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Qwen3-VL - Alibaba Tongyi's Most Powerful Vision-Language Model

Author: AI小集 · Original source: AI工具集

What is Qwen3-VL

Qwen3-VL is the most powerful vision-language model in the Qwen series launched by Alibaba Tongyi, featuring outstanding multimodal capabilities. The model can understand plain text, images, and videos, and supports long context, spatial perception, code generation, and other functions. Qwen3-VL-235B-A22B is the flagship model of this series, including two versions: Instruct and Thinking. The Instruct version performs excellently in visual perception tasks, while the Thinking version reaches a leading level in multimodal reasoning. Qwen3-VL can operate interfaces like a human, perform complex reasoning, generate creative copy, and turn design drawings into code. The model supports multilingual OCR and long video understanding, and is widely used in education, development, automation, and other fields, making it a standout among current multimodal models.

Qwen3-VL-30B-A3B is the latest open-source multimodal AI model. With only 3 billion activated parameters, it can match or even surpass GPT-5-Mini and Claude4-Sonnetin multiple fields such as STEM, visual question answering, OCR, video understanding, and agent tasks. The model has released an FP8 version and is simultaneously available on Qwen Chat, supporting free体验.

The latest Alibaba Tongyi Qianwen team has launched the 4B and 8B versions of the Qwen3-VL series. These two versions respectively provide Instruct and Thinking versions. The reduction in model size significantly lowers VRAM usage, enabling developers to deploy and run the models on a wider range of hardware devices and lowering the barrier to use. All core functions of Qwen3-VL are fully retained, with no weakening of functionality due to the reduced size.

  • Qwen3-VL-8B Instruct: Achieved SOTA (state-of-the-art) results in 30 authoritative benchmark tests including MIABench, OCRBench, SUNRGBD, ERQA, VideoMMMU, and ScreenSpot, surpassing top models such as Gemini 2.5 Flash Lite, GPT-5 Nano, and Qwen2.5-VL-72B.
  • Qwen3-VL-4B Instruct: With fewer parameters, it can compete with Gemini 2.5 Flash Lite and GPT-5 Nano in tests such as STEM, VQA, OCR, video understanding, and Agent tasks, demonstrating excellent multimodal performance.
  • Qwen3-VL-8B Thinking: Achieved SOTA results in 23 authoritative benchmark tests including MathVision, MMStar, HallusionBench, MM-MT-Bench, and CountBench, surpassing Gemini 2.5 Flash Lite, the high version of GPT-5 Nano, and other top open-source models of the same level.
  • Qwen3-VL-4B Thinking: Also demonstrates the achievement of "using the small to rival the large," with excellent performance in multimodal capabilities.
Qwen3-VL

Main Functions of Qwen3-VL

  • Visual interaction and task execution: Qwen3-VL can operate computer and mobile phone interfaces, recognize graphical user interface (GUI) elements, understand button functions, call tools, and execute tasks. It performs excellently in benchmarks such as OS World, and through tool calling significantly improves performance on fine-grained perception tasks.
  • Powerful plain text processing capability: From the early stage of pretraining, it integrates collaborative training of text and visual modalities, continuously strengthening text processing capability. Performance on plain text tasks is comparable to the plain text flagship model Qwen3-235B-A22B-2507.
  • Visual programming capability: Supports generating code based on images or videos.
  • Spatial perception and reasoning: 2D localization has been upgraded from absolute coordinates to relative coordinates, supporting judgment of object orientation, viewpoint changes, and occlusion relationships, and enabling 3D localization.
  • Long context and long video understanding: The entire series natively supports a context length of 256K tokens, expandable to 1 million tokens. The model can fully input, remember throughout, and accurately retrieve, and supports precise video localization down to the second level.
  • Multimodal reasoning and thinking: Thinking models focus on optimizing STEM and mathematical reasoning capabilities. When facing professional subject questions, the model can capture details, analyze cause and effect, and provide logical, evidence-based answers.
  • Comprehensively upgraded visual perception and recognition: Qwen3-VL can recognize a richer range of object categories, from celebrities, anime characters, products, and landmarks to animals and plants, meeting the "recognize everything" needs of daily life and professional fields.
  • Multilingual OCR and complex scene recognition: The number of languages supported by OCR has expanded from 10 to 32, covering more countries and regions. It performs stably in real-world shooting scenarios such as complex lighting, blur, and tilt, and the recognition accuracy for rare characters, ancient book characters, and professional terminology has improved significantly. The ability to understand ultra-long documents and restore fine structures has been further enhanced.

Technical Principles of Qwen3-VL

  • Multimodal fusion：Combines visual (image, video) and language (text) modalities, and through mixed-modality pretraining, achieves deep fusion of vision and language.
  • Architecture design：Based on a native dynamic resolution design, combined with MRoPE-Interleave technology, it interleaves the distribution of time, height, and width information, improving the ability to understand long videos.Introduces DeepStack technology to fuse multi-level ViT features, improving the ability to capture visual details and the precision of image-text alignment.
  • Tokenization of visual features：Tokenizes visual features from different layers of ViT, preserving rich visual information from low-level to high-level features, and improving visual understanding capabilities.
  • Timestamp alignment mechanism：Based on a "timestamp-video frame" interleaved input format, it achieves fine-grained alignment between frame-level temporal information and visual content, improving video semantic perception and temporal localization accuracy.

Qwen3-VL Project Address

  • Project official website：https://qwen.ai/blog?id=99f0335c4ad9ff6153e517418d48535ab6d8afef
  • GitHub repository：https://github.com/QwenLM/Qwen3-VL
  • HuggingFace model library：https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe
  • Official website experience：Qwen Chat

Application scenarios of Qwen3-VL

  • Automated office work and task execution: The model operates computer and mobile phone interfaces and automatically completes tasks such as opening applications and filling out forms, improving office efficiency.
  • Visual programming assistance: Generates webpage code based on design drafts, helping developers quickly implement front-end development and improving development efficiency.
  • Education and learning tutoring: It performs excellently in answering STEM subject questions and can provide students with detailed problem-solving approaches and answers to assist learning.
  • Creative content generation: Generates copy, stories, and more based on image or video content, providing creators with inspiration and materials to support creative writing.
  • Complex document processing: The model can parse long documents and multi-page files, extract key information, and make it convenient for users to quickly obtain the content they need.