---
title: Qwen2-VL 72B Instruct — Multimodal Vision Language | Mo...
description: Process images, video, and text with Qwen2-VL 72B. Handle 20+ minute videos, multilingual content, and complex visual reasoning. Try the API today.
url: https://modelslab.com/qwen2-vl-72b-instruct
canonical: https://modelslab.com/qwen2-vl-72b-instruct
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:02:52.107148Z
---

Available now on ModelsLab · Language Model

Qwen2-VL (72B) Instruct
Vision, Video, Reasoning
---

[Try Qwen2-VL (72B) Instruct](/models/qwen/Qwen-Qwen2-VL-72B-Instruct) [API Documentation](https://docs.modelslab.com)

See, Understand, Reason Better
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Extended Context

### Process 20+ Minute Videos

Handle long-form video content for QA, dialogue, and analysis without truncation.

Dynamic Resolution

### Arbitrary Image Resolutions

Process images at any resolution with adaptive token mapping for optimal efficiency.

Multilingual Support

### Global Text Recognition

Extract and understand text in 30+ languages, including European, Asian, and Arabic scripts.

Examples

See what Qwen2-VL (72B) Instruct can create
---

Copy any prompt below and try it yourself in the [playground](/models/qwen/Qwen-Qwen2-VL-72B-Instruct).

Document Analysis

“Analyze this architectural blueprint and extract all dimensions, materials, and structural specifications. Format the output as structured JSON with categories for walls, openings, and load-bearing elements.”

Video Event Detection

“Review this 15-minute surveillance footage and identify all significant events. For each event, provide timestamp, description, and confidence level. Output as a timeline with bounding box coordinates.”

Chart Data Extraction

“Extract all data from this financial chart including axis labels, data points, and trends. Return as CSV format with column headers and numerical values.”

Visual Localization

“Locate all product packaging in this retail shelf image. For each item, provide bounding box coordinates, product name, and shelf position (top, middle, bottom).”

For Developers

A few lines of code.
Multimodal reasoning. Three lines.
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about Qwen2-VL (72B) Instruct
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[Read the docs ](https://docs.modelslab.com)

### What makes Qwen2-VL 72B Instruct different from other vision-language models?

### Can Qwen2-VL 72B Instruct process video input?

### Is the Qwen2-VL 72B Instruct API suitable for production use?

### What languages does Qwen2-VL 72B Instruct support?

### Can this model be used for agentic applications?

Ready to create?
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Start generating with Qwen2-VL (72B) Instruct on ModelsLab.

[Try Qwen2-VL (72B) Instruct](/models/qwen/Qwen-Qwen2-VL-72B-Instruct) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-04-15*