开源高效多模态模型，28亿参数实现强推理与长文本理解。
Kimi-VL Technical Report
- 采用专家混合架构，仅激活28亿语言参数实现高效推理。
- 在长视频和文档理解任务上分别取得64.5和35.1高分。
- 支持超分辨率图像输入，适合需要细节感知的视觉任务。
我们提出Kimi-VL，一个高效的开源视觉语言模型（VLM），具备先进的多模态推理、长上下文理解和强大的智能体能力，同时其语言解码器仅激活28亿参数（Kimi-VL-A3B）。Kimi-VL在多项挑战性任务中表现卓越：作为通用多模态模型，在多轮智能体任务（如OSWorld）中表现媲美顶尖模型；在大学级图像与视频理解、OCR、数学推理及多图理解等任务中均展现强大能力。在对比评测中，其性能可与GPT-4o-mini、Qwen2.5-VL-7B、Gemma-3-12B-IT等先进高效模型竞争，并在多个关键领域超越GPT-4o。模型支持128K扩展上下文窗口，在LongVideoBench和MMLongBench-Doc上分别获得64.5和35.1得分。其原生分辨率视觉编码器MoonViT可处理超高分辨率输入，在InfoVQA和ScreenSpot-Pro上分别取得83.2和34.5分，且日常任务计算成本更低。基于Kimi-VL，我们进一步推出长思维变体Kimi-VL-Thinking-2506，通过长链式思维监督微调与强化学习训练，在长期推理任务中表现突出（MMMU 64.0，MMMU-Pro 46.3，MathVision 56.9，MathVista 80.1，VideoMMMU 65.2），并保持良好泛化能力。代码与模型已在GitHub公开：https://github.com/MoonshotAI/Kimi-VL。
原文摘要 · Abstract (English)
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2.8B parameters in its language decoder (Kimi-VL-A3B). Kimi-VL demonstrates strong performance across challenging domains: as a general-purpose VLM, Kimi-VL excels in multi-turn agent tasks (e.g., OSWorld), matching flagship models. Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, OCR, mathematical reasoning, and multi-image understanding. In comparative evaluations, it effectively competes with cutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and Gemma-3-12B-IT, while surpassing GPT-4o in several key domains. Kimi-VL also advances in processing long contexts and perceiving clearly. With a 128K extended context window, Kimi-VL can process diverse long inputs, achieving impressive scores of 64.5 on LongVideoBench and 35.1 on MMLongBench-Doc. Its native-resolution vision encoder, MoonViT, further allows it to see and understand ultra-high-resolution visual inputs, achieving 83.2 on InfoVQA and 34.5 on ScreenSpot-Pro, while maintaining lower computational cost for common tasks. Building upon Kimi-VL, we introduce an advanced long-thinking variant: Kimi-VL-Thinking-2506. Developed through long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL), the latest model exhibits strong long-horizon reasoning capabilities (64.0 on MMMU, 46.3 on MMMU-Pro, 56.9 on MathVision, 80.1 on MathVista, 65.2 on VideoMMMU) while obtaining robust general abilities. Code and models are publicly accessible at https://github.com/MoonshotAI/Kimi-VL.
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