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Qwen3 VL 235B A22B Thinking

Qwen3 VL 235B A22B Thinking is the reasoning-specialized edition of Alibaba's Qwen3-VL vision-language model, combining a multimodal context of 131.1K tokens with extended chain-of-thought traces for STEM reasoning, mathematical problem solving, and compositional visual analysis.

Vision (Image)ReasoningTool UseFile Input
index.ts
import { streamText } from 'ai'


const result = streamText({
  model: 'alibaba/qwen3-vl-thinking',
  prompt: 'Why is the sky blue?'
})
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Frequently Asked Questions

  • What makes Qwen3 VL 235B A22B Thinking different from Qwen3-VL-Instruct?

    The Thinking variant is trained to produce extended chain-of-thought reasoning traces before its final answer. This improves accuracy on complex, multi-step visual problems but increases output token count and response time compared to the Instruct variant.

  • What kinds of visual STEM tasks benefit most from the thinking mode?

    Problems that require reading numerical values from diagrams, applying formulas based on geometric relationships, interpreting multi-axis scientific charts, or reasoning about causality across multiple images benefit the most from step-by-step visual reasoning.

  • Does the model support the same modalities as the Instruct variant?

    Yes. Both variants accept interleaved text, images, and video within a context window of 131.1K tokens. The difference is in the reasoning depth of the response, not the supported input types.

  • How does DeepStack improve reasoning accuracy on visual inputs?

    DeepStack fuses feature maps from multiple Vision Transformer depth levels, combining coarse and fine-grained visual representations, so the language model has richer input when constructing a reasoning chain. This is especially valuable for tasks requiring precise spatial measurement or small-detail recognition within an image.

  • Can the Thinking variant handle long video inputs that require temporal reasoning?

    Yes. Text-based temporal alignment grounds the model's understanding of when events occur in a video using explicit timestamp markers. Combined with the multimodal context window of 131.1K tokens, the model can reason about event sequences across extended video without losing temporal reference.

  • What benchmarks has this model been evaluated on?

    The Qwen3-VL family reports strong benchmark scores on MMMU, MathVista, MathVision, and MMBench (the 235B-A22B model scored 89.3/88.9 on MMBench and 79.2 on RealWorldQA). Specific thinking-variant scores should be verified against Qwen's published technical report at https://arxiv.org/abs/2511.21631.

  • How should I set timeouts for this model in production?

    Thinking-mode completions for complex visual reasoning problems can generate thousands of reasoning tokens before the final answer. Set your HTTP and streaming timeouts to accommodate generation times that may be several times longer than a comparable direct-answer request.