cursor agent loops on product and portfolio work where I already live, especially after first-day speed reports versus Claude
Long-horizon research, codebase, and “idea to first working version” jobs
Cost-sensitive frontier coding versus Fable or Sol list prices, while watching the higher cache-read rate
Visual and interactive first-pass UI where a strong structure pass saves steering
When I would still pick something else
Everyday small edits where Cursor still points at Composer for speed and cost
Hardest DeepSWE and Terminal-Bench v3.0 terminal jobs where Sol or Fable still lead
Workloads that need a full 1M context ingest (Kimi K3, Muse Spark, Flash)
Extreme per-token price floors where Flash wins on paper
Teams that need Muse Code’s persistent event-log harness as the product, not just a model inside Cursor
Takeaway
Grok 4.6 is a real step up from Grok 4.5 on xAI’s agentic board. It now sits in the Fable / Sol composite band at $2 / $6, and it is already inside Cursor with a first-week usage promo. The first 24 hours of user reports line up with that: faster than Claude for several engineers, more expensive than Flash, cache reads cost more than 4.5, Composer still wins small edits. It is not a verified sweep of DeepSWE or Terminal-Bench v3.0.
Leaderboards help you shortlist. Delivery decides. This post is one delivery test: planned and written with Grok 4.6 in Cursor.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Meta released Muse Code (beta) and Muse Spark 1.2 on August 5, 2026. I am writing this two days later with the same builder lens I used for Kimi K3 and DeepSeek V4 Flash: not who won the internet, but what I would put on a long agent loop for product and agency work.
The important framing is that this is a model plus harness product. Muse Spark 1.2 was co-trained with Muse Code. Meta’s coding benches mostly compare Muse Code against Claude Code, Codex, Grok Build, and similar agent products. That is closer to how developers ship than a bare model bake-off. It also means you should not treat the launch chart as a clean ranking of raw model IQ.
What shipped
Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1. Meta says it scaled training compute on coding tasks, widened training environments, and kept general agent strength. Context stays at 1M tokens. Access is through Muse Code, the Meta Model API with expanded global access (1.1 was US-only), and OpenRouter. Treat it as a hosted proprietary model: no downloadable weights in the launch materials.
Muse Code is a terminal coding agent for macOS and Linux, installed with a single curl command. There is no dedicated desktop app and no IDE plug-in at launch. On Windows, that is an immediate workflow gap for me: my daily shipping loop lives in Cursor on this machine, not a Linux terminal agent.
Meta also co-trained the model with the harness using rejection-sampled trajectories, goal conditioning, context compaction, and subagent recipes. That is the same industry move Anthropic and OpenAI made with Claude Code and Codex: the agent shell is part of the product, not an afterthought wrapper.
Muse Code user experience
The UX story is more interesting than another chat box.
Persistent async background agents. Most coding agents spawn a subagent, use it, and discard it. Muse Code keeps specialized background agents alive for the whole session so they accumulate context instead of re-gathering it. They decide when to report back to the main agent. Meta’s bet is less steering and less redundant repo exploration on hard multi-step work.
Append-only local event log. Every model call, tool run, approval, and edit is appended to one log. After a crash, Meta says the runtime can resume exactly where it stopped. That matters for long-horizon jobs more than for a five-minute autocomplete.
Parallel subagents in isolated worktrees. Large tasks can fan out across subagents in separate git worktrees so your working copy stays clean. Meta’s launch demos lean on that for multi-feature work without merge theater.
Bundled skills./plan turns a task into an approval-gated plan. /grill stress-tests that plan until it holds up. /goal drives toward a stated objective.
Compared with Claude Code, Codex, and Cursor’s agent loops: Muse Code ships a thoughtful runtime for persistence, recovery, and parallel work, on a thinner surface. Terminal beta only. No Windows-native install. No Cursor integration yet. Architecture first, distribution second.
How the benchmarks look
Meta-reported system scores
Meta’s methodology and launch coverage (compiled in pieces like Binary Verse AI) put Muse Spark 1.2 with Muse Code near the front of several coding and tool boards. These rows are model plus selected agent product, not one shared harness.
System
Terminal-Bench 2.1
DeepSWE 1.1
Meta Internal
GDPVal-AA v2
MCP Atlas
Claude Opus 5 + Claude Code
86.7%
65.0%
79.4%
1852
85.8%
Muse Spark 1.2 + Muse Code
82.9%
59.3%
70.6%
1631
90.3%
GPT-5.6 Terra + Codex
81.8%
64.8%
65.4%
1577
n/a
Grok 4.5 + Grok Build
81.6%
56.6%
n/a
1526
n/a
Muse Spark 1.1
76.2% verified / Meta claimed 80
53.0%
68.3%
1371
88.1%
Claude Opus 5 leads most of Meta’s supplied coding and knowledge-work rows. Muse Spark 1.2 is close on Terminal-Bench, improves DeepSWE by about 6 points over 1.1, and leads MCP Atlas at 90.3%. That MCP result is the clearest commercial signal for tool-heavy agent products.
Independent Artificial Analysis
Artificial Analysis scored Muse Spark 1.2 (xhigh) at 54 on the Intelligence Index, up from 51 on 1.1 and 43 on 1.0. That sits effectively tied with Grok 4.5 (54) and near GPT-5.5 (55), still behind Claude Opus 5 (61), Claude Fable 5 (60), GPT-5.6 Sol (59), and Kimi K3 (57).
The gain is concentrated in agentic work. GDPval-AA v2 rose about 260 Elo to 1631 (fifth among models AA has scored, ahead of Claude Opus 4.8). AA’s own Terminal-Bench v2.1 run moved from about 78% to about 80%. Cost per Intelligence Index task lands around $0.40 at Meta’s standard $1.25 / $4.25 pricing, among the cheaper options in that intelligence cluster, though token usage per task rose versus 1.1.
