Kimi K2

Moonshot AI's trillion-parameter MoE model series with 32B active parameters and agentic tool use.

Open SourceSelf HostedOffline CapableGPU Required
0.0 (0)

About

Kimi K2 pushed open-weight scale to a trillion parameters: Moonshot AI's MoE series activates 32B parameters per token across 384 experts with 8 selected plus one shared expert, 61 layers, MLA attention, a 160k vocabulary, and a 128K context window. Training stability at that scale came from applying the MuonClip variant of the Muon optimizer. K2-Base serves fine-tuners while K2-Instruct is post-trained for chat and agentic tool use, and the later K2 Thinking release added an open reasoning model that Moonshot reports sustaining 200 to 300 sequential tool calls without human intervention. Weights carry a Modified MIT license: fully commercial, with the sole condition that products exceeding 100 million monthly active users or 20 million US dollars in monthly revenue must prominently display Kimi K2 in their interface. Self-hosting the 1T checkpoint requires a multi-GPU cluster served through vLLM, SGLang, KTransformers, or TensorRT-LLM, so many teams instead use the OpenAI- and Anthropic-compatible API on Moonshot's hosted Kimi platform. It set state-of-the-art open results on agentic and coding benchmarks at release.

Should you use Kimi K2?

Pick it when

Pick this for agentic tool use and coding where long chains of calls matter, either on a multi-GPU cluster via vLLM or SGLang or through Moonshot's OpenAI and Anthropic compatible API, with K2-Base for your own fine-tunes.

Look elsewhere when

Skip it if you need image input or more than 128K context, where Kimi K3 reaches 1M tokens, or if the display clause for very large products is a problem: GLM-5 and DeepSeek-V4 are plain MIT, and GLM-5 is smaller to host.

Alternatives to Kimi K2

  • Kimi K3

    Successor at 2.8T with 1M context and native image input, but roughly 104B active parameters make it costlier to serve per token.

  • GLM-5

    Plain MIT at around 750B with 40B active and a coding focus, smaller to host and with community quantized builds in llama.cpp and Ollama.

  • DeepSeek-V3

    671B MoE with 37B active, smaller to host than 1T, but under the custom DeepSeek License instead of Modified MIT.

  • MiniMax M3

    About 428B with 23B active, 1M context, and native image and video, cheaper to serve but under the MiniMax Community license.

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Details

Price
Freemium
Platform
Hybrid
Difficulty
Advanced (4/5)
License
Modified MIT
Added
Jul 29, 2026

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