> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Meet Qwen3-VL, the most powerful vision-language model in the Qwen series with superior text understanding, visual perception, and agent capabilities.

![Qwen3-VL Logo](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/qwen3vllogo.png)

## Overview

Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.

This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.

Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.

> **[Quick Start](https://mintlify.com/QwenLM/Qwen3-VL/quickstart.md)**
>
> Get started with Qwen3-VL in minutes with a simple inference example

> **[Installation](https://mintlify.com/QwenLM/Qwen3-VL/installation.md)**
>
> Install transformers, qwen-vl-utils, and dependencies

> **[Model Variants](#model-variants)**
>
> Explore different model sizes from 2B to 235B parameters

> **[GitHub Repository](https://github.com/QwenLM/Qwen3-VL)**
>
> View source code, examples, and contribute

## Key Features

> **Visual Agent**
>
> Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.

> **Visual Coding Boost**
>
> Generates Draw\.io/HTML/CSS/JS from images/videos.

> **Advanced Spatial Perception**
>
> Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.

> **Long Context & Video Understanding**
>
> Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.

> **Enhanced Multimodal Reasoning**
>
> Excels in STEM/Math—causal analysis and logical, evidence-based answers.

> **Upgraded Visual Recognition**
>
> Broader, higher-quality pretraining is able to "recognize everything"—celebrities, anime, products, landmarks, flora/fauna, etc.

> **Expanded OCR**
>
> Supports 32 languages (up from 10); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.

> **Text Understanding on par with pure LLMs**
>
> Seamless text–vision fusion for lossless, unified comprehension.

## Model Variants

Qwen3-VL offers a range of model sizes to suit different deployment scenarios:

> **2B Instruct/Thinking**
>
> Lightweight edge deployment

> **4B Instruct/Thinking**
>
> Balanced performance

> **8B Instruct/Thinking**
>
> Strong capabilities

> **32B Instruct/Thinking**
>
> High performance

> **30B-A3B Instruct/Thinking**
>
> MoE architecture

> **235B-A22B Instruct/Thinking**
>
> Flagship model with MoE

> [!NOTE]
>
> All models are available on [Hugging Face](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe) and [ModelScope](https://modelscope.cn/collections/Qwen3-VL-5c7a94c8cb144b).

## Architecture Innovations

![Qwen3-VL Architecture](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/qwen3vl_arc.jpg)

### Core Improvements

**1. Interleaved-MRoPE**

Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.

**2. DeepStack**

Fuses multi‑level ViT features to capture fine‑grained details and sharpen image–text alignment.

**3. Text–Timestamp Alignment**

Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.

## Resources

> **[Research Paper](https://arxiv.org/pdf/2511.21631)**
>
> Read the full technical report

> **[Blog Post](https://qwen.ai/blog?id=99f0335c4ad9ff6153e517418d48535ab6d8afef)**
>
> Latest announcements and insights

> **[Cookbooks](https://github.com/QwenLM/Qwen3-VL/tree/main/cookbooks)**
>
> Explore practical examples and tutorials

> **[Demo](https://huggingface.co/spaces/Qwen/Qwen3-VL-Demo)**
>
> Try the interactive demo

## Next Steps

> **[Run Your First Inference](https://mintlify.com/QwenLM/Qwen3-VL/quickstart.md)**
>
> Follow our quickstart guide to run Qwen3-VL with your first image

> **[Deploy to Production](https://mintlify.com/QwenLM/Qwen3-VL/deployment/overview.md)**
>
> Learn how to deploy Qwen3-VL with vLLM or SGLang


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.