title: Kimi-VL Technical Report
summary: 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.
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paper_title: Kimi-VL Technical Report
paper_summary: 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.
paper_ai_summary: Kimi-VL, an efficient Mixture-of-Experts vision-language model, excels in multimodal reasoning, long-context understanding, and diverse vision-language tasks, achieving competitive performance with reduced computational cost.
paper_ai_keywords: Mixture-of-Experts (MoE) vision-language model (VLM) multi-turn agent tasks OSWorld college-level image and video comprehension OCR mathematical reasoning multi-image understanding GPT-4o-mini Qwen2.5-VL-7B Gemma-3-12B-IT long contexts LongVideoBench MMLongBench-Doc MoonViT InfoVQA ScreenSpot-Pro chain-of-thought (CoT) supervised fine-tuning (SFT) reinforcement learning (RL) MMMU MathVision MathVista