---
title: Qwen3-VL-235B-A22B-Instruct-FP8 — Vision LLM | ModelsLab
description: Access Qwen3-VL-235B-A22B-Instruct-FP8 for vision-language tasks with 262K context. Deploy via API for image analysis and video reasoning. Start generat...
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/qwen3-vl-235b-a22b-instruct-fp8
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/qwen3-vl-235b-a22b-instruct-fp8
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component: Seo/ModelPage
generated_at: 2026-05-13T10:36:43.217200Z
---

Available now on ModelsLab · Language Model

Qwen3-VL-235B-A22B-Instruct-FP8
Vision Meets Reasoning
---

[Try Qwen3-VL-235B-A22B-Instruct-FP8](/models/together_ai/Qwen-Qwen3-VL-235B-A22B-Instruct-FP8) [API Documentation](https://docs.modelslab.com)

Process Images. Reason Deeply.
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Visual Agent

### Navigate GUIs Autonomously

Recognizes GUI elements, understands functions, invokes tools for task completion.

Spatial Reasoning

### Ground 2D and 3D

Judges object positions, viewpoints, occlusions with precise spatial perception.

Video Analysis

### Handle Long Videos

Supports 262K context for hours-long videos with second-level indexing.

Examples

See what Qwen3-VL-235B-A22B-Instruct-FP8 can create
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Copy any prompt below and try it yourself in the [playground](/models/together_ai/Qwen-Qwen3-VL-235B-A22B-Instruct-FP8).

GUI Task

“Analyze this screenshot of a web app. Identify the login button, describe its position relative to the header, and suggest how to click it using coordinates.”

Spatial Query

“Examine this architectural blueprint image. Determine the relative positions of rooms, detect any occlusions, and provide 3D grounding estimates.”

Video Summary

“Process this 5-minute product demo video. Index key events by second, describe spatial changes in objects, and generate a timeline summary.”

Document OCR

“Extract all text from this scanned technical diagram. Align text with visual elements, reason about diagram logic, and output structured JSON.”

For Developers

A few lines of code.
Vision inference. One call.
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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 Qwen3-VL-235B-A22B-Instruct-FP8
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[Read the docs ](https://docs.modelslab.com)

### What is Qwen3-VL-235B-A22B-Instruct-FP8?

Qwen3-VL-235B-A22B-Instruct-FP8 is a 235B parameter MoE vision-language model with 22B active params in FP8 quantization. It excels in visual reasoning, agent tasks, and long-context video understanding. Context window reaches 262K tokens.

### How does Qwen3-VL-235B-A22B-Instruct-FP8 API perform?

Offers high throughput via FP8 for cost-efficient inference. Providers like DeepInfra deliver 11+ tokens/second output speed. Supports vision input with 16K max output tokens.

### What are key capabilities of qwen3 vl 235b a22b instruct fp8?

Features DeepStack for fine-grained visual details and Interleaved-MRoPE for video reasoning. Handles GUI navigation, visual coding, and multimodal STEM tasks. Recognizes broad visual categories accurately.

### Is Qwen3-VL-235B-A22B-Instruct-FP8 a good alternative?

Serves as strong Qwen3-VL-235B-A22B-Instruct-FP8 alternative for vision LLM needs with MoE efficiency. Competes on benchmarks against top models in coding and math. Open-weight for flexible deployment.

### What context length supports Qwen3-VL-235B-A22B-Instruct-FP8 model?

Native 262K token context, expandable for books and long videos. Enables full recall and precise temporal indexing. Max output is 16K tokens per response.

### Does qwen3 vl 235b a22b instruct fp8 api handle video?

Processes hours-long videos with second-level understanding via enhanced dynamics comprehension. Uses robust positional embeddings for long-horizon reasoning. Ideal for detailed video analysis tasks.

Ready to create?
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Start generating with Qwen3-VL-235B-A22B-Instruct-FP8 on ModelsLab.

[Try Qwen3-VL-235B-A22B-Instruct-FP8](/models/together_ai/Qwen-Qwen3-VL-235B-A22B-Instruct-FP8) [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 - 2026-05-13*