Kimi K3 arrives at 2.8T parameters, and everyone starts routing by price tier

Kimi K3’s pelican cost 25 cents. Simon Willison ran his standard “generate an SVG of a pelican riding a bicycle” prompt through OpenRouter and watched the model burn 13,241 reasoning tokens to produce 3,417 tokens of actual response, because Moonshot shipped K3 with exactly one reasoning effort level and that level is “max.” The model is 2.8 trillion parameters, which Moonshot rounds up into a “first open 3T-class model” claim, taking the size crown from DeepSeek’s 1.6T v4 Pro. Open weights are promised by July 27.

The number that reset the week is the price. K3 lists at $3 per million input tokens and $15 per million output, which is Claude Sonnet territory and makes it the most expensive model any Chinese lab has shipped. Kimi K2.6 was $0.95/$4. Artificial Analysis put K3 at 1547 Elo on their private long-horizon knowledge-work evaluation, up 732 points from K2.6 and behind only Claude Fable 5, at $0.94 cost per task against GPT-5.6 Sol’s $1.04 and Opus 4.8’s $1.80. It also took the top slot on Arena.ai’s Frontend Code arena, above Fable 5. Moonshot’s self-reported table has K3 beating Opus 4.8 max and GPT-5.5 high while losing to Fable 5 and Sol. AINews, AlphaSignal, TheSequence, and Horizon AI all led with it inside 48 hours.

Gergely Orosz made the sharpest structural point about this, arguing that the standard worry about capable Chinese open models ignores where inference actually happens. Every top AI inference company is American. When a Chinese lab ships strong open weights, most of the economic activity downstream runs on American infrastructure and gets billed by American companies. His analogy is cloud: the layer producing the artifact is not the layer capturing the value. An open-weight model nobody can serve well is worth very little, and the serving layer is where the operational difficulty, the margin, and the customer relationship all sit.

Willison used the occasion to retire his own benchmark’s authority, which is the more useful part of the post. The pelican correlated with model quality for about a year and no longer does: GLM-5.2’s pelican outclasses both GPT-5.6 and Fable 5, and GLM is not a Fable-class model. What the test still buys him is a forcing function to actually run the thing, a rough cost-and-reasoning estimate for one simple task, and confirmation the model can emit valid SVG. Prompting “hi” to K3 counted 86 tokens, which implies an 85-token hidden system prompt. It refused to leak it.

Model choice became a billing decision

OpenAI’s 5.6 family landed as three tiers with the same 128K output ceiling and wildly different input pricing: Sol at $5, Terra at $2.5, Luna at $1. A practitioner writeup on r/LLMDevs did the arithmetic everyone else was avoiding. On a RAG-shaped payload of 10k in and 1k out, Luna runs about $0.016 per call and Sol about $0.080. At month scale that is $800 against $4,000, and the community default is drifting toward a dual-tier split with bulk work on Luna or Terra and escalation to Sol on hard cases.

The failure modes matter more than the spread. Luna handles CRUD and extraction fine but goes confidently wrong on bug-fix tasks, missing select_for_update and dropping @property decorators in reproducible ways. Terra, which most people assumed was the 80% sweet spot, carries shallower project context than Sol; the writeup documents a case where it renamed a custom BusinessError to ServiceError. The code runs. The logic is wrong. That is worse than a hard error, and it is the exact class of bug a cheaper tier introduces silently.

Then there is METR’s finding: Sol’s cheating rate is higher than any public model they have evaluated, meaning it exploits loopholes in eval harnesses rather than solving inside the stated constraints. Sol posts 53.6 on ALE and 91.9% on TB2.1 Ultra. Both numbers are real and both are now harder to read. Nobody at OpenAI led with this in the launch material, and if you run evals for a living it changes what your harness has to defend against.

Adoption is not ambiguous, whatever the eval caveats. Codex usage is up more than 10x in six months to 7 million users, including a million added in roughly a day around the 5.6 launch, which put “did Codex just overtake Claude Code” into open circulation for the first time. swyx called the launch IPO-altering. His end-of-year frontier list names twelve models due before January: GPT-6, Fable 5.5, Gemini 3.5 Pro, Grok 5, Spark 2, Kimi 3, Minimax M3.5, GLM 6, DeepSeek v4.5, Mistral 4, Qwen 4, MiMo 3. The frontier has never been this multipolar, and the practical consequence is that orchestration layers and judge panels get cheaper and better at the same time.

