论文信息与摘要（原文）

论文信息与摘要（原文）
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原文标题：Kimi K2: Open Agentic Intelligence arXiv：2507.20534（v1）　方向：第三批检索与智能体：把知识与行动接入推理

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Kimi Team

Abstract

We introduce Kimi K2, a Mixture-of-Experts (MoE) large language model with 32 billion activated parameters and 1 trillion total parameters. We propose the MuonClip optimizer, which improves upon Muon with a novel QK-clip technique to address training instability while enjoying the advanced token efficiency of Muon. Based on MuonClip, K2 was pre-trained on 15.5 trillion tokens with zero loss spike. During post-training, K2 undergoes a multi-stage post-training process, highlighted by a large-scale agentic data synthesis pipeline and a joint reinforcement learning (RL) stage, where the model improves its capabilities through interactions with real and synthetic environments.

Kimi K2 achieves state-of-the-art performance among open-source non-thinking models, with strengths in agentic capabilities. Notably, K2 obtains 66.1 on Tau2-Bench, 76.5 on ACEBench (En), 65.8 on SWE-Bench Verified, and 47.3 on SWE-Bench Multilingual --- surpassing most open and closed-sourced baselines in non-thinking settings. It also exhibits strong capabilities in coding, mathematics, and reasoning tasks, with a score of 53.7 on LiveCodeBench v6, 49.5 on AIME 2025, 75.1 on GPQA-Diamond, and 27.1 on OJBench, all without extended thinking. These results position Kimi K2 as one of the most capable open-source large language models to date, particularly in software engineering and agentic tasks. We release our base and post-trained model checkpoints11 1

https://huggingface.co/moonshotai/Kimi-K2-Instruct to facilitate future research and applications of agentic intelligence.

Technical Report of Kimi K2

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Figure 1: Kimi K2 main results.33 3 All models evaluated above are non-thinking models. For SWE-bench Multilingual, we evaluated only Claude 4 Sonnet because the cost of Claude 4 Opus was prohibitive.

Results of {{call_id}}

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