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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Qwen3-VL has been extensively evaluated on a wide range of benchmarks to demonstrate its capabilities in both visual understanding and text processing. Below are the comprehensive benchmark results for all model variants.

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Visual Tasks Performance

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Large Models (235B & 30B MoE)

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Instruct Models - Visual Tasks

Qwen3-VL Large Instruct Models - Visual Tasks Qwen3-VL-235B-A22B-Instruct and Qwen3-VL-30B-A3B-Instruct demonstrate state-of-the-art performance across visual benchmarks.

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Thinking Models - Visual Tasks

Qwen3-VL Large Thinking Models - Visual Tasks Qwen3-VL-235B-A22B-Thinking and Qwen3-VL-30B-A3B-Thinking show enhanced reasoning capabilities on complex visual tasks.

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MoE Models Comparison

Qwen3-VL-30B-A3B InstructQwen3-VL-30B-A3B Thinking

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Smaller Models (2B-32B Dense)

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2B & 32B Models - Visual Tasks

Qwen3-VL 2B & 32B Instruct - VisualQwen3-VL 2B & 32B Thinking - Visual
Comprehensive comparison of Qwen3-VL-2B and Qwen3-VL-32B models in both Instruct and Thinking editions across visual understanding benchmarks.

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Text-Centric Tasks Performance

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Large Models (235B & 30B MoE)

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Instruct Models - Text Tasks

Qwen3-VL Large Instruct - Text Tasks Qwen3-VL demonstrates text understanding on par with pure LLMs, showing seamless text-vision fusion.

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Thinking Models - Text Tasks

Qwen3-VL Large Thinking - Text Tasks Thinking editions show enhanced performance on reasoning-heavy text tasks.

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MoE Model - Text Tasks

Qwen3-VL-30B-A3B Text Performance

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Smaller Models (4B & 8B Dense)

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4B & 8B Models - Text Tasks

Qwen3-VL 4B & 8B Instruct - TextQwen3-VL 4B & 8B Thinking - Text
Performance comparison of Qwen3-VL-4B and Qwen3-VL-8B models on text-centric benchmarks.

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Key Capabilities

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Visual Understanding

  • Image Recognition: State-of-the-art performance on standard vision benchmarks
  • OCR: Support for 32 languages with robustness to challenging conditions
  • Document Parsing: Advanced layout understanding and structure extraction
  • Object Grounding: Precise 2D and 3D object localization
  • Video Understanding: Long-form video comprehension with temporal reasoning

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Text Processing

  • Pure Text Tasks: Performance comparable to dedicated LLMs
  • Multimodal Reasoning: Seamless integration of visual and textual information
  • STEM/Math: Enhanced reasoning capabilities, especially in Thinking editions
  • Multilingual: Strong performance across multiple languages

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Specialized Capabilities

  • Spatial Understanding: Advanced 3D reasoning and spatial relationships
  • Coding: Visual coding from screenshots to HTML/CSS/JavaScript
  • Agent Tasks: GUI interaction and tool use
  • Long Context: Native 256K tokens, expandable to 1M

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Evaluation Settings

To ensure reproducibility, we provide our official evaluation configuration:

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Inference & Evaluation

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Generation Hyperparameters

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Instruct Models

export greedy='false'
export seed=3407
export top_p=0.8
export top_k=20
export temperature=0.7
export repetition_penalty=1.0
export presence_penalty=1.5
export out_seq_length=32768

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Thinking Models

export greedy='false'
export seed=1234
export top_p=0.95
export top_k=20
export repetition_penalty=1.0
export presence_penalty=0.0
export temperature=0.6
export out_seq_length=40960

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Notes on Evaluation

  • For certain benchmarks, evaluation prompts were slightly modified for better performance
  • Some benchmarks are internally constructed; reproduction code will be released
  • Detailed methodology will be documented in the technical report

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Benchmark Categories

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Visual Tasks Evaluated

  • General vision understanding
  • OCR and document analysis
  • Object detection and grounding
  • Video question answering
  • Spatial reasoning
  • Visual coding
  • Agent tasks (GUI interaction)

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Text-Centric Tasks Evaluated

  • Natural language understanding
  • Mathematical reasoning
  • Code understanding and generation
  • Logical reasoning
  • Common sense reasoning
  • Multilingual comprehension

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Performance Highlights

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Qwen3-VL-235B-A22B

  • Best-in-class performance across most benchmarks
  • State-of-the-art visual reasoning
  • Comparable to pure LLMs on text tasks

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Qwen3-VL-30B-A3B

  • Excellent performance-to-cost ratio
  • MoE architecture for efficient inference
  • Strong across both visual and text tasks

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Qwen3-VL-2B to 32B

  • Scalable performance across model sizes
  • 2B suitable for edge deployment
  • 32B competitive with larger models
  • Thinking editions show consistent improvements

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