Moonshot AI released Kimi K3 on July 16, 2026 — a 2.8-trillion-parameter open-weight model whose self-reported benchmarks beat Claude Opus 4.8 and GPT-5.5 on most tasks, trailing only Claude Fable 5 and GPT-5.6 Sol. Kimi K3 is available through the kimi.com website and API immediately, and Moonshot AI, a Chinese lab, has promised to publish the model’s weights for download by July 27, 2026.
Kimi K3 is Moonshot AI’s third frontier model, following Kimi K2.6, and the largest open-weight model released to date at 2.8 trillion parameters — ahead of the previous open-weight leader, DeepSeek V4 Pro, at 1.6 trillion. The model carries a 1-million-token context window and prices API access at $3 per million input tokens and $15 per million output tokens, the same cost tier as GPT-5.6 Sol. Moonshot AI describes Kimi K3 as its “most capable model to date,” according to a launch summary by Simon Willison.
What the Kimi K3 Benchmarks Show
The Kimi K3 benchmarks place it ahead of every other open-weight model on reasoning and agentic tasks, and behind only two proprietary frontier models. Kimi K3 scored 93.5% on GPQA Diamond — the strongest open-weight result recorded on that benchmark at release — and reached a 1547 Elo on Artificial Analysis’s private long-horizon knowledge-work evaluation, behind only Claude Fable 5. The verified scores across 5 published benchmarks are listed below.
| Benchmark | Kimi K3 Score | What It Measures |
|---|---|---|
| GPQA Diamond | 93.5% | Graduate-level science reasoning |
| Terminal-Bench 2.1 | 88.3% | Command-line task completion |
| BrowseComp | 91.2% | Autonomous web-browsing agents |
| MCP Atlas | 84.2% | Model Context Protocol tool use |
| Long-horizon knowledge work (Elo) | 1547 | Multi-step professional tasks |
On the Artificial Analysis composite indexes, Kimi K3 records a 57.11 Intelligence Index, a 76.24 Coding Index, and a 50.07 Agentic Index. The model outputs 62 tokens per second with a 1.99-second time-to-first-token, and completes a standard agentic task at a cost of $0.94 — similar to GPT-5.6 Sol, according to Artificial Analysis figures cited by Simon Willison. Moonshot AI reports these results itself; no independent lab has reproduced the full set.
Why Kimi K3 Matters for Businesses Building AI Agents
Kimi K3’s agentic benchmarks make it a credible option for companies building AI agents on top of a hosted model. Its BrowseComp score of 91.2% measures autonomous web-browsing performance, and its MCP Atlas score of 84.2% measures how reliably the model uses external tools through the Model Context Protocol — the two capabilities that determine whether an agent can complete real multi-step work rather than answer a single prompt. At $3 and $15 per million tokens, Kimi K3 undercuts the frontier price ceiling while matching GPT-5.6 Sol on cost per task, which gives agent builders a lower-priced tier to evaluate against the best AI coding assistants for autonomous development.
How Kimi K3 Compares to Other Open-Weight Models
Kimi K3 takes the open-weight parameter crown at 2.8 trillion parameters, ahead of DeepSeek V4 Pro at 1.6 trillion, and is the first open model from a Chinese lab to challenge United States frontier labs on agentic and reasoning benchmarks at the same time. Moonshot AI competes directly with OpenAI and Anthropic for API market share, and Kimi K3’s GPQA Diamond result of 93.5% is the strongest open-weight score on that benchmark. Buyers weighing these numbers should read the self-reported scores against independent evaluation, as explained in our guide to how to read AI coding benchmarks.
Kimi K3 does not beat Claude Fable 5 or GPT-5.6 Sol overall on Moonshot AI’s own numbers — it trails both. The “open weight” label also means the weights are scheduled for download on July 27, 2026, not that the model is fully open source or available for self-hosting today.
- Chinese AI Models Are Running US Enterprise Workloads — how open-weight models from Chinese labs entered production stacks
- GLM-5.2: Zhipu AI’s 1M-Context Open-Source Model — the release that set the current open-source context-length bar
- Tencent HY3: A 295B Open-Source MoE Model — another Chinese open-weight entrant competing on cost
Chinese open-weight models are now genuinely competitive with US frontier labs, and Kimi K3 is the clearest proof yet. The number that matters for businesses is not the GPQA headline — it is the $0.94 cost per task at agentic scores within reach of the frontier, which makes Kimi K3 worth a serious pilot for any team building on AI agents. The bigger story arrives July 27: if Moonshot AI ships the weights as promised, a 2.8-trillion-parameter model becomes something a company can host and fine-tune on its own data, not just rent through an API. Treat the self-reported benchmarks as a starting point until an independent lab reproduces them.
Related Coverage
- Claude Sonnet 5 Agentic Model Launch — the frontier line Kimi K3 measures itself against
- 5 Agentic AI Security Risks Every Team Must Know in 2026 — what to check before deploying any agent model in production
Frequently Asked Questions
What is Kimi K3?
Kimi K3 is a 2.8-trillion-parameter open-weight AI model released by Moonshot AI, a Chinese lab, on July 16, 2026. It is available through the kimi.com website and API now, with downloadable weights promised by July 27, 2026, and carries a 1-million-token context window.
Does Kimi K3 beat Claude Fable 5?
No. On Moonshot AI’s own self-reported benchmarks, Kimi K3 trails both Claude Fable 5 and GPT-5.6 Sol. It beats Claude Opus 4.8 and GPT-5.5 on most tasks and leads all open-weight models, but it does not top the frontier overall.
How much does Kimi K3 cost?
Kimi K3 costs $3 per million input tokens and $15 per million output tokens through Moonshot AI’s API — the same price tier as GPT-5.6 Sol. Artificial Analysis measured its cost per agentic task at $0.94.
Is Kimi K3 open source?
Not fully. Moonshot AI has promised to publish Kimi K3’s trained weights for download by July 27, 2026, which makes it open-weight rather than fully open source. As of the July 16 launch, the model is accessible only through the website and API.
