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 / blog/index.mdx
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Blog
blog/index.mdx
Notes on shipping AI features and production web apps for business owners and agencies, by Sonny Nabong.
