# Qwen2-VL

> Alibaba's open vision-language family (2B/7B/72B) with dynamic-resolution vision, M-RoPE, and 20-minute video understanding.

- Maker: Alibaba (Qwen)
- Line: Qwen-VL
- Canonical page: https://ai-tldr.dev/models/qwen2-vl/
- Updated: 2026-06-18

## In short

Qwen2-VL is the second-generation vision-language model line from Alibaba's Qwen team, announced on 29 August 2024 with the 2B and 7B sizes, followed by the 72B instruction-tuned model on 19 September 2024 and the technical report (arXiv 2409.12191). You can use it through Hugging Face (Qwen2-VL-72B-Instruct), Hugging Face (Qwen2-VL-7B-Instruct) and Hugging Face (Qwen2-VL-2B-Instruct).

## Facts

| Fact | Value | Source |
| --- | --- | --- |
| Context window | 32,768 tokens (max_position_embeddings) | [1] |
| Licence | Apache 2.0 (Qwen2-VL-2B and 7B); Qwen license / tongyi-qianwen (Qwen2-VL-72B) · open weights | [1] |
| Released | August 2024 | [2] |
| Parameters | 2B, 7B, and 72B (dense) variants | [1] |
| Knowledge cutoff | June 2023 | [1] |

## Sources

1. [Qwen2-VL-72B-Instruct — Hugging Face model card](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct)
2. [Qwen2-VL — official Qwen blog](https://qwenlm.github.io/blog/qwen2-vl/)

## Overview

Qwen2-VL is the second-generation vision-language model line from Alibaba's Qwen team, announced on 29 August 2024 with the 2B and 7B sizes, followed by the 72B instruction-tuned model on 19 September 2024 and the technical report (arXiv 2409.12191). It reads images, multi-page documents, charts, and video, and answers questions about them. The 2B and 7B checkpoints ship under Apache 2.0, while the flagship Qwen2-VL-72B-Instruct is released under Alibaba's own Qwen (tongyi-qianwen) license.

The release's headline ideas are Naive Dynamic Resolution and Multimodal Rotary Position Embedding (M-RoPE). Rather than squashing every image to a fixed size, Qwen2-VL turns an image into a variable number of visual tokens based on its actual resolution, which helps fine-grained tasks like OCR and document parsing. M-RoPE gives the model a single positional scheme that spans text, 2D images, and 3D video, letting Qwen2-VL handle videos longer than 20 minutes for video question-answering.

On standard multimodal benchmarks, Qwen2-VL-72B scores competitively with closed models of its era on document and OCR-heavy tasks: 96.5 on DocVQA, 64.5 on MMMU, 70.5 on MathVista, and 877 on OCRBench. The smaller 2B and 7B models trade accuracy for footprint, making the 2B a practical choice for on-device and edge deployment. The line was later superseded by Qwen2.5-VL (January 2025) and Qwen3-VL, but the Qwen2-VL weights remain freely downloadable from Hugging Face.

## Benchmarks

- DocVQA (test): 96.5% ([source](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct))
- MMMU (val): 64.5% ([source](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct))
- MathVista (testmini): 70.5% ([source](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct))
- RealWorldQA: 77.8% ([source](https://openlm.ai/qwen2-vl/))
- MTVQA: 30.9% ([source](https://openlm.ai/qwen2-vl/))
- TextVQA (val): 85.5% ([source](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct))
- MMBench-EN: 86.5% ([source](https://openlm.ai/qwen2-vl/))
- EgoSchema (video, test): 77.9% ([source](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct))
- MVBench (video): 73.6% ([source](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct))

## Strengths

- Strong document and OCR understanding — Qwen2-VL-72B reaches 96.5 on DocVQA and 877 on OCRBench
- Naive Dynamic Resolution handles arbitrary image sizes without forced downscaling, helping fine-grained text-in-image tasks
- Long video comprehension (20+ minutes) via M-RoPE's 3D temporal positional encoding
- Three sizes (2B/7B/72B) span from edge/on-device to server-grade deployment
- Open weights — 2B and 7B under permissive Apache 2.0, allowing commercial use and fine-tuning
- Multilingual OCR and multilingual visual QA (MTVQA) coverage

## Best for

- Document, form, and invoice parsing where text-in-image accuracy matters
- Multilingual OCR and screenshot/UI understanding
- Chart and diagram question-answering for data extraction
- Video understanding and summarization of clips longer than 20 minutes
- On-device or edge multimodal assistants using the compact 2B checkpoint
- Visual reasoning for mobile, automotive, and robotics agent prototypes

## How to access

- [Hugging Face (Qwen2-VL-72B-Instruct)](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct) — model id `Qwen/Qwen2-VL-72B-Instruct`
- [Hugging Face (Qwen2-VL-7B-Instruct)](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) — model id `Qwen/Qwen2-VL-7B-Instruct`
- [Hugging Face (Qwen2-VL-2B-Instruct)](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) — model id `Qwen/Qwen2-VL-2B-Instruct`

## Qwen-VL versions

- [Qwen3-VL](https://ai-tldr.dev/models/qwen3-vl/) (23 September 2025)
- [Qwen2.5-VL](https://ai-tldr.dev/models/qwen2-5-vl/) (January 2025)
- Qwen2-VL (December 2024) — this page

## FAQ

### When was Qwen2-VL released?

Alibaba's Qwen team announced Qwen2-VL on 29 August 2024 with the 2B and 7B sizes. The instruction-tuned 72B model followed on 19 September 2024, and the technical report (arXiv 2409.12191) was published in September 2024.

### Is Qwen2-VL open source, and what license does it use?

Yes. Qwen2-VL-2B and Qwen2-VL-7B are released under the permissive Apache 2.0 license, which allows commercial use and fine-tuning. The flagship Qwen2-VL-72B-Instruct uses Alibaba's own Qwen (tongyi-qianwen) license rather than Apache 2.0.

### What sizes and context length does Qwen2-VL offer?

Qwen2-VL comes in 2B, 7B, and 72B dense variants. The released checkpoints have a maximum context of 32,768 tokens (max_position_embeddings), and the model can process video longer than 20 minutes thanks to its M-RoPE positional encoding.

### How does Qwen2-VL compare on benchmarks?

The 72B model scores 96.5 on DocVQA, 64.5 on MMMU, 70.5 on MathVista, and 877 on OCRBench, making it especially strong on document and OCR-heavy tasks. It was later superseded by Qwen2.5-VL (January 2025) and Qwen3-VL, which improve document parsing, grounding, and long-video understanding.

## Links

- [Qwen2-VL — official Qwen blog](https://qwenlm.github.io/blog/qwen2-vl/)
- [Qwen2-VL technical report (arXiv 2409.12191)](https://arxiv.org/abs/2409.12191)
- [Qwen2-VL-72B-Instruct — Hugging Face model card](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct)
- [Qwen2-VL-7B-Instruct — Hugging Face model card](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)
- [Qwen2-VL benchmark table — OpenLM.ai](https://openlm.ai/qwen2-vl/)

Source: AI/TLDR LLM registry, https://ai-tldr.dev/models/qwen2-vl/