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
Organization: Moonshot AI

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.