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MULTIMODAL REASONING • CURATED • UPDATED JAN 31, 2026

Qwen 2.5-VL

The Open Vision-Reasoner: SOTA Multimodal Performance

Qwen 2.5-VL is Alibaba's state-of-the-art open-weight multimodal model, designed to bridge the gap between open source and proprietary vision-language models. It features advanced 'NaViVi' (Native Dynamic Resolution) architecture, allowing it to process images of any resolution and videos of any length with extreme precision. It excels at complex visual reasoning, document understanding (OCR), and real-time video analysis, matching or exceeding GPT-4o in many multimodal benchmarks while remaining fully open for the community to build upon.

Pricing Free
Platforms web, api, local
API Yes
Open Source Yes
Modalities Multimodal Reasoning, LLMs
Best For Best for Open Vision Reasoning
Date Added 2026-01-31

We added Qwen 2.5-VL to the Open Frontier movement because it is currently the highest-performing open-weight vision model. It proves that open source can lead in multimodal reasoning, especially for tasks requiring high-resolution OCR and long-form video understanding.

Try Qwen 2.5-VL for free on the Qwen official demo site or Hugging Face Spaces. For developers, download the weights from Hugging Face and run them locally using vLLM or Ollama. API access is available through providers like DashScope and OpenRouter.

1 Use the 72B model for maximum reasoning depth and the 7B model for real-time speed
2 Leverage the dynamic resolution by providing high-quality images for dense OCR tasks
3 Provide timestamps when asking questions about long videos to get more precise answers
4 Combine with tools like LangChain to build visual agents that can navigate UIs
5 Check the Hugging Face community for quantized versions to run on consumer GPUs
Website Hugging Face

Qwen 2.5-VL GitHub

Access the source code, training details, and deployment guides.

Local Deployment Guide

How to run Qwen 2.5-VL on your own hardware with vLLM.

Claude Fable 5 NotebookLM Claude Opus 5 GPT-5.6 Sol Kimi K3

Complex Document Digitization

Extracting structured data from multi-page PDFs with complex tables and charts.

STEPS:
  1. Upload the document images or PDF pages
  2. Ask: 'Extract all table data into a JSON format'
  3. Review the high-precision OCR output

Visual UI Automation

Using the model to 'see' a website or app UI and describe the steps to complete a task.

STEPS:
  1. Provide a screenshot of the UI
  2. Ask: 'Where should I click to change the notification settings?'
  3. Get the exact coordinates and visual description of the element
Free Completely free
📚

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Q

Is Qwen 2.5-VL free?

A

Yes, Qwen 2.5-VL is completely free to use.

Q

Does Qwen 2.5-VL have an API?

A

Yes, Qwen 2.5-VL offers an API for programmatic integration.

Q

What is Qwen 2.5-VL best for?

A

Qwen 2.5-VL is best for Open Vision Reasoning. Qwen 2. We added Qwen 2.5-VL to the Open Frontier movement because it is currently the highest-performing open-weight vision model. It proves that open source can lead in multimodal reasoning, especially for tasks requiring high-resolution OCR and long-form video understanding.

Q

What platforms does Qwen 2.5-VL support?

A

Qwen 2.5-VL supports web, api, local.

Q

Is Qwen 2.5-VL open source?

A

Yes, Qwen 2.5-VL is open source. You can access the source code on GitHub at https://github.com/QwenLM/Qwen2.5-VL.

Q

How do I get started with Qwen 2.5-VL?

A

Try Qwen 2.5-VL for free on the Qwen official demo site or Hugging Face Spaces. For developers, download the weights from Hugging Face and run them locally using vLLM or Ollama. API access is available through providers like DashScope and OpenRouter.

Q

How do I use Qwen 2.5-VL?

A

Qwen 2.5-VL is a large language model for text generation, analysis, and conversation. Access through the web interface. Enter prompts or questions to get responses. It excels at native dynamic resolution: processes images without resizing or quality loss.

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