Reading both tables as a builder: Muse is competitive on terminal and repo agent work, strong on MCP-style tool orchestration, and not the composite flagship. Flash and K3 still own different open-price niches. Muse is Meta’s closed coding wedge.
Coding tasks vs frontier peers
On coding specifically, the practical split looks like this:
Terminal and repo loops: Muse Spark 1.2 + Muse Code sits just behind Opus 5 + Claude Code on Meta’s Terminal-Bench and DeepSWE rows, and slightly ahead of Terra + Codex and Grok 4.5 on Terminal-Bench in that same chart.
Tool orchestration: MCP Atlas is where Muse leads the supplied set. If your agents call many MCP servers and distractor tools, that is the row I would watch first.
Long-horizon persistence: Meta’s GPU kernel case study runs 1,000+ tool calls for up to 24 hours (write, compile, profile, revise Triton kernels on Hopper). Muse posts a strong KDA speedup in Meta’s table but does not top Opus 5 or Sol. The useful signal is staying in the loop, not claiming a general coding crown.
Vs open cheap tiers:DeepSeek V4 Flash still owns absurd per-token economics. Kimi K3 still owns the open 3T-class swarm story. Muse is competing with Claude Code and Codex on closed agent systems and mid-range API pricing.
Pricing and data terms
Meta is competing on price as hard as capability. Both tiers keep the 1M context window.
Tier
Input / 1M
Cached input / 1M
Output / 1M
Data use
Standard
$1.25
$0.15
$4.25
Meta says not used to improve products
Contributor
$0.10
$0.002
$0.20
Prompts and completions used to train / improve Meta products
Rough worked example from launch coverage: 1M uncached input plus 100K output is about $0.12 on Contributor and about $1.68 on Standard. Cache hits collapse that further.
Cursor wishlist
Honest status: Muse Spark 1.2 is not in Cursor today. My daily IDE stack stays on Composer, Grok, and the other models Cursor already exposes. That is where I already ship this site (Building this blog with Cursor and Grok 4.5).
What I want next is simple: Cursor adds Muse Spark 1.2 (directly or via OpenRouter) so I can run the same multi-file IDE loop I use for client work, without leaving the editor for a macOS/Linux terminal beta. Until that lands, the test path is Meta Model API or OpenRouter for API experiments, and Muse Code CLI only if I am on a supported OS.
If Cursor ships it, the first thing I will do is the same delivery test I use for every release: one real multi-file task with tools, constraints, and a deadline.
When I would use it
When I would reach for Muse Spark 1.2 / Muse Code
MCP-heavy agents where tool selection and orchestration dominate
Long-horizon terminal jobs that need persistence, an event log, and crash resume
Cost experiments on non-sensitive code, especially on the Contributor tier with eyes open
Teams outside the US that could not use Muse Spark 1.1’s earlier API footprint
When I would still pick something else
Top verified coding boards and stacks already standardized on Claude Fable 5 / Claude Code or GPT-5.6 Sol / Codex
Windows-first IDE workflow until Muse Code or Cursor support lands
Confidential client repos that cannot accept Contributor training terms
Open-weight or extreme price-floor needs where Flash or K3 fit better
Takeaway
Muse Spark 1.2 and Muse Code are a serious Meta coding product: competitive on Meta’s terminal and repo charts, strong on MCP Atlas, thoughtfully built for long-running multi-agent work, and aggressive on API pricing. They are not a verified takeover of Claude Code or Codex. Watch independent Terminal-Bench verification, keep Contributor data terms in mind, and hope Cursor adds the model soon so builders can run the same delivery test inside the editor.
Leaderboards help you shortlist. Delivery decides.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
On July 31, 2026 DeepSeek promoted DeepSeek-V4-Flash-0731 from preview to official API public beta. Same 284B MoE with 13B active parameters, same 1M-token context, MIT weights. What changed is the agent post-training and the sticker: $0.14 input / $0.28 output per million tokens on a cache miss.
I am writing this the next day with the same builder lens I used for Kimi K3 and Grok 4.5: not who won the internet, but what I would put on a long agent loop and how the bill looks when that loop runs all week. The access and procurement half of the Chinese-model story is still in Kimi K3, a possible US ban, and why some builders pick Codex.
What shipped
DeepSeek V4 first landed as a preview pair on April 24, 2026. Flash is the efficiency tier. Pro is the flagship. Both ship open weights under MIT and a default 1M context window with up to 384K output tokens.
Spec
V4 Flash 0731
V4 Pro (preview)
Total params
284B
1.6T
Active per token
13B
49B
Context
1M tokens
1M tokens
Max output
384K
384K
License
MIT
MIT
API id
deepseek-v4-flash
deepseek-v4-pro
The 0731 build keeps the same architecture and size as the Flash preview. DeepSeek says it was only re-post-trained. The official Flash API also natively supports the Responses API format and is adapted for Codex. Weights live on Hugging Face.
One migration detail that still bites people: the legacy deepseek-chat and deepseek-reasoner aliases retired on July 24, 2026. Point production traffic at deepseek-v4-flash or deepseek-v4-pro explicitly.
How the benchmarks look
DeepSeek’s own changelog lists a steep jump from Flash preview to 0731 on agent tasks. Several rows also beat the still-preview V4 Pro checkpoint.
Benchmark
V4 Flash preview
V4 Flash 0731
V4 Pro preview
Notes
Terminal Bench 2.1
61.8
82.7
72.1
vs Opus 4.8 ~85.0; GLM-5.2 ~81.0
NL2Repo
39.4
54.2
n/a
Cybergym
38.7
76.7
n/a
DeepSWE
7.3
54.4
n/a
Toolathlon verified
49.7
70.3
n/a
Agents’ Last Exam
15.8
25.2
n/a
AutomationBench Public
10.8
25.1
n/a
DSBench-FullStack
n/a
68.7
41.8
DeepSeek internal set
DSBench-Hard
n/a
59.6
31.1
DeepSeek internal set
Independent composites tell a similar story at the index level. Reporting around Artificial Analysis puts Flash 0731 around 50 on the Intelligence Index at max reasoning effort, ahead of V4 Pro at 44, while still trailing the closed flagships that sit near the high 50s.
