国产大模型Kimi K3：2.8万亿参数，逼近GPT-5
国产大模型Kimi K3来了，参数规模2.8万亿，每次只激活1040亿参数，就能在编程、推理、视觉等任务上超越大多数开源和闭源模型，仅次于Claude和GPT-5的最新版本。它用了一种叫“Delta Attention”的技术，让信息在长文本（100万token）和深层网络中流动更顺畅，还通过“Stable LatentMoE”把专家路由做得更稳。训练时混合了通用、智能体、编程等强化学习，让模型能组合推理、执行长任务。虽然还没到最强，但已经开源权重，意味着你可以自己部署或微调。这不是你明天就能直接用的工具，但它是国产大模型逼近世界前沿的信号——下次选模型时，Kimi K3可能是个值得关注的选项。
📄 原文摘要(英文)
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is built on Kimi Delta Attention and Attention Residuals, which improve information flow across sequence length and model depth. Together with Stable LatentMoE, which effectively activates 16 of 896 routed experts per token, and refined training and data recipes, these advances yield an approximately 2.5x improvement in overall scaling efficiency over Kimi K2. Post-training highlights reinforcement learning across general, agentic, and coding domains and multiple reasoning-effort levels, enabling compositional generalization and robust long-horizon execution. At 2.8T scale, Kimi K3 is supported by infrastructure advances in multiple areas: algorithm-system co-design for KDA, perfectly balanced expert-parallel training with efficient memory management, million-token agentic RL with persistent rollout and sandbox states, and deployment innovations. Extensive evaluations show that Kimi K3 achieves frontier-level performance across long-horizon coding, agentic, knowledge, reasoning, and vision tasks. While its overall performance still trails the most powerful proprietary models, namely Claude Fable 5 and GPT-5.6 Sol, Kimi K3 consistently outperforms other open and proprietary models evaluated in our suite. We release the full Kimi K3 model weights to facilitate future research and accelerate the broader deployment and adoption of frontier intelligence.