Qwen/Qwen3-VL-235B-A22B-Thinking

Qwen3-VL-235B-A22B-Thinking is the most powerful vision-language model in the Qwen series. With 235B total parameters (22B active per token) and deep reasoning capabilities in thinking mode, it excels in multimodal STEM/math reasoning, visual agent tasks, GUI automation, spatial perception, long video comprehension, and multilingual OCR across 32 languages.

Alibaba Cloud Vision 256K Tokens (up to 1M)
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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "Qwen/Qwen3-VL-235B-A22B-Thinking",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What is in this image? Describe the main elements."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
          }
        }
      ]
    }
  ],
  "max_tokens": 4096,
  "temperature": 0.7,
  "stream": true,
  "top_p": 0.9
}'

Technical Specifications

Model Architecture & Performance

Variant Thinking
Model Size 235B params (22B active)
Context Length 256K Tokens (up to 1M)
Quantization bf16 / FP8
Tokens/Second 60
Architecture MoE Transformer with ViT visual encoder, Interleaved-MRoPE, DeepStack feature fusion
Precision bf16 / FP8
License Apache 2.0
Release Date September 2025
Developers Alibaba Cloud (QwenLM)

Pricing

Pay-per-use, no commitments

Input Tokens $0.0004/1K Tokens
Output Tokens $0.004/1K Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.7 Controls randomness in output.
max_tokens number 4096 Maximum tokens to generate.
top_p number 0.9 Controls nucleus sampling.

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
Thinking mode for deep reasoning
Native 256K context (expandable to 1M)
DeepStack multi-level ViT feature fusion
Interleaved-MRoPE for video temporal reasoning
Rivals Gemini 2.5 Pro on perception benchmarks
Very high GPU memory requirements
Slower due to thinking mode overhead
Not suitable for real-time low-latency tasks

Use cases

Recommended applications for this model

Visual STEM/Math reasoning
GUI automation & visual agents
Multimodal coding from images/video
Long video understanding
Multilingual OCR (32 languages)
3D grounding & spatial reasoning

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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