Reading that as a builder: Flash 0731 looks strongest for terminal loops, tool-heavy agents, and high-volume coding automation. Opus-class and Fable-class boards still lead on the hardest single-shot reasoning. The awkward part of this release is that DeepSeek’s own agent numbers put the 284B Flash ahead of the 1.6T Pro preview until the official Pro post-training lands.
Pricing: why this feels absurdly cheap
Official API rates (cache-miss input / cache-hit input / output, per million tokens). Verify on the DeepSeek pricing page before you budget:
Model
Input (miss)
Input (hit)
Output
DeepSeek V4 Flash
$0.14
$0.0028
$0.28
DeepSeek V4 Pro
$0.435
$0.003625
$0.87
GPT-5.6 Luna
$0.20
n/a
$1.20
GPT-5.6 Sol
$5.00
n/a
$30.00
Claude Fable 5
$10.00
n/a
$50.00
Grok 4.5
$2.00
n/a
$6.00
On paper, Flash output is about 100× cheaper than Claude Fable 5 and about 4× cheaper than GPT-5.6 Luna after OpenAI’s July 30 cut. Cache hits make the gap wider still: $0.0028 per million cached input tokens is a 98% discount off the miss rate.
Worked examples at Flash rates (approximate, uncached unless noted):
100K-token document analysis (100K input, 4K output): about $0.015
Repository coding session (200K input with 70% cache hit, 20K output): about $0.014
Multi-step agent trace (1M cumulative input with 80% cache hit, 100K output): about $0.058
That is the shape that changes product math. A feature that was too expensive to run on every ticket or every PR becomes cheap enough to leave on by default.
The price war frontier labs are fighting
The calendar around this release is the story as much as the model card.
On July 30, OpenAI cut GPT-5.6 Luna by 80% to $0.20 / $1.20 and Terra by 20% to $2 / $12. Sol stayed at $5 / $30. OpenAI credits GPT-5.6 Sol with rewriting production GPU kernels and cutting end-to-end serving cost by about 20%. CNBC and Axios both framed the move as cost pressure from customers and Chinese open-weight competition, not charity.
On July 31, DeepSeek answered with official Flash 0731 at $0.14 / $0.28. That undercuts Luna on output within hours. Coverage such as Office Chai and Wccftech treated it as the next volley in an open price war.
DeepSeek also made its 75% V4 Pro discount permanent. The promotion was scheduled to expire May 31; The Next Web reports list rates that would have been $1.74 / $3.48 are now locked at $0.435 / $0.87. Google has been cutting Gemini Flash-class prices through 2026 for the same reason. Anthropic’s flagship Fable tier still lists at a premium; the bet there is that the quality gap on hard reasoning still pays.
Builder takeaway: token price is becoming a commodity floor. Differentiation shifts to harness quality, latency, compliance, and the slice of work where a closed flagship still earns its markup.
V4 Pro: official 0813 is out
That July 31 “follow soon” note landed. deepseek-v4-pro now serves DeepSeek-V4-Pro-0813. I wrote the builder comparison against Fable 5, GPT-5.6 Sol, and Grok 4.6, including the August 16 peak/off-peak rate card, in DeepSeek V4 Pro 0813 vs Fable 5, GPT-5.6 Sol, and Grok 4.6. Flash 0731 remains the volume-and-price default. Reach for official Pro when you need the 49B-active pass and can live with 500 concurrency.
Grok 4.6 and 4.7 are next from xAI
While Chinese open models set the cost floor, xAI is iterating on cadence. On July 28, Elon Musk posted that Grok 4.6 releases around August 7 as a 1.5T model with significantly improved SFT and RL. Grok 4.7 follows a few weeks later as a 2.1T model that Musk says will be better than 4.6 in every way except slightly slower to serve, with even better token efficiency.
That sits on top of Grok 4.5, which went public on July 8 at $2 / $6 and was trained with Cursor for coding and agent work. I already wrote about shipping this site with that stack in Building this blog with Cursor and Grok 4.5.
Western labs are competing on release cadence and closed harnesses. Chinese labs are competing on open weights and per-token economics. Builders get both: a cheaper floor for volume work, and a fast-moving premium tier for the jobs that still need it.
August 2026 is going to be juicy
July closed with a price war. August opens with a release stack. Official DeepSeek V4 Pro is due any day. Grok 4.6 targets around August 7, then Grok 4.7 a few weeks after that. OpenAI and Google will not sit still while Chinese open weights keep setting the floor. If you are picking a model stack for a Q3 ship, this is the month to re-benchmark, not the month to lock in last quarter’s defaults.
When I would use V4 Flash
When I would reach for DeepSeek V4 Flash
High-volume terminal, repo, and tool-heavy agent loops
Long-context workloads where a 1M window fits whole codebases or long traces
Cost-sensitive production features that must stay on by default
Teams that can accept Chinese-model procurement and access risk
When I would still pick closed flagships
Compliance or client policy blocks Chinese API routes
Absolute top single-shot reasoning and knowledge-work boards
Stacks already standardized on Anthropic or OpenAI toolchains, eval harnesses, and fallbacks
Product contracts where provider policy is part of the delivery promise
Takeaway
V4 Flash 0731 puts near-frontier agent performance at roughly 1/100th the output cost of Claude Fable 5 on paper, and under OpenAI’s freshly cut Luna tier on output price. That is why OpenAI moved Luna 80% the day before. The market is splitting into a cheap open-weight tier and a premium closed tier. August 2026 is going to be juicy: official V4 Pro, Grok 4.6, and Grok 4.7 land in the same window. Shortlist with leaderboards. Decide with one real multi-file task, tools, constraints, and a deadline.
