Kimi K2
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Moonshot AI introduces Kimi K2, a 1T parameter Mixture-of-Experts model (32B active) optimized not just for knowledge tasks but for agentic capabilities, models that act, not just respond. Released as Kimi-K2-Base and Kimi-K2-Instruct, it achieves strong scores across coding, math, and tool-use benchmarks and is open-source for research and deployment.
State-of-the-art in open agentic coding: Kimi K2-Instruct hits 65.8% on SWE-bench Verified (agentic, pass@1) and 47.3% on SWE-bench Multilingual, outperforming open models like DeepSeek and Qwen3, and even rivalling Claude Sonnet 4 in several settings.
Robust statistical reasoning: A full salary analysis workflow showcases its agentic capabilities, loading data, visualizing interactions, conducting ANOVA, and generating a polished, interactive webpage with personalized recommendations, all via tool execution.
MuonClip optimizer: For stable and token-efficient pretraining, Moonshot introduces qk-clip, a fix for exploding attention logits during training with Muon. This technique rescales the query-key projections dynamically and enables stable training on 15.5T tokens.
Agentic training pipeline: Kimi K2’s tool use stems from ACEBench-inspired simulations, rubric-driven, multi-turn tool scenarios with synthetic and real MCP tools. These are filtered by LLM judges and scaled with RL using both verifiable (math, coding) and non-verifiable (writing) reward setups.
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