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Qwen3-VL

From Alibaba Cloud

Alibaba’s Most Powerful Open-Source Multimodal Vision-Language Model – Superior Visual Understanding, Long Context, and Reasoning Across Text, Images, and Video
Tool Release Date

23 Sep 2025

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Pricing Model

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About This AI

Qwen3-VL is the flagship multimodal vision-language model series from Alibaba’s Qwen team, representing a major advancement in integrated vision and language intelligence.

Released in September 2025, it includes dense and MoE variants (from 2B to 235B parameters) with Instruct and reasoning-enhanced Thinking editions for flexible deployment.

Key upgrades feature enhanced MRope for spatial-temporal modeling, DeepStack for multi-level ViT feature fusion, and text-based timestamp alignment for precise video understanding.

It supports native long context (256K tokens, expandable to 1M), handles books, hours-long videos with full recall and second-level indexing, excels in multilingual OCR, fine-grained scene interpretation, visual reasoning, document parsing, creative writing, and complex multimodal tasks.

The series achieves SOTA open-source performance in perception and reasoning benchmarks, rivaling or surpassing closed models in areas like VQA, image captioning, document understanding, and agent interaction.

Available under Apache 2.0 license on Hugging Face and ModelScope, with FP8 quantized versions for efficiency, it is accessible via Transformers, vLLM, and ModelScope for local or cloud inference.

Ideal for developers, researchers, and enterprises needing high-performance vision-language AI for OCR, visual QA, video analysis, multimodal retrieval, and agentic applications without proprietary restrictions.

Key Features

  1. Multimodal unification: Seamless processing of text, images, and video in a single model with strong vision-language alignment
  2. Long context support: Native 256K tokens expandable to 1M for handling extensive documents or long videos with full recall
  3. Advanced video understanding: Text timestamp alignment for precise temporal grounding and second-level indexing of hours-long content
  4. Enhanced visual reasoning: Superior performance in complex scene interpretation, fine-grained details, and spatial-temporal modeling via MRope and DeepStack
  5. Multilingual OCR excellence: High-accuracy document parsing and text extraction across languages
  6. Reasoning-enhanced editions: Thinking variants for deeper multimodal reasoning and STEM tasks
  7. MoE and dense variants: Scalable architectures from edge (2B) to cloud (235B) for different compute needs
  8. Agent interaction capabilities: Stronger support for tool use and autonomous multimodal workflows
  9. FP8 quantization: Efficient inference versions with near-identical performance to BF16
  10. Open-source accessibility: Full weights, code, and examples on Hugging Face and ModelScope under Apache 2.0

Price Plans

  1. Free ($0): Full open-source access to all model weights, code, and inference examples under Apache 2.0; no usage fees for local or self-hosted deployment
  2. Cloud API (Paid via Alibaba Cloud): Optional hosted inference through Alibaba Model Studio with per-token pricing for production-scale use

Pros

  1. Top-tier open-source multimodal performance: Leads in visual perception, reasoning, and video tasks among open models
  2. Exceptional long-context handling: Processes books or long videos with precise recall and indexing
  3. Strong multilingual capabilities: Excellent OCR and understanding across diverse languages
  4. Flexible deployment: Dense/MoE variants, Instruct/Thinking editions, and quantized options for edge to cloud
  5. Completely open-source: Apache 2.0 license enables full local use, fine-tuning, and commercial applications
  6. Rapid community adoption: High downloads and integration support via Transformers and vLLM
  7. Balanced text and vision strength: Maintains excellent pure text performance alongside advanced multimodal features

Cons

  1. High compute requirements: Larger variants (e.g., 235B) need substantial GPU resources for inference
  2. Setup for local use: Requires building from source Transformers or using vLLM for optimal performance
  3. Recent release: Some advanced features like full video support still evolving in community feedback
  4. No hosted chat interface: Primarily for developers; use via API or local deployment
  5. Potential quantization trade-offs: FP8 versions may have minor precision loss in edge cases
  6. Limited user metrics: Adoption numbers not widely reported yet due to recency
  7. Focus on high-end tasks: May be overkill for simple image captioning or basic OCR

Use Cases

  1. Advanced OCR and document understanding: Extract and reason over text in complex layouts, multilingual documents, or scanned PDFs
  2. Visual question answering: Answer detailed questions about images or videos with high accuracy
  3. Video analysis and summarization: Process long videos with temporal grounding and key event extraction
  4. Multimodal retrieval: Power search systems combining text and visual queries
  5. Agentic applications: Enable AI agents to perceive and interact with visual environments
  6. Creative and scientific visualization: Generate descriptions or reason about diagrams, charts, and scientific images
  7. Accessibility tools: Describe images for visually impaired users or automate alt-text generation

