Kimi-VL-A3B-Instruct

// Overview

Kimi-VL-A3B-Instruct is the instruction-tuned vision-language model in Moonshot AI's (Kimi) Kimi-VL line, released in April 2025 under an MIT license with fully open weights. It pairs a native-resolution visual encoder called MoonViT with a sparse Mixture-of-Experts language decoder: although the model totals 16B parameters, it activates only about 2.8B per token by routing through 8 of 384 experts, giving it the efficiency of a roughly 3B-class dense model while retaining the capacity of a much larger one.

The model handles text, single and multiple images, video sequences, and long documents within a 128K-token context window, making it well suited to OCR, college-level image and video comprehension, mathematical reasoning, and multi-image understanding. Moonshot AI positions Kimi-VL-A3B-Instruct as a compact, open alternative that competes with efficient closed and open VLMs such as GPT-4o-mini, Qwen2.5-VL-7B and Gemma-3-12B-IT, while surpassing GPT-4o in several specialized domains like document and OCR tasks.

Kimi-VL-A3B-Instruct is distributed primarily as open weights on Hugging Face for self-hosting rather than as a paid hosted API. A separate reasoning-focused sibling, Kimi-VL-A3B-Thinking, adds chain-of-thought and reinforcement-learning training on top of the same A3B backbone.

// Benchmarks

1. MMMU (Val)57%
2. MathVista68.7%
3. MMBench-EN-v1.183.1%
4. AI2D84.9%
5. InfoVQA83.2%
6. OCRBench86.7% (867/1000)
7. ScreenSpot-V292.8%
8. ScreenSpot-Pro34.5%
9. LongVideoBench64.5%
10. MMLongBench-Doc35.1%
11. VideoMMMU52.6%

Scores on a 0–100 scale (25-point gridlines); higher is better. Each benchmark links to its published source.

// Strengths

• Strong document understanding and OCR for its size — 86.7% on OCRBench and 83.2 on InfoVQA
• Efficient MoE design: ~2.8B active parameters keep inference cheap while 16B total capacity preserves quality
• 128K context window enables long documents, multi-image sets and video clips in a single prompt
• Capable GUI/agent grounding — 92.8 on ScreenSpot-V2 for on-screen element localization
• Fully open weights under a permissive MIT license, so it can be self-hosted and fine-tuned freely
• Solid math and college-level multimodal reasoning (68.7 MathVista, 57.0 MMMU) despite its small active footprint

// Best for

• OCR and document parsing across PDFs, scans and screenshots
• Visual question answering over charts, diagrams and infographics
• Long-document and multi-image analysis using the 128K context
• Video comprehension and summarization
• GUI agents and on-screen element grounding for automation
• Self-hosted multimodal applications where open weights and low inference cost matter

// Kimi-VL — every version

The full lineage of the Kimi-VL line, newest first. Every version has its own page — click any to compare specs, benchmarks and pricing.