If you want help picking a model stack that still ships under real usage and cost constraints, start on the contact page.
Coding agents ship UI fast. They also ship the same defaults fast: purple meshes, Inter heroes, equal feature cards, and chrome that looks like every other generated landing page.
Open the Impeccable demo landing (new tab) for a live Persuade-mode page built for this post. That HTML was rebuilt from the ground up with the installed Impeccable skill in Cursor (init, new-work direction roll, polish, audit/detect), running on Grok 4.5 high. It is not a paste of the marketing site. For how I have been using that model on this portfolio, see Building this blog with Cursor Grok 4.5.
Impeccable is the missing shared vocabulary: named interventions, locked product context, live iteration in the running app, and deterministic checks before the defaults land in production. This post is the builder walkthrough. For the broader skill and inspiration bookmark list, see Anti-slop design skills and inspiration pins.
What it actually is
Impeccable is one skill with about 23 commands, created by Paul Bakaus / pbakaus. You install a build tuned to your harness:
npx impeccable install
That path targets Cursor, Claude Code, Gemini CLI, Codex CLI, and peers. Copilot users can enable it under experimental settings. First run in a project:
/impeccable init
Version 4 is a leaner core tuned for frontier models, with clearer visitor modes and pages that commit to a direction instead of hovering in safe mid-tones.
The useful idea is not “another frontend prompt pack.” It is a language you share with the agent: /polish, /distill, /audit, /typeset, and the rest. Each name one kind of intervention.
Context before craft
Without project context, design skills invent a new system every session. Impeccable expects two files:
PRODUCT.md: who it is for, what success looks like, brand voice, and anti-references (the looks you refuse)
DESIGN.md: colors, type, components, elevation, and rules in a portable format tools can read
/impeccable init gathers that once. /impeccable document can reverse a DESIGN.md from an existing codebase when you already have tokens and components. Later commands read both files before they touch UI.
That is how you keep Win95 sharp corners, celestial glass, or a sober agency palette from getting overwritten by the model’s favorite radius and purple.
Surface modes
Impeccable names what the surface is for before it designs:
Mode
Job
Persuade
Win attention and move someone to act
Operate
Help someone finish a task
Read
Build understanding
Experience
Let the work lead
A marketing hero and an on-call dashboard should not share the same density, motion, or CTA weight. Mode is judged from the page, not only from what the company sells, so one product can hold all four.
The demo landing for this post is Persuade: one idea, one hierarchy, one primary CTA, and no card soup above the fold.
The command language
You do not need every verb on day one. Group them by job.
Setup and system
init: interview and write PRODUCT / DESIGN context
document: extract DESIGN.md from code you already have
extract: pull repeated patterns into tokens and primitives
shape / craft: plan or build with the vocabulary loaded
Quality
critique: hierarchy, clarity, emotional fit
audit: a11y, performance, responsive gaps
polish: final pass inside the existing system
harden: overflow, empty states, edge cases
onboard: first-run and activation paths
Amplitude
bolder / quieter: push or restrain presence
distill: strip to the one idea
Typeset, color, layout, and motion verbs when those are the only levers you want moved
Live
/impeccable live: open the running app, pick an element or steer the page, generate variants, write the winner to source
Day-to-day loop I would run on a section route:
/impeccable audit the surface
/impeccable distill if the page is doing three jobs
/impeccable polish before merge
Slop detector and shipping gates
Impeccable ships on the order of 60 deterministic detector rules (the site also cites a mid-50s check set depending on build). Examples of tells it is meant to catch: AI beige, italic serif display on soft cream, nested cards, side-tab accent borders, decorative pulsing status dots, equal feature triplets.
Those checks show up in three places:
Agent hooks on UI edits, so findings bounce back into the same turn
Chrome extension overlay on staging or a competitor page
CI CLI: npx impeccable detect src/ with JSON and exit codes for PR gates
That is production discipline. Taste without a gate still ships the model default when you are tired.
Worlds on the table
Ask a model to “be creative” and it often rebuilds its favorite layout. Impeccable can deal human-reviewed visual worlds as challengers: complete graphic systems with their own laws. The winner takes the build. Treat this as a direction tool for greenfield marketing work, not a requirement for every dashboard polish.
How I would use it on this stack
This portfolio is Astro 7, React islands, Tailwind tokens, and theme modes that already ban a lot of AI chrome. Impeccable fits as:
Init once so PRODUCT.md knows owners, agencies, and recruiters as audiences, and anti-references include purple gradients and Trustpilot theater
Document or maintain DESIGN.md against real tokens and components
Polish section routes (/projects, /contact) without inventing a second brand
Keep theme laws intact (Win95 zero radius, celestial Space Grotesk, default blueprint grid)
I still pin inspiration boards for section order and proof placement. Skills stop the default look. Boards stop blank-page guessing. That split is spelled out in the anti-slop bookmark post. Impeccable is the vocabulary and enforcement layer on top.
Live demo
Open the Lexicon demo landing in a new tab. It is a self-contained HTML page (skip link, landmarks, focus styles, reduced-motion) built as a drum-machine step-row world: named Impeccable commands as punchable steps, silkscreen labels, and a chase light for “now.” Rebuilt with the installed Impeccable skill in Cursor (npx impeccable install, /impeccable init, new-work direction seed, polish, impeccable detect), with Grok 4.5 high as the model in session. Not affiliated with the Impeccable product. Inspired by the workflow on impeccable.style.
Takeaway
Named interventions plus locked context plus detector gates turn agent speed into intentional UI. If your coding agent only “makes it look nice,” you will keep shipping the same page with different copy. Give it a vocabulary, then make the checks fail the PR when the tells return.
If you want help shipping a production web app with intentional UI, start on the contact page.
