Kimi K3
Kimi K3 is Moonshot AI's July 2026 flagship multimodal model for long-horizon coding, knowledge work, and reasoning. It is available through Kimi products and the Kimi API under the kimi-k3 model ID, and Moonshot AI has released the full moonshotai/Kimi-K3 checkpoint under the Kimi K3 License.
Kimi K3 matters because it combines a 2.8-trillion-parameter sparse Mixture-of-Experts architecture, native vision, a 1-million-token context window, a callable API, and an official open-weight checkpoint. The release expands evaluation and deployment options, but the 1.56 TB checkpoint and Moonshot AI's 64-accelerator supernode recommendation keep self-hosting far beyond a typical local workstation.
Moonshot AI's launch blog provides the model architecture, availability, context window, API model ID, pricing, and benchmark methodology. The official Hugging Face model card and repository confirm the full checkpoint, Kimi K3 License, technical report, 104B activated parameters, MXFP4 weights, and vLLM, SGLang, and TokenSpeed deployment paths. Kimi Code documentation separately defines the `k3` and `k3-256k` product-facing model IDs. Reddit discussions remain useful for questions about local deployment scale, provider availability, cost, and comparisons, but they are not used as authority for model specifications or benchmark claims.
- Run long-horizon coding and software-engineering tasks through Kimi Code or the Kimi API.
- Analyze long documents, repositories, images, and mixed knowledge-work inputs within a 1M-token context.
- Build multimodal agents that combine visual understanding, reasoning, and tool-driven workflows.
- Compare frontier-model quality and task cost using the same harness, tests, and review criteria.
Moonshot AI describes Kimi K3 as a 2.8-trillion-parameter model with native vision and a 1-million-token context window. The architecture combines Kimi Delta Attention, Attention Residuals, and a Stable LatentMoE design that activates 16 of 896 experts. Moonshot positions it for long-horizon coding, knowledge work, and reasoning.
- Architecture: 2.8T total parameters with 16 of 896 experts activated in the sparse MoE design.
- Modalities and context: native vision with a 1-million-token context window.
- Reasoning effort: the current Kimi API supports low, high, and max, with max as the default.
Kimi K3 is available on Kimi.com, Kimi Work, Kimi Code, and the Kimi API, where the `kimi-k3` model ID costs $0.30 per million cache-hit input tokens, $3 per million cache-miss input tokens, and $15 per million output tokens. Moonshot AI now also publishes the full `moonshotai/Kimi-K3` checkpoint on Hugging Face. The 1.56 TB repository contains 96 safetensor shards, and the model card recommends vLLM, SGLang, or TokenSpeed on a supernode with at least 64 accelerators.
Kimi Code exposes `k3` and `k3-256k` as separate choices. The standard `k3` route uses automatic context management and accepts images and video. The `k3-256k` route fixes the context window at 256K tokens, accepts images but not video, and Kimi says it consumes roughly half the quota while producing the same result as `k3` for tasks that fit inside 256K. Kimi Code documents low, high, and max reasoning effort with high as its default; that product default should not be confused with the separate `kimi-k3` API guide, which documents max as the default.
The Kimi K3 License permits use, modification, distribution, deployment, fine-tuning, and commercial products, subject to its conditions. Large model-as-a-service businesses and very large commercial products have additional agreement or attribution requirements, so teams should review the license rather than treating “open weight” as an unrestricted label. Moonshot AI also publishes separate MIT-licensed infrastructure projects such as FlashKDA for Kimi Delta Attention kernels and MoonEP for balanced expert-parallel communication.
The launch table compares Kimi K3 with Claude Fable 5, GPT-5.6 Sol, Claude Opus 4.8, GPT-5.5, and GLM-5.2 across coding, agentic, knowledge, and vision tasks. These are useful vendor-reported launch results, but several rows use different agent harnesses, model settings, internal evaluations, or cited third-party results. Compare Kimi K3 on your own tasks before choosing it as a default model.
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Kimi K3 FAQ
Common questions about Kimi K3.
Is Kimi K3 available now?+
Yes. Moonshot AI says Kimi K3 is available on Kimi.com, Kimi Work, Kimi Code, and the Kimi API. The API model ID is `kimi-k3`, but availability through another provider should be verified on that provider's current model list.
Is Kimi K3 open weight?+
Yes. Moonshot AI has published the full `moonshotai/Kimi-K3` checkpoint under the Kimi K3 License. The Hugging Face repository is about 1.56 TB and contains 96 safetensor shards, so “open weight” does not mean it is practical to run on an ordinary local machine.
How much does the Kimi K3 API cost?+
Moonshot AI lists Kimi K3 at $0.30 per million cache-hit input tokens, $3 per million cache-miss input tokens, and $15 per million output tokens on its international API. The Chinese platform lists separate yuan pricing, and third-party providers may charge different rates.
Can Kimi K3 run locally?+
Kimi K3 can now be self-hosted from the official checkpoint, but Moonshot AI recommends a supernode with at least 64 accelerators. The model card lists vLLM, SGLang, and TokenSpeed deployment paths; teams still need to validate hardware capacity, runtime compatibility, and the Kimi K3 License before production use.
What is Kimi Code k3-256k?+
`k3-256k` is Kimi Code's fixed-256K-context option. Moonshot AI says it produces the same result as `k3` for tasks that fit within 256K while consuming roughly half the quota. It accepts images but not video, and it is a Kimi Code model choice rather than a replacement for the `kimi-k3` API identifier.