Kimi-VL Technical Report
Authors: name: Kimi Team; name: Angang Du; name: Bohong Yin; name: Bowei Xing; name: Bowen Qu; name: Bowen Wang; name: Cheng Chen; name: Chenlin Zhang; name: Chenzhuang Du; name: Chu Wei; name: Congcong Wang; name: Dehao Zhang; name: Dikang Du; name: Dongliang Wang; name: Enming Yuan; name: Enzhe Lu; name: Fang Li; name: Flood Sung; name: Guangda Wei; name: Guokun Lai; name: Han Zhu; name: Hao Ding; name: Hao Hu; name: Hao Yang; name: Hao Zhang; name: Haoning Wu; name: Haotian Yao; name: Haoyu Lu; name: Heng Wang; name: Hongcheng Gao; name: Huabin Zheng; name: Jiaming Li; name: Jianlin Su; name: Jianzhou Wang; name: Jiaqi Deng; name: Jiezhong Qiu; name: Jin Xie; name: Jinhong Wang; name: Jingyuan Liu; name: Junjie Yan; name: Kun Ouyang; name: Liang Chen; name: Lin Sui; name: Longhui Yu; name: Mengfan Dong; name: Mengnan Dong; name: Nuo Xu; name: Pengyu Cheng; name: Qizheng Gu; name: Runjie Zhou; name: Shaowei Liu; name: Sihan Cao; name: Tao Yu; name: Tianhui Song; name: Tongtong Bai; name: Wei Song; name: Weiran He; name: Weixiao Huang; name: Weixin Xu; name: Xiaokun Yuan; name: Xingcheng Yao; name: Xingzhe Wu; name: Xinxing Zu; name: Xinyu Zhou; name: Xinyuan Wang; name: Y. Charles; name: Yan Zhong; name: Yang Li; name: Yangyang Hu; name: Yanru Chen; name: Yejie Wang; name: Yibo Liu; name: Yibo Miao; name: Yidao Qin; name: Yimin Chen; name: Yiping Bao; name: Yiqin Wang; name: Yongsheng Kang; name: Yuanxin Liu; name: Yulun Du; name: Yuxin Wu; name: Yuzhi Wang; name: Yuzi Yan; name: Zaida Zhou; name: Zhaowei Li; name: Zhejun Jiang; name: Zheng Zhang; name: Zhilin Yang; name: Zhiqi Huang; name: Zihao Huang; name: Zijia Zhao; name: Ziwei Chen
Published: 2025-04-10 06:48:26

Abstract: 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. Developed through long chain-of-thought (CoT) supervised
fine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong
long-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8
on MathVision, and 71.3 on MathVista while maintaining the compact 2.8B
activated LLM parameters, setting a new standard for efficient multimodal
thinking models. Code and models are publicly accessible at
https://github.com/MoonshotAI/Kimi-VL.