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Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is Alibaba's multimodal vision-language model supporting interleaved text, images, and video over a native context of 262.1K tokens, with architectural improvements in spatial-temporal modeling and agentic GUI interaction.

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


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

  • What modalities does Qwen3 VL 235B A22B Instruct accept?

    The model accepts interleaved sequences of text, images, and video frames within a single context window of up to 262.1K tokens.

  • How does DeepStack improve vision-language alignment?

    DeepStack fuses feature maps from multiple depth levels of the Vision Transformer, shallow layers capture low-level detail while deeper layers encode abstract semantics. Combining these gives the language model richer visual grounding information than single-layer ViT representations.

  • What is the difference between MRoPE and standard positional encoding for video?

    Enhanced interleaved MRoPE assigns distinct positional axes to the temporal (time), height, and width dimensions of video inputs, giving the model an explicit spatial-temporal coordinate system. This improves its ability to reason about where and when events occur within a video sequence.

  • Can this model perform GUI automation tasks?

    Yes. The model is trained to parse GUI screenshots, identify interactive elements (buttons, forms, navigation), and plan multi-step actions for PC or mobile application automation.

  • What OCR languages and conditions are supported?

    The model covers OCR in 32 languages and has been evaluated for robustness in low-light, blurred, and tilted-text conditions.

  • How does Qwen3 VL 235B A22B Instruct differ from Qwen3-VL-Thinking?

    The Instruct variant is designed for direct instruction following and is generally faster and more cost-effective. The Thinking variant adds extended step-by-step reasoning traces optimized for complex STEM and compositional visual reasoning problems.

  • Is the context window of 262.1K tokens shared across text, image, and video tokens together?

    Yes. The limit of 262.1K tokens applies to the combined sequence of text tokens and visual tokens (image patches, video frames encoded as tokens) in an interleaved context.