Kimi-VL Technical Report
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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Kimi-VL Technical Report
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.
Mixture-of-Experts (MoE) vision-language model (VLM) multimodal reasoning parameter-efficient fine-tuning language decoder multi-turn agent tasks college-level image and video comprehension OCR mathematical reasoning multi-image understanding extended context window LongVideoBench MMLongBench-Doc native-resolution vision encoder MoonViT InfoVQA ScreenSpot-Pro long chain-of-thought (CoT) supervised fine-tuning (SFT) reinforcement learning (RL) MMMU MathVision MathVista