Moonshot shipped Kimi K3 in mid-July. A few days later, Axios reported that the Trump administration is weighing moves that could lock US companies out of cutting-edge Chinese AI models. I covered the model itself in Kimi K3 vs Claude Fable 5 and GPT-5.6 Sol. This post is the other half of the story: access risk, price, and whether a coding seat lasts a real work week.
I am not writing this as geopolitics theater. For product and agency work, three questions matter. Can you still call the model? Does the API bill stay sane? Does the subscription meter leave you mid-sprint with nothing left?
Why Chinese AI is in the US crosshairs
Open-weight Chinese models from DeepSeek, Moonshot, and the Qwen lineage closed a lot of the quality gap while US labs kept flagship prices high. K3 sits near Claude Fable 5 and GPT-5.6 Sol on independent composites, ships a 2.8T-class MoE, and undercuts closed flagship token rates. That combination is exactly the competitive shock Washington is reacting to.
The security story runs beside the performance story. Reporting around the Axios piece describes officials highlighting backdoors, governance gaps, and liability for companies that host or depend on Chinese open-source stacks. You do not need an outright ban for that narrative to change procurement and risk review inside US firms.
The toolkit under discussion, summarized by Axios and secondary outlets such as ChosunBiz, looks gradual rather than dramatic:
Adding Chinese AI labs to the Commerce Department Entity List
Procurement rules that pressure companies away from Chinese models
Cybersecurity advisories that chill adoption without a statute
Liability-style executive order language for hosting foreign models
As of writing, there is no formal ban. The point of these levers is a chilling effect. US companies drop Chinese models because the compliance and political cost rises, not because the weights suddenly disappear from GitHub.
There is also a counter-voice inside the same administration orbit. David Sacks and other pro-competition voices have argued that limiting American access to tools Chinese models already handle makes the US less competitive. Attribute that as a live policy fight, not a settled fact.
Chinese models win on sticker price
API list prices still explain why US teams reached for Chinese open models in the first place. Approximate cache-miss input and output rates per million tokens (verify on the vendor pages before you budget):
Model
Input
Output
DeepSeek V4 Pro
$0.435
$0.87
Kimi K3
$3
$15
GPT-5.6 Sol
$5
$30
Claude Fable 5
$10
$50
DeepSeek sits in a different price band entirely. K3 is still roughly half the closed flagship token cost while competing on coding and agent boards. Open weights add another path: teams with GPUs can self-host and escape per-token bills after the download ships. Moonshot promised K3 weights by late July; that path matters as much as the API sticker.
Kimi plans: weekly quota and the 5-hour wall
Sticker price is not the same as usable capacity on a membership seat. Kimi Code membership docs describe two meters that catch heavy users:
A weekly quota that refreshes every seven days from your subscription date, with unused quota that does not roll over
A rolling 5-hour rate window that can throttle you even when weekly quota remains
That dual system is easy to misunderstand. One meter is a hard weekly stop. The other feels like intermittent 429s mid-session until the window rolls. CLI /usage, the Code Console, and the web membership page also do not always show the same numbers, which makes it harder to plan a day of agent work.
Kimi’s consumer ladder runs Moderato ($19/mo) through Vivace ($199/mo), with Allegro at $99/mo as a common power-user seat. On paper that looks durable. In practice, Allegro users report burning weekly quota in a few days of intensive coding, and the 5-hour throughput window can interrupt long agent loops before the weekly meter is empty.
Extra Usage exists as a paid escape hatch. Once enabled, overage bills closer to Open Platform API rates. That rescues a blocked sprint, but it also turns the “cheap Chinese seat” story into metered spend the moment you hit the wall.
I already wrote about Kimi membership latency and capacity pain in the K2.5 era in the benchmarks post. The same lesson applies to K3-era plans: a model that wins boards still fails the workday if weekly and 5-hour meters empty while you are shipping.
Why some builders prefer Codex plans
OpenAI bundles Codex into ChatGPT plans. Plus sits around $20/mo with Codex across web, CLI, IDE, and related surfaces. Pro adds 5x or 20x usage at $100 or $200/mo. One bill covers chat and the coding agent, and the upgrade ladder is clear when you need more headroom.
That is the generosity argument builders make in plain language. Heavy agent users often pay for Pro so a long multi-file loop does not die mid-session on a dual weekly-plus-5-hour meter. The stack is also already standard for many US-facing client projects, which reduces compliance friction if Chinese-model access gets colder.
Fair balance: ChatGPT Plus can exhaust on a heavy coding day too. Generosity is relative. People who prefer Codex are usually comparing power-user burn rates and reset behavior, not reading Plus marketing copy as unlimited. Claude Max is the other common “pay for headroom” lane at a similar $100/$200 shape; the point is usable capacity, not brand loyalty.
If your week is long agent loops, parallel tasks, and few interruptions, a more generous US coding seat can beat a cheaper Chinese membership that empties by Wednesday. If your week is bursty API volume on well-scoped jobs, Chinese token prices (or self-host after weights ship) can still win.
Takeaway
Treat the Kimi K3 moment as three layers, not one headline:
Policy access risk. Reported US curbs are not law yet, but Entity List, procurement, and liability pressure can freeze Chinese-model adoption inside US companies.
API sticker price. Chinese models still undercut closed US flagships, which is why teams adopted them and why Washington cares.
Subscription usable capacity. Kimi’s weekly quota and 5-hour window burn fast for agentic coding; some builders prefer Codex plans because the bundled ChatGPT ladder buys more uninterrupted work.
For client and agency delivery, keep the same test I use on every release: one real multi-file task with tools, constraints, and a deadline, plus an honest check of whether the seat lasts the week. Leaderboards and geopolitics both matter. Delivery still decides.
If you want help picking a model stack that still ships under real usage and compliance constraints, start on the contact page.