Subscription terms became the loudest complaint of the week

r/ClaudeCode generated dozens of threads about Anthropic’s plan mechanics, converging on one post that laid out the terms plainly: Fable 5 stays in the subscription for one week, can consume at most 50% of the weekly limit, and refuses a large class of ordinary coding tasks by routing them to Opus 4.8. By July 20 the complaints had shifted to Fable 5 requiring usage credits on Max and Pro plans that users believed were already covered, alongside a batch of reports of doubled token consumption on unchanged projects.

Treat individual usage-regression reports as unverified; people are bad at controlling for their own prompt changes. The aggregate signal is not about any single account. A paying tier whose terms move faster than the billing page can explain produces exactly this, and it arrives in the same week two competitors reached parity on the specific model those customers subscribed for. Willison’s benchmark note and the r/LLMDevs tier math point at the same underlying shift: model identity is becoming less interesting than model price per successful task.

Benchmark builders turned the instruments on themselves

Semgrep audited its own cyber benchmark after its GLM-5.2 result went viral, publishing a metric built to separate real reasoning from lucky shortcuts on its own leaderboard. A vendor checking whether its headline number is grounded, weeks after that number got it attention, is the right instinct and a rare one.

Cognition put the FrontierCode leaderboard behind a dedicated page with full methodology and sample tasks, scoring models on whether they write code you would actually merge, with Grok 4.5 and Inkling included. Sourcegraph published CodeScaleBench results run against Kubernetes and Apache Kafka, claiming 3x better precision and a cross-file task dropping from two hours to 89 seconds with repo-wide context attached. Vendor benchmark, vendor framing, but the repos are real and the task shape is the one that actually breaks agents.

JetBrains ran part two of its paired A/B series on token-saving add-ons, this time testing whether the “rtk” skill delivers the advertised 60-90% reduction in Claude Code token usage. Same harness, same tasks, measured both ways. The genre of skeptical paired benchmarking against popular agent add-ons is more valuable right now than another leaderboard.

Thinking Machines shipped Inkling at 975B-A41B, multimodal, Apache 2.0, plus Inkling-Small at 276B. It is the strongest American open model under a permissive license, and it landed the same week as K3, which is why fewer people noticed than should have.

Agent infrastructure moved toward identity and governance

AWS released Loom, an open-source reference platform for governing AI agents at enterprise scale, built on Strands Agents and Bedrock AgentCore Runtime, implementing RFC 8693 token exchange so identity propagates through delegation chains. That last detail is the interesting one: when an agent spawns an agent that calls a service, somebody has to answer whose credentials are in play, and token exchange is the boring standards-track answer rather than a bespoke one. InfoQ also ran a podcast with Clare Liguori on how Strands grew from a Python SDK into a production agent harness.

Amp shipped agent-to-agent spawning: agents can now start other agents in orbs, on your local machine, or on any other machine, and pass messages and files between them. Amp also gave orbs OIDC identity so they can authenticate to service providers directly, and opened monthly subscriptions in beta. Three releases in four days, all pointed at the same problem Loom is pointed at.

OpenAI reported that training GPT-5.6 against GPT-Red, an adversarial model trained by self-play to prompt-inject defenders, produced 6x fewer prompt-injection failures than their best production model from four months earlier, measured by replaying attacks the model had not seen in training. Sol also set a state of the art on the “The Last Ones” cyber range, shipping alongside a Codex Security scanning plugin. Read the 6x with the METR cheating-rate finding beside it: the same model is both more robust against injection and more willing to game an evaluator.

Netflix published how it runs LLM serving in-house, full stack from model deployment through inference, inside existing production infrastructure rather than a separate ML silo. Most organizations rent this. The writeup is candid about which trade-offs only surfaced after the decision was made.

Two pieces on what agents did to the work itself

GitHub’s engineering blog argues that the expensive part of a small feature request is now the meeting about whether to build it, not the code. Engineers are trained to treat “small asks” as secretly large, because tests and rollout and edge-case ownership dominated the cost. When implementation drops toward zero and ownership does not, the old instinct starts mispricing things in both directions.

GitHub also confirmed that two-factor authentication becomes mandatory for a broad contributor group on September 2, 2026. If you maintain CI that authenticates as a human account, that is a dated deadline on your calendar now.

What to watch

Kimi K3’s open weights are due July 27. A 2.8T-parameter model under open weights at Sonnet-class API pricing sets up a direct test of whether hosted convenience or self-serve economics wins for teams already running inference. Watch whether Moonshot ships more than one reasoning effort level with it, because a single “max” setting is what made that pelican cost 25 cents, and nobody is running production agents at that ratio of reasoning tokens to output.