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Qwen3-VL

Qwen3-VL is Alibaba Cloud's vision-language model series, designed to understand and reason over images, videos, and text in a single architecture. It is available in 2B and 8B parameter sizes, both released under Apache 2.0. The architecture handles diverse visual tasks including document understanding, chart analysis, image-based question answering, and video comprehension.

General
DeveloperQwen / Alibaba Cloud
TypeOpen-weight vision-language LLM
LicenseApache 2.0
GitHubQwenLM/Qwen3-VL
Hugging FaceQwen3-VL-8B-Instruct
Technical Reportarxiv.org/abs/2511.21631
Documentationqwenlm.github.io

Core Features

  • Multimodal inputs: accepts text, images, and videos in a single conversation turn.
  • Document and chart understanding: parses structured visual content like tables, slides, PDFs, and infographics.
  • Video comprehension: understands multi-frame video sequences and answers temporal questions.
  • Thinking mode: includes a reasoning variant (Qwen3-VL-8B-Thinking) for step-by-step visual problem solving.
  • Apache 2.0: weights are open for commercial use and fine-tuning.

Model Variants

VariantParametersKey capability
Qwen3-VL-2B-Instruct2BLightweight multimodal inference
Qwen3-VL-8B-Instruct8BGeneral vision-language tasks
Qwen3-VL-8B-Thinking8BStep-by-step visual reasoning

Tools and Resources


Ecosystem and Integrations

  • Served through Alibaba Cloud DashScope via an OpenAI-compatible vision endpoint.
  • Available on Ollama for local multimodal inference.
  • Weights downloadable from Hugging Face Hub in standard and GGUF formats.
  • Forms the encoder backbone for Qwen-Image-2.0, the image generation model.

Model weights are available on Hugging Face. API access is available through the Qwen API Platform and Alibaba Cloud Model Studio.

Qwen Qwen3-VL AI technology Hackathon projects

Discover innovative solutions crafted with Qwen Qwen3-VL AI technology, developed by our community members during our engaging hackathons.

OmniDoc — Talk to Any Document

OmniDoc — Talk to Any Document

Documents aren't just text. Financial reports live in charts. Scientific insights hide in figures. Legal risks bury in tables. Traditional document AI treats visuals as noise. OmniDoc treats them as signal. OmniDoc is a multimodal document intelligence platform that understands everything: text, charts, tables, diagrams, handwritten notes, scanned pages, equations, and mixed-language content. Upload any document and talk to it. Ask: "What was the gross margin trend from section 3 charts?" → OmniDoc reads the bars, not just surrounding text. "Which appendix clauses exceed $500K?" → Parses tables precisely. "Explain the page-12 diagram's relation to the conclusion" → Understands figures in context. Powered by a two-model pipeline optimized for AMD MI300X: • Llama 3.2 Vision 90B processes pages as high-res images, preserving layout and visuals • Qwen3-VL extracts structured data from tables/forms with cross-lingual precision Both run simultaneously on a single MI300X (192GB HBM3, 5.3TB/s bandwidth)—eliminating the complex multi-GPU parallelism H100s would require. Pipeline: 300 DPI page rendering → Llama for semantic structure → Qwen for table precision → retrieval layer → intelligent query routing → cited responses with confidence scores. Performance: 100-page PDF in 42s | 340 pages/min batch | 12 concurrent sessions | ~18× faster than cloud CPU. Use it for: M&A due diligence, regulatory review, academic literature synthesis, contract portfolio analysis, insurance claims with form+image understanding. Ships as a ready-to-use web app: drag-and-drop upload, conversational Q&A, document navigation, and citation tracking that links every answer to its source page and element.