Coding agents ship working UI fast. They also ship the default look even faster: purple gradients, Inter heroes, three identical feature cards, and centered everything.
More mood boards alone do not fix that. What I pin for client and portfolio work is a two-layer stack: rules the agent must obey, then references that teach structure. This post is that bookmark page.
Skills that fight AI slop
I start with two agent-native design skills. Both install as portable SKILL.md packs that Cursor, Claude Code, Codex, and peers can load.
Hallmark
Hallmark (Together AI / Nutlope) picks a layout family first, dresses it with a theme, then runs slop-test gates before it ships. The useful part for real work is the verb set: build by default, then study a URL or screenshot into a portable design brief, audit an existing page, or redesign with different bones.
npx skills add nutlope/hallmark
Taste Skill
Taste Skill is the broader anti-slop pack I reach for when the brief needs a declared design direction, variance, and a hard pre-flight check. Default install is v2 design-taste-frontend, with companion skills for GPT/Codex, image-to-code, redesign audits, minimalist, and brutalist directions. Several of those variants already live in this repo under .agents/skills/.
These sit next to Hallmark or Taste Skill. They do not replace them.
UI Craft: score and gate generated UI after the agent writes code
ui-skills (baseline-ui): polish spacing, accessibility, and motion on UI you already have
UI/UX Pro Max: searchable style, palette, and font starting points by product type
skills.sh: discovery and install layer for the ecosystem
Agent Skills: the portable SKILL.md standard that makes the packs above work across tools
Workflow I actually use: Taste or Hallmark to set direction, ui-skills or UI Craft to clean, and never invent fake social proof or Trustpilot stars.
Inspiration boards to pin
Skills stop the default look. Boards stop blank-page guessing. I pin sites that already convert in the category, then rebuild the information architecture in the real stack with real copy.
Always pinned
Recent (formerly Godly): web, motion, and editorial rhythm. Steal the beat count above the fold, not the animation library.
Land-book: landing page section sequences. Filter by page type and copy the funnel order, not the Framer skin.
Awwwards: award-tier craft. Reverse-score winners against what the project actually needs to win on (usability vs spectacle).
Dribbble: component sketches. Extract one reusable pattern and rebuild it with real content and constraints.
Add by job
Landing / launch
Landingfolio: section-level SaaS and AI marketing refs
Behance: process and brand systems, not just hero shots
Takeaway
Skills stop the default look. Boards stop blank-page guessing. Together they turn agent speed into intentional UI instead of another purple gradient landing page.
If you want help shipping a production web app with intentional UI, start on the contact page.
Moonshot AI released Kimi K3 on July 16, 2026. I am writing this the next day, with the same builder lens I used for Grok 4.5: not “who won the internet,” but what I would actually put on a long agent loop for product and agency work.
Moonshot’s own launch post says the quiet part out loud. Overall performance still trails the strongest proprietary models, Claude Fable 5 and GPT-5.6 Sol. That honesty is useful. It frames K3 as open-scale frontier competition, not a magic takeover.
What shipped
Kimi K3 is a 2.8-trillion-parameter mixture-of-experts model. Moonshot activates 16 of 896 experts per token, ships a 1,048,576-token context window, and treats vision as native input. The API model id is kimi-k3. Full weights are promised by July 27, 2026; the API and consumer products are live now.
Two product variants matter for builders:
K3 Max for chat, reasoning, and single-agent autonomous tasks
K3 Swarm Max for large-scale parallel multi-agent orchestration
Architecture notes from the launch materials include Kimi Delta Attention for long-context decode, Attention Residuals for training efficiency, and Stable LatentMoE. Treat the biggest speed and efficiency claims as vendor claims until the technical report ships with the weights.
Agent Swarm
Agent Swarm is the feature I care about most for real delivery work. Moonshot’s Agent Swarm docs (built through the K2 line and carried into K3 Swarm Max) describe an orchestrator plus specialist sub-agents that can run in parallel.
What the docs and launch case studies emphasize:
Up to hundreds of concurrent sub-agents on swarm workloads
Thousands of tool calls on hard tasks
Roughly 4.5× faster wall time versus a single-agent path on comparable work
Training that freezes specialists and improves the “coach” (PARL: Parallel-Agent Reinforcement Learning)
That shape matches how I already ship AI features: research a codebase or domain, fan out tools, then merge results into one coherent change. OpenAI’s GPT-5.6 Sol ultra mode is a closed-stack cousin (a handful of parallel subagents by default, more on some evals). K3’s bet is open scale plus swarm orchestration at lower token cost.
How the benchmarks look
Independent composites put K3 right behind the two closed flagships. On Artificial Analysis Intelligence Index v4.1 around launch week:
Model
Intelligence Index
Claude Fable 5
59.9
GPT-5.6 Sol
58.9
Kimi K3
57.1
Moonshot’s launch table (max reasoning effort) and Arena preference boards show a sharper split by task type.
Where K3 leads or ties
Area
K3
Fable 5
GPT-5.6 Sol
Frontend Code Arena (Elo)
1679 (#1)
1631
1618
Program Bench
77.8
76.8
77.6
SWE Marathon
42.0
35.0
39.0
BrowseComp
91.2
88.0
90.4
AutomationBench
30.8
29.1
29.7
Terminal-Bench 2.1
88.3
84.6
88.8
Where K3 still trails
Area
K3
Fable 5
GPT-5.6 Sol
DeepSWE
67.5
70.0
73.0
FrontierSWE (dominance)
81.2
86.6
71.3
HLE-Full (no tools)
43.5
53.3
44.5
HLE-Full (with tools)
56.0
63.0
58.0
GDPval-AA v2 (Elo)
1668
1760
1748
GPQA-Diamond
93.5
92.6
94.1
Reading that as a builder: K3 looks strongest on long-horizon coding, web research, frontend preference, and cost-sensitive agent loops. Fable 5 still owns several hard reasoning and knowledge-work boards. Sol edges several coding and STEM rows by small margins.
