Qwen3-VL

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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
Developer	Qwen / Alibaba Cloud
Type	Open-weight vision-language LLM
License	Apache 2.0
GitHub	QwenLM/Qwen3-VL
Hugging Face	Qwen3-VL-8B-Instruct
Technical Report	arxiv.org/abs/2511.21631
Documentation	qwenlm.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

Variant	Parameters	Key capability
Qwen3-VL-2B-Instruct	2B	Lightweight multimodal inference
Qwen3-VL-8B-Instruct	8B	General vision-language tasks
Qwen3-VL-8B-Thinking	8B	Step-by-step visual reasoning

Tools and Resources

GitHub (QwenLM/Qwen3-VL): model code, usage examples, and fine-tuning scripts.
Hugging Face (Qwen): download weights for all variants.
Technical Report: arXiv paper with architecture and benchmark details.
Qwen API Platform: access Qwen3-VL via the DashScope API.
Ollama: run Qwen3-VL locally.

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.

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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.

AXON — Live AI Desktop Agent

AXON is an open-source, vision-based autonomous desktop agent built on the paradigm of "GitHub Copilot for your entire Operating System." Unlike traditional RPA or automation tools that rely on brittle element selectors, fixed coordinate macros, or application-specific APIs, AXON interacts with computers exactly like a human does: by looking at the screen. Using an advanced cognitive loop, AXON captures live screen feeds, analyzes the visual layout using Vision Language Models (VLMs) and OCR, dynamically charts a multi-step execution plan, and fires native system-level commands to control the cursor and keyboard. If a user can see a task on their display, AXON can automate it. Key Technical Architecture & Features: Multi-LLM Integration: Seamlessly supports cloud-based frontier models (Google Gemini, Anthropic Claude) alongside fully local, privacy-focused deployments via Ollama. Fluid PyQt6 Overlay: Uses a hardware-accelerated transparent canvas with a color-coded visual reticle that tracks agent states (Idle, Thinking, Moving, Clicking) without taking over the screen. Safety Engineering: Built with a global hardware F12 Kill Switch to instantly halt background threads, paired with algorithmic stuck-loop detection to prevent runaway inputs. By turning complex system workflows into a simple natural language interface, AXON bridges the gap between AI reasoning and native OS execution—democratizing desktop automation for everyone.

Uplan: A Stateful Deep-Agent Document Intelligence

International visa rejections disproportionately stem not from document illegibility but from logical inconsistencies: unexplained financial spikes, crossdocument income disparities, sponsor–applicant coherence failures, and transliteration-induced identity mismatches. Existing automated systems either blindly pass anomalous documents or over-flag legitimate ones, while traditional consultancies are costly, inconsistent, and constitute a privacy risk when handling sensitive financial data. The Uplan concept emerged from a concrete crisis: a real visa applicant’s processwas jeopardised due to the negligence of a consultancy that failed to identifya critical financial narrative inconsistency prior to submission. The applicant declared a conservative baseline taxable income on the primary application form but simultaneously presented supplementary affidavits showing substantially higher, unverified financial figures. To an experienced immigration officer, such a pattern triggers immediate scrutiny. To an automated document-processing tool

Ken: The Real-Time Co-Listener

Every professional consultation contains a moment where you stop understanding — and say nothing. The lawyer mentions indemnification clauses. The doctor walks through your treatment options. You nod. You leave. You google it in the parking lot. Existing tools don't solve this. Otter records the meeting — but the moment has passed. Hedy nudges in real time but can't explain why it fires. ChatGPT answers what you ask — but you don't know what to ask. Ken is the only tool combining real-time intervention, explainable triggers, and self-hostable open-weight infrastructure. Ken transcribes live audio and runs it through four explainable trigger types: Jargon Bomb, Impact Alert, Question Suggester, and Commitment Tracker — each mapped to a specific cognitive gap. Every intervention tells you exactly why it fired. See it in our demo video above. Built on AMD Developer Cloud (Instinct MI300X, ROCm 7) using faster-whisper and Qwen3-14B via vLLM. Full stack runs on open weights, self-hostable — the first AMD-native co-listener viable for law firms, hospitals, and enterprises where data cannot leave the firewall. Market: The global AI meeting assistant market is $4–6B and growing. Ken's SAM — regulated-industry knowledge workers in legal, healthcare, and finance — exceeds 15 million professionals in the US alone. Freemium + Pro at $19/month for individuals; $30–$80/user/month for enterprise on-premise deployment. Future: Domain packs, multilingual support, and community trigger rules near-term. Longer term: insurance, immigration, government benefits — any regulated expert-to-layperson conversation. Consumer adoption drives enterprise pipeline.

AMD-Link: Autonomous PCB Routing for Ryzen

AMD-Link is an autonomous hardware design system that brings cognitive hardware awareness to PCB layout, specifically targeting the routing bottleneck around the AMD Ryzen Embedded V3000 Series. The V3000 is a powerful Zen 3 SoC offering up to 8 cores, 20 lanes of PCIe Gen4, dual 10Gb Ethernet, and a flexible 10W–54W TDP envelope — but its 484-pin BGA package puts manual board layout out of reach for most builders. Limited routing channels, multilayer escape strategies, and the strict length-matching and impedance demands of buses like DDR5 mean that even seasoned engineers can spend hours fan-out routing a single device, with constant risk of DRC violations and human error. AMD-Link replaces that grind with an AI engine that ingests a KiCad PCB, reasons about the pin grid, and generates compliant routes in seconds. In our demo it navigates the dense 484-pin V3000 grid to a breakout header, avoids obstacles, and maintains parallel bus alignment automatically — collapsing a 30-minute manual task into roughly 5 seconds, and a full board fan-out from 120+ minutes (manual) or 45 minutes (traditional autorouter) down to 2 minutes. That's up to a 60x improvement in routing efficiency. The system is wrapped in a Mission Control UI built on Streamlit with custom CSS, providing real-time compliance gauges, live PCB layout previews, audit logs of every AI routing decision, and instant signal-name cross-referencing. We separate Objective Health (ground-truth KiCad DRC/ERC rules) from Subjective Confidence (the AI's self-assessment), so low-confidence edge cases get flagged for human review while routine patterns are committed automatