Kimi K3: Moonshot AI's 2.8T-Parameter Open-Weight Frontier Model

Moonshot AI released Kimi K3, an open-weight 2.8-trillion-parameter Mixture-of-Experts model with 104B activated parameters, built on the new Kimi Delta Attention architecture and featuring a 1,048,576-token context window and native multimodal vision. The company claims it is the world's first open 3T-class model, with self-reported benchmarks showing it leads on agentic tool-use and vision tasks but trails Claude Fable 5 and GPT-5.6 Sol on some reasoning evals. Weights are available on Hugging Face under the custom Kimi K3 License.

Kimi K3: Moonshot AI's 2.8T-Parameter Open-Weight Frontier Model Kimi K3 is Moonshot AI's open-weight successor to Kimi K2 — a 2.8T-parameter, 104B-activated MoE model built on a new Kimi Delta Attention KDA architecture, with native multimodal vision, a 1,048,576-token context window, and published benchmarks against Claude and GPT-class frontier models. - ⭐ 8176 - Python - Kimi K3 License - Updated 2026-08-08 1M Context Window LLM 2026: Real Tests https://dibi8.com/resources/llm-frameworks/1m-context-window-llm-2026-real-test/ • Open Interpreter: A Codex Fork That Emulates Claude Code and Kimi’s Harness https://dibi8.com/resources/llm-frameworks/openinterpreter-low-cost-model-coding-agent-2026/ Kimi K3 — from github.com/MoonshotAI/Kimi-K3 What Is Kimi K3? what-is-kimi-k3 Kimi K3 is Moonshot AI’s newest open-weight model, and per the README, their most capable to date: a 2.8-trillion-parameter Mixture-of-Experts model with 104B activated parameters per token, built on a new Kimi Delta Attention KDA and Attention Residuals AttnRes architecture. Moonshot AI describes it as “the world’s first open 3T-class model” — native multimodal text, image, video-frame understanding , a 1,048,576-token context window , and released with full weights under a custom Kimi K3 License . 🔗 GitHub : https://github.com/MoonshotAI/Kimi-K3 https://github.com/MoonshotAI/Kimi-K3 🤗 Weights : huggingface.co/moonshotai/Kimi-K3 https://huggingface.co/moonshotai/Kimi-K3 📄 Tech report : linked from the repo as k3 tech report.pdf At 8,100+ GitHub stars , created July 27, 2026 and pushed as recently as August 6, 2026. Architecture: What Changed From K2 architecture-what-changed-from-k2 | Spec | Value | |---|---| | Total parameters | 2.8T | | Activated parameters | 104B | | Layers | 93 1 dense + 92 MoE | | Attention composition | 69 KDA + 24 Gated MLA | | Attention heads | 96 hidden dim 7168 | | Experts | 896 total, 16 selected/token, 2 shared | | Vocabulary | 160K tokens | | Context length | 1,048,576 tokens | | Vision encoder | MoonViT-V2 401M params | | Quantization | Native MXFP4 weights / MXFP8 activations quantization-aware trained | Per Moonshot AI, the Stable LatentMoE framework activating 16 of 896 experts yields “an approximate 2.5× improvement in overall scaling efficiency” over K2. The quantization detail is worth flagging separately: MXFP4/MXFP8 is trained in from the SFT stage onward , not bolted on as a post-hoc compression step — the stated goal being broad hardware compatibility without a separate quantization pass degrading quality. Benchmark Highlights Self-Reported, Max Effort benchmark-highlights-self-reported-max-effort Moonshot AI’s README publishes a large comparison table against Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, GPT-5.5, and GLM-5.2. A representative slice — K3 doesn’t sweep every category, and the pattern shifts by benchmark type: | Benchmark | Kimi K3 | Best of the rest | |---|---|---| | BrowseComp agentic web | 91.2 | GPT-5.6 Sol 90.4 | | MCPMark-Verified MCP tool use | 94.5 | GPT-5.6 Sol / GPT-5.5 tied 92.9 | | Terminal-Bench 2.1 | 88.3 | GPT-5.6 Sol 88.8 | | GPQA Diamond reasoning | 93.5 | GPT-5.6 Sol 94.1 | | HLE-Full | 43.5 | Claude Fable 5 53.3 | | CritPt physics reasoning | 23.4 | GPT-5.6 Sol 32.3 | | Video-MME w/ subtitles | 90.0 | GPT-5.6 Sol 89.5 | | OmniDocBench document vision | 91.1 | Claude Fable 5 89.8 | | Harvey Lab-AA legal | 94.6 | Claude Fable 5 93.6 | Read this carefully : these are Moonshot AI’s own numbers from their tech report, not third-party reproductions. K3 leads on agentic tool-use and several vision/document benchmarks, but trails Claude Fable 5 and GPT-5.6 Sol on some of the hardest pure-reasoning evals HLE-Full, CritPt . Treat it as “strong, benchmark-dependent,” not “best at everything.” Deployment and Model Usage deployment-and-model-usage Recommended inference engines, per the README: — published recipes at recipes.vllm.ai vLLM https://github.com/vllm-project/vllm — cookbook at docs.sglang.io SGLang https://github.com/sgl-project/sglang TokenSpeed — recipes at lightseek.org A hosted, OpenAI/Anthropic-compatible API is available at platform.kimi.ai model name kimi-k3 . Thinking is always on. Reasoning effort is set via a reasoning effort field "low" / "high" / "max" , default "max" , returned as a separate reasoning content field. The one gotcha worth flagging for anyone integrating this: K3 was trained in preserved thinking history mode , meaning multi-turn calls must pass the complete prior assistant message back — reasoning content and tool calls included, not just the final content string — or the model loses the thread on follow-up turns. For an agent harness, Moonshot AI points to their own Kimi Code CLI — run it in a terminal and switch to K3 with the /model command. Licensing: What Triggers a Paid Agreement licensing-what-triggers-a-paid-agreement The Kimi K3 License custom, same shape as K2’s is permissive by default — free to use, modify, fine-tune, and redistribute — with two revenue-gated conditions: “Model as a Service” threshold : if you plus affiliates give third parties API-level control over K3’s inputs/parameters/fine-tuning and your combined revenue exceeds $20M USD over any 12 consecutive months , you need a separate commercial agreement with Moonshot AI. Attribution at scale : if K3 powers a product with 100M+ monthly active users or $20M+/month revenue , “Kimi K3” must be prominently displayed in that product’s UI. Both conditions are waived for internal use and for end-user products that merely embed K3’s capabilities without exposing model-level control to third parties. License license Kimi K3 License custom, permissive with revenue-based commercial terms — see LICENSE https://github.com/MoonshotAI/Kimi-K3/blob/main/LICENSE .