Target Audience

  1. AI developers and researchers: Building or fine-tuning multimodal models locally
  2. Computer vision engineers: Needing SOTA open-source VL for OCR, VQA, or video tasks
  3. Enterprises with document-heavy workflows: Automating processing of images, PDFs, and videos
  4. Multimodal application creators: Developing agents, retrieval systems, or visual analytics tools
  5. Open-source enthusiasts: Experimenting with large-scale vision-language models
  6. Alibaba Cloud users: Leveraging hosted versions for production

How To Use

  1. Install Transformers: Build from source or use latest Hugging Face Transformers for Qwen3-VL support
  2. Load model: Use AutoModelForImageTextToText.from_pretrained('Qwen/Qwen3-VL-8B-Instruct') with device_map='auto'
  3. Prepare inputs: Combine text messages with image URLs or paths using qwen_vl_utils.process_vision_info
  4. Generate output: Call model.generate with processor-applied inputs for responses
  5. Use vLLM for speed: Run inference with vLLM backend for faster throughput on GPU
  6. Handle long context: Pass extended text/video frames within 256K limit
  7. Try demos: Access Hugging Face Spaces or ModelScope demos for no-code testing

How we rated Qwen3-VL

  • Performance: 4.8/5
  • Accuracy: 4.7/5
  • Features: 4.9/5
  • Cost-Efficiency: 5.0/5
  • Ease of Use: 4.5/5
  • Customization: 4.8/5
  • Data Privacy: 5.0/5
  • Support: 4.6/5
  • Integration: 4.7/5
  • Overall Score: 4.8/5

Qwen3-VL integration with other tools

  1. Hugging Face Transformers: Native support for loading, processing, and inference with Qwen3-VL models
  2. vLLM: High-throughput serving and inference backend for faster local or server deployment
  3. ModelScope: Alibaba's platform for easy model download, demos, and cloud inference
  4. GitHub Repository: Full code, cookbooks, and community contributions for custom use
  5. Local GPU/Cloud: Runs on consumer hardware or Alibaba Cloud Model Studio for scaled access

Best prompts optimised for Qwen3-VL

  1. Describe this image in detail, including all visible objects, their positions, colors, and any text. Then answer: What is happening in the scene? [attach image]
  2. Perform OCR on this document image and extract all text exactly as shown, preserving layout and formatting. [attach scanned PDF page]
  3. Analyze this chart: What trends do the data show? Provide numerical values and insights. [attach graph image]
  4. Answer the question based on the video frames: What events occur between timestamp 1:30 and 2:00? [attach video frames or description]
  5. Generate a detailed caption for this photo suitable for alt-text accessibility, including emotions and context. [attach photo]
Qwen3-VL sets a new benchmark for open-source multimodal AI with exceptional visual reasoning, long-context video handling, and multilingual OCR capabilities. Fully free under Apache 2.0, it rivals proprietary models in perception and reasoning while enabling local deployment. Ideal for developers needing powerful vision-language tools without restrictions.

FAQs

  • What is Qwen3-VL?

    Qwen3-VL is Alibaba’s Qwen team’s most advanced open-source multimodal vision-language model series, featuring dense and MoE variants with strong visual perception, reasoning, long context, and video understanding.

  • When was Qwen3-VL released?

    The Qwen3-VL series was officially released in September 2025, with major updates and variants continuing through late 2025 and early 2026.

  • Is Qwen3-VL free to use?

    Yes, it is completely open-source under Apache 2.0 license with full weights and code available on Hugging Face and ModelScope for local or self-hosted use.

  • What model sizes are available in Qwen3-VL?

    Variants range from 2B to 235B parameters, including dense models and MoE (e.g., 30B-A3B), with Instruct and Thinking editions.

  • What are the key strengths of Qwen3-VL?

    It excels in multilingual OCR, long-context video analysis (up to 1M tokens), visual reasoning, spatial-temporal understanding, and agent capabilities, leading open-source multimodal benchmarks.

  • How do I run Qwen3-VL locally?

    Use Hugging Face Transformers (build from source for full support) or vLLM for inference; load models like Qwen3-VL-8B-Instruct and process images/videos with provided utilities.

  • Does Qwen3-VL support video input?

    Yes, it handles long videos with precise temporal grounding via text timestamp alignment and second-level indexing for hours-long content.

  • Where can I find Qwen3-VL models?

    All variants are hosted on Hugging Face (Qwen collection) and ModelScope, with code on GitHub at QwenLM/Qwen3-VL.

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