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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Kimi-VL: Mixture-of-Experts Vision-Language Model for Multimodal Reasoning, Long-Context Understanding, and Strong Agent Capabilities
| Date | Stars |
|---|---|
| 2026-07-31 | 1218 |
| 2026-08-03 | 1219 |
| 2026-08-06 | 1219 |
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<div align="center"> <a href="Kimi-VL.pdf">KIMI-VL TECHNICAL REPORT</a> </div> <div align="center"> <a href="https://arxiv.org/abs/2504.07491"><img src="figures/logo.png" height="16" width="16" style="vertical-align:middle"><b> Tech Report</b></a> | <a href="https://huggingface.co/collections/moonshotai/kimi-vl-a3b-67f67b6ac91d3b03d382dd85"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="16" width="16" style="vertical-align:middle"><b> HuggingFace</b> </a> | <a href="https://huggingface.co/spaces/moonshotai/Kimi-VL-A3B-Thinking/">💬<b>Chat with Latest Kimi-VL (2506)</b></a> </div> ## 1. Introduction 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 interaction tasks (e.g.,OSWorld), achieving state-of-the-art results comparable to flagship models. Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, optical character recognition (OCR), mathematical reasoning, multi-image understanding, and etc. 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 specialized domains. Kimi-VL also advances the pareto frontiers of multimodal models in processing long contexts and perceiving clearly: Equipped with a 128K extended context window, Kimi-VL can processes long and diverse 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 with common visual inputs and general tasks. Building on this foundation, 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 parameter footprint, setting a new standard for efficient yet capable multimodal **thinking** models. <i>Besides original model variants, we also provide a new [Kimi-VL-A3B-Thinking-2506](https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking-2506) variant with several new or improved abilities: - It Thinks Smarter while Consuming Less Tokens: The 2506 version reaches better accuracy on multimodal reasoning benchmarks: 56.9 on MathVision (+20.1), 80.1 on MathVista (+8.4), 46.3 on MMMU-Pro (+3.2), 64.0 on MMMU (+2.1), while in average reducing 20% thinking length. - It Sees Clearer with Thinking: Unlike the previous version that specializes on thinking tasks, the 2506 version can also achieve the same or even better ability on general visual perception and understanding, e.g. MMBench-EN-v1.1 (84.4), MMStar (70.4), RealWorldQA (70.0), MMVet (78.4) compared to the original non-thinking version (Kimi-VL-A3B-Instruct). - It Extends to Video Scenarios: The new 2506 version also improves on video reasoning and understanding benchmarks. It sets new state-of-the-art for open-source models on VideoMMMU (65.2), while also retaining good ability on general video understanding (71.9 on Video-MME). - It Extends to Higher Resolution: The new 2506 version supports 3.2 million total pixels in a single image (1792x1792), 4X compared to the original release. This leads to non-trivial improvements on hig
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9
Teo (Timothy) Wu Haoning · Moonshot AI Singapore
7
Zaida Zhou
6
Ikko Eltociear Ashimine · Japan
1
Liang Chen · UniPat AI · China
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weedge · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:30cefb49e3b75c9a, desc:multimodal, desc:vision-language, desc:vision language model