Description
Qwen3-VL is a vision-language model series built by the Qwen team at Alibaba Cloud, positioned as the most capable vision-language model the team has released to date. It comes in both Dense and Mixture-of-Experts architectures, in sizes from 2 billion up to 235 billion (with 22 billion active) parameters, and each size ships in two editions: Instruct, for direct responses, and Thinking, tuned for step-by-step reasoning.
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The model targets several specific capabilities beyond general image description. A visual agent mode reads a computer or phone screen, recognizes interface elements, and can invoke tools to complete a task, aimed at GUI automation. A visual coding feature generates Draw.io diagrams, HTML, CSS, or JavaScript directly from an image or video, useful for turning a screenshot or design mockup into working markup. Spatial perception covers both 2D grounding, locating and describing objects in a scene, and 3D grounding for tasks like embodied AI or robotics reasoning, where judging position, viewpoint, and occlusion matters.
For long-form content, Qwen3-VL supports a native 256K-token context window that can be expanded to 1M, with what the team describes as second-level indexing for hour-long video, meaning it can recall and reference specific moments rather than only summarizing. OCR coverage extends to 32 languages, with claimed robustness to low light, blur, and tilted text, along with better handling of rare or ancient characters and long, structured documents. Text-only understanding is built to match dedicated text LLMs, so the model doesn't trade off pure-text performance for its visual capabilities, and the team reports stronger multimodal reasoning on STEM and math tasks that require causal, evidence-based answers.
Underneath, the architecture introduces Interleaved-MRoPE for positional embeddings across time, width, and height, helpful for long video reasoning, DeepStack for fusing multi-level vision transformer features into sharper image-text alignment, and a text-timestamp alignment mechanism for more precise event localization in video.
Qwen3-VL is available through Hugging Face, ModelScope, an interactive demo Space, Qwen Chat for direct use, and a hosted API through Alibaba Cloud's Model Studio, so it can be used without downloading weights. It's released under the Apache 2.0 license, making it usable in commercial projects. The range of sizes and the visual-agent and OCR features make it a fit for developers building multimodal apps, from document processing to GUI-driven agents.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
Qwen3-VL's Core Features
Visual agent capability that reads PC and mobile GUIs, recognizes elements, and invokes tools to complete tasks
Generates Draw.io diagrams, HTML, CSS, and JS code directly from images or video
2D and 3D spatial grounding for judging object position, viewpoint, and occlusion
Native 256K-token context, expandable to 1M, with second-level indexing for hour-long video
OCR across 32 languages, tuned for low light, blur, tilt, and rare or ancient characters
Broader visual recognition covering celebrities, anime characters, products, landmarks, and flora and fauna
Released in Dense and Mixture-of-Experts sizes from 2B to 235B-A22B parameters
Separate Instruct and Thinking (reasoning-enhanced) editions for each size
Getting Started with Qwen3-VL
Chat with it online: Try Qwen3-VL through Qwen Chat or the Hugging Face demo Space
Pick a size: Choose a model from 2B up to 235B-A22B based on your hardware and latency needs
Choose an edition: Use Instruct for direct answers or Thinking for step-by-step reasoning
Download weights: Get the model from Hugging Face or ModelScope for self-hosting
Or call the hosted API: Use Alibaba Cloud's Model Studio API instead of self-hosting
Follow the cookbooks: Use the published cookbooks for GUI-agent, OCR, or video-focused workflows
Qwen3-VL's Use Cases
- GUI agent automation
- Document and scene OCR
- Image-to-code generation
- Long-video understanding