Price and when I would use it
API pricing at launch (cache-miss input / output, per million tokens):
Model
Input
Output
Kimi K3
$3
$15
GPT-5.6 Sol
$5
$30
Claude Fable 5
$10
$50
K3 also advertises aggressive prefix-cache hits on coding workloads, which matters more than list price once an agent is looping on the same repo context.
Membership is separate from API billing. Kimi’s consumer plans use musical tempo names (Adagio free, then Moderato, Allegretto, Allegro, Vivace). Current list prices live on the membership pricing page. Allegretto is $39/mo monthly or about $31/mo on annual billing today.
About four months ago, during the Kimi 2.5 era, I subscribed to a paid Kimi plan in that ~$30 USD range. On paper it looked like a solid mid-tier seat for long chats and agent work. In practice the product felt slow, and it often seemed like the service was hitting capacity limits. Capability on a leaderboard does not matter if you are waiting on a queue when you need to ship. That history is why I am watching K3 Swarm Max and API capacity as closely as the benchmark tables.
Kimi 2.5 also shows up inside my daily IDE stack. Cursor’s Composer 2.5 is built on the same open-source Moonshot checkpoint as Composer 2: Kimi K2.5. Cursor then runs continued pretraining and large-scale reinforcement learning on top, so Composer is not a raw Kimi wrapper, but the base lineage is explicit. Cursor’s own forum announcement repeats the same point: Composer 2.5 builds on Moonshot’s Kimi K2.5 with Cursor’s continued training (Composer 2.5 is now live). That is part of why a Kimi K3 release still matters to me even when I am mostly working in Cursor: the open base that trained Composer 2.5 just got a much larger successor.
When I would reach for Kimi K3
Long-horizon coding and research agents where swarm parallelism cuts wall time
Cost-sensitive production loops that still need frontier-class coding
Teams that want an open-weight path after July 27 for self-host or private deploy
Builders willing to re-test membership latency after the K2.5-era capacity pain
When I would still pick Fable 5 or GPT-5.6 Sol
Absolute top composite reasoning and knowledge-work boards
Stacks already standardized on Anthropic or OpenAI toolchains, eval harnesses, and compliance
Cases where a closed model’s policy and fallback behavior is part of the product contract
Workflows that cannot tolerate product-side queue or capacity slowdowns
Takeaway
Kimi K3 is the first open 3T-class model that sits within shouting distance of Claude Fable 5 and GPT-5.6 Sol on independent intelligence rankings, while leading several agentic coding and frontend preference boards at roughly half the closed flagship token cost. Agent Swarm is the practical differentiator for multi-step shipping work, not the parameter count alone.
If you are evaluating models for AI features that have to pay for themselves in a product, try the same test I use for every release: one real multi-file task with tools, constraints, and a deadline. Leaderboards help you shortlist. Delivery decides.
If you want help shipping an AI feature or production web app with the right model for the job, start on the contact page.
Cursor and SpaceXAI released Grok 4.5 on July 8, 2026. I am writing this the next day, on the same portfolio where I just used that model to ship a real feature: the MDX blog section you are reading now.
What shipped
According to the Cursor forum announcement, Grok 4.5 is Cursor’s most capable model so far, and the first one trained jointly with SpaceXAI for more than software engineering alone. It is aimed at long-running work that needs creative tool use: coding, data work, research, and other computer tasks.
Highlights from the release:
Trained with SpaceXAI on a mixture-of-experts base, then continued on Cursor workflow data
Reinforcement learning on hard problems designed to break weaker models
Effort levels (high, medium, low) so you can match compute to the task
Available across Cursor desktop, web, iOS, CLI, and SDK
EU availability is still rolling out in the coming weeks
The community thread on r/cursor is where a lot of early reactions landed after the drop.
How the benchmarks look
Cursor published a comparison chart across Terminal-Bench, SWE-Bench Multilingual, DeepSWE, and SWE-Bench Pro. Here is that chart:
Reading the highlighted Grok 4.5 column:
Benchmark
Grok 4.5
Notes
Terminal-Bench 2.1
83.3%
Nearly tied with GPT-5.5 (83.4%), ahead of Opus 4.8 (78.9%)
SWE-Bench Multilingual
78.0%
Ahead of GPT-5.5 (77.8%) and Composer 2.5 (71.6%)
DeepSWE 1.0
62.0% (high)
Ahead of Opus 4.8 max (55.8%)
SWE-Bench Pro
64.7% (high)
Competitive mid-pack; Fable 5 leads this row
Benchmarks are not the whole story. What mattered for me was whether the model could stay on a multi-step Astro portfolio task without losing the IDE theme, SEO rules, and routing constraints.
How I used Grok 4.5 to build this blog
I asked Cursor (running Grok 4.5) to plan an Astro blog that stayed inside this IDE chrome, not a separate marketing layout. The model researched Astro content collections, then we locked MDX so posts could embed components like callouts.
What it shipped in this repo:
Content collections under src/content/blog with Zod frontmatter
Routes at /blog and /blog/[slug] wired into the existing RouteId SPA
Explorer blog/ folder, tab labels, and pet tips
BlogPosting schema plus llms.txt links so AI crawlers can find posts
Seed posts, then this release note as the first “real” entry
The hard part was not “make a markdown page.” It was keeping client-side IDE navigation working while still prerendering MDX for SEO. Grok 4.5 proposed content collections for metadata, then build-time HTML templates so post bodies stay available when you switch posts without rebooting the shell.
What it cost on Cursor Pro
I ran this work on Cursor Pro with the included $20 API usage bucket. By the time the blog feature and these posts were done, I had used about 6% of that quota (the dashboard sat in the single-digit range when I captured it).
