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Qwen3-VL - The most powerful visual language model launched by Ali Tongyi

Qwen3-VL is the most powerful visual language model in the Qwen series launched by Alibaba Tongyi, possessing outstanding multimodal capabilities. The model can understand plain text, images, and videos, and supports long context, spatial awareness, code generation, and other functions...

What is Qwen3-VL?

Qwen3-VL is the most powerful visual language model in the Qwen series launched by Alibaba Tongyi, possessing outstanding multimodal capabilities. The model can understand plain text, images, and videos, and supports features such as long context, spatial awareness, and code generation. Qwen3-VL-235B-A22B is the flagship model in this series, including Instruct and Thinking versions. The Instruct version performs excellently in visual perception tasks, while the Thinking version achieves leading levels in multimodal reasoning. Qwen3-VL can interact with interfaces like a human, perform complex reasoning, generate creative copy, and convert design diagrams 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 leader among current multimodal models.

Qwen3-VL-30B-A3B is a newly open-source multimodal AI model that requires only 3 billion activation parameters. It rivals or even surpasses GPT-5-Mini and Claude4-Sonnet in multiple fields, including STEM, visual question answering, OCR, video understanding, and agent tasks. An FP8 version of the model has been released, and it is also available on Qwen Chat for free trial.

The latest release from the Alitongyi Qianwen team is the 4B and 8B versions of the Qwen3-VL series. These versions offer Instruct and Thinking functionalities, respectively. The reduced model size significantly lowers VRAM usage, allowing developers to deploy and run models on a wider range of hardware devices, thus lowering the barrier to entry. All core functionalities of the Qwen3-VL are fully retained; no functionality has been compromised due to the smaller size.

  • Qwen3-VL-8B InstructIt achieved state-of-the-art (SOTA) 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 InstructWith 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 ThinkingIt achieved state-of-the-art (SOTA) scores in 23 authoritative benchmark tests, including MathVision, MMStar, HallusionBench, MM-MT-Bench, and CountBench, surpassing Gemini 2.5 Flash Lite, high-version GPT-5 Nano, and other top-tier open-source models of the same level.
  • Qwen3-VL-4B ThinkingIt also demonstrates the ability to "defeat large targets with small ones" and excels in multimodal performance.

Main functions of Qwen3-VL

  • Visual interaction and task executionQwen3-VL can operate computer and mobile phone interfaces, recognize graphical user interface (GUI) elements, understand button functions, invoke tools, and execute tasks. It performs exceptionally well in benchmark tests such as OS World, significantly improving performance in fine-grained perception tasks through tool invocation.
  • Powerful plain text processing capabilitiesFrom the initial pre-training stage, it integrates collaborative training of text and visual modalities to continuously enhance text processing capabilities. Its performance on pure text tasks is comparable to the Qwen3-235B-A22B-2507 flagship pure text model.
  • Visual programming abilitySupports generating code based on images or videos.
  • Spatial perception and reasoning2D positioning has been upgraded from absolute coordinates to relative coordinates, supporting the determination of object orientation, viewpoint changes and occlusion relationships, and enabling 3D positioning.
  • Long context and long video understandingThe entire model series natively supports a context length of 256K tokens, expandable to 1 million tokens. The model can input complete data, remember the entire process, and retrieve data accurately, supporting video positioning down to the second level.
  • Multimodal Reasoning and ThinkingThe Thinking model focuses on optimizing STEM and mathematical reasoning abilities. When faced with specialized subject-specific problems, the model can capture details, analyze cause and effect, and provide logical and well-founded answers.
  • A comprehensive upgrade in visual perception and recognitionQwen3-VL can recognize a wider range of object categories, from celebrities, anime characters, products, landmarks to plants and animals, meeting the "identification of everything" needs in daily life and professional fields.
  • Multilingual OCR and Complex Scene RecognitionThe 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 with complex lighting, blurriness, and tilt, and the recognition accuracy for rare characters, ancient texts, and professional terms has been significantly improved. The ability to understand ultra-long documents and restore fine structures has been further enhanced.

Technical Principles of Qwen3-VL

  • Multimodal fusion:By combining visual (images, videos) and linguistic (text) modalities, deep fusion of vision and language is achieved through hybrid modality pre-training.
  • Architecture Design:Based on native dynamic resolution design, combined with MRoPE-Interleave technology, time, height and width information are interleaved to improve the ability to understand long videos.By introducing DeepStack technology and integrating multi-layered features of ViT, we can improve the ability to capture visual details and the accuracy of image-text alignment.
  • Visual feature tokenization:Tokenize the visual features of different layers of ViT to retain rich visual information from the bottom to the top and improve visual understanding capabilities.
  • Timestamp alignment mechanism:Based on the "timestamp-video frame" interleaved input format, fine-grained alignment of frame-level time information with visual content is achieved, improving the accuracy of video semantic perception and time positioning.

Qwen3-VL's project address

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

Application scenarios of Qwen3-VL

  • Automated office and task executionThe model operates on computer and mobile phone interfaces, automatically completing tasks such as opening applications and filling out forms, thus improving office efficiency.
  • Visual programming assistanceGenerate webpage code based on design drafts to help developers quickly implement front-end development and improve development efficiency.
  • Education and Learning GuidanceIt excels in answering STEM questions, providing students with detailed problem-solving strategies and answers to aid their learning.
  • Creative content generationIt generates text, stories, etc. based on images or video content, providing creators with inspiration and materials to help with creative writing.
  • Complex document processingThe model can parse long documents and multi-page files, extract key information, and help users quickly obtain the content they need.
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