MoonshotAI/Kimi-VL

Health v2 · maintenance only

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1224 stars · 95 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Kimi-VL is an open-source Mixture-of-Experts vision-language model (VLM) with a 2.8B activated parameter language decoder, offering multimodal reasoning, 128K long-context understanding, OCR, and agent capabilities. It includes a long-thinking variant trained with chain-of-thought SFT and reinforcement learning, with weights available on Hugging Face.

Use cases

• run a vision-language model for image and video understanding
• extract text from images with OCR using an open model
• build multimodal agents that see screens and interact with GUIs
• reason over long documents and long videos with a 128K context window
• perform mathematical reasoning over charts and images
• compare high-resolution screenshots and UI elements
• deploy an efficient small VLM locally on GPU

When to choose

• you need an efficient open-weight VLM with strong reasoning and long-context support
• you want multimodal OCR, document, or video comprehension without flagship-model costs
• you need agent capabilities like GUI grounding (ScreenSpot-Pro) in a compact model
• you want MIT-licensed model weights you can fine-tune or self-host

When to avoid

• you need text-only LLM features with no vision input
• you lack GPU resources for inference even at 2.8B activated parameters
• you need a production API with SLA rather than self-managed model weights
• your task requires the very highest accuracy regardless of compute cost

Facets

library · maturity active

machine-learning deep-learning llm-inference ocr computer-vision nlp agent-framework large-language-models computer-vision artificial-intelligence machine-learning python cross-platform vision-language-model mixture-of-experts multimodal long-context chain-of-thought model-weights huggingface natural-language-processing gpu

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markdown · JSON · MCP: product_card(name="MoonshotAI/Kimi-VL")