Almost all of the agent work was grok-4.5-high. The usage log for that session looks like this:
Call it about 18 million tokens of grok-4.5-high, with a little room for rounding and any rows that scrolled off the screenshot. A few Composer rows appear in the same window, but the blog build itself was Grok 4.5 high.
What I want from a coding model now
For agency and product work, I care less about a single leaderboard win and more about:
Holding architecture constraints across a long session
Atomic commits that match a plan
Fixing empty content stores and routing edge cases without rewriting the feature
Writing outcome-led copy that still fits the site voice
That is the bar Grok 4.5 cleared on this feature. If you are evaluating models inside Cursor, try a task that spans UI, content, and SEO in one pass. That is closer to shipping than a toy prompt.
If you want help shipping an AI-assisted product feature of your own, start on the contact page.
Most stalled projects share the same pattern: unclear scope, too many tools, and no owner for launch day. A production web app needs a short path from decision to deploy.
Decide the outcome first
Write one sentence for what success looks like. More leads, faster onboarding, or a store that can take payments without manual work. That sentence drives stack choices and what you cut.
Prefer a stack you can operate
Pick tools your team can host, monitor, and change. A boring stack that ships weekly beats a clever one that only the original author understands.
Plan for handoff and uptime
Document env vars, deploy steps, and who gets paged. Agencies win when the client can run the product after the build, not when the demo looks impressive for a week.
If you want help turning a brief into a live app, start on the contact page.
Business owners do not need another chatbot demo. They need features that show up in the numbers: more booked calls, fewer missed follow-ups, faster handoffs between sales and support.
Start from the bottleneck
Map one painful step in your funnel. Missed inbound calls, slow quote turnaround, or support tickets that repeat the same answer. That bottleneck is where AI earns its keep.
Ship a thin slice first
A useful first release is often a single workflow: summarize a call, draft a follow-up, or flag a risk in a transcript. Keep the UI boring. Put the intelligence where the work already happens.
Measure before you expand
Track time saved, conversion lift, or error rate. Expand only after the thin slice proves value. That is how AI projects stay funded and how agencies keep clients.
When you are ready to scope a production feature, get in touch.
portfolio / README.md
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Terminal
Sonny Nabong
AI Full Stack Developer
I build AI-powered web apps that help businesses win more revenue, save time, and launch faster.
Negros Occidental, Philippines
I help business owners and agencies turn ideas into production web apps and AI features that drive growth: more leads, clearer customer insights, and faster launches. 16+ years shipping for clients worldwide from the Philippines. Recent builds include AI call analysis that surfaces missed revenue and e-commerce stores doing five-figure monthly sales.
How I help
-Help agencies find revenue hidden in client calls with AI transcription and analysis on Next.js and LangChain, so teams get follow-ups without replaying every recording (System Launchpad).
-Track how your brand shows up in ChatGPT and other AI answers with a Next.js dashboard and scheduled Supabase jobs, so you stay visible where customers now search (Brandvisible AI).
-Build and grow WooCommerce stores that earn five-figure monthly revenue, with custom plugins for shipping, email, and checkout so the store keeps selling without constant developer work (Pendragon Peptides).
-Ship marketing sites that SEO teams can edit in Sanity without waiting on a developer, with a fast Astro frontend so new pages go live as static HTML (myJobManager).
Is Sonny Nabong an AI full stack developer in the Philippines?
Yes. Sonny Nabong is an AI full stack developer based in Negros Occidental, Philippines, with 16+ years building production web apps and AI-powered features for clients worldwide.
Can Sonny build AI tools for my business?
Yes. Sonny builds production AI features for business owners: call analysis that surfaces missed revenue, brand monitoring across AI search platforms, and custom web apps that automate work. Recent shipped products include System Launchpad and Brandvisible AI.
What business problems has Sonny solved for clients?
Agencies use his call-analysis SaaS to find revenue hidden in client conversations. E-commerce brands rely on his WooCommerce work, including a UK store doing five-figure monthly sales. Other clients needed faster lead handoffs to CRM, automated job listings, and reliable sites that stay up under real traffic.
Can Sonny help agencies or e-commerce brands?
Yes. For agencies, he has built AI call transcription and analysis tools, internal workflow apps, and CRM integrations. For e-commerce, he has delivered WooCommerce stores, checkout flows, and ongoing conversion and performance work for brands selling online in competitive markets.
Does Sonny work remotely from the Philippines?
Yes. Sonny works with clients remotely from Negros Occidental, Philippines (UTC+8). He has experience with agencies, SaaS startups, and freelance clients worldwide.
What types of projects does Sonny build?
AI SaaS platforms, agency tools with AI workflows, WordPress and WooCommerce, API and CRM integrations, and performance-focused web applications, from prototype to production deployment. Earlier agency work included launching and maintaining hundreds of CMS sites; current work focuses on AI-powered apps and production web builds.
What does working together look like?
You share your goal and constraints; Sonny scopes the build, ships in clear milestones, and owns the stack from database and APIs through deployment. Engagements are typically freelance or contract, remote, with updates over email or your preferred channel. Start at the contact page to discuss your project.
What AI tools does Sonny use in development?
Sonny uses Cursor with Grok and Claude Code for AI-assisted development, plus LangChain, OpenRouter, and OpenAI APIs for production AI features such as agents, RAG, and call analysis in shipped products like Brandvisible AI and System Launchpad.
What is Sonny's full stack?
Sonny owns the work from the screens customers see through launch and uptime. Frontend: React, Next.js, Astro, TypeScript, Tailwind CSS. Backend and data: Sanity, Supabase, PHP, Node.js patterns. AI: LangChain, OpenRouter, MCP, Grok, Claude, OpenAI. Deployment: Vercel, Docker, Linux, Cloudflare, CI/CD.
How can I contact Sonny Nabong?
Visit https://sonnynabong.dev/contact for email and social links, or connect on LinkedIn and OnlineJobs.ph. Resume is available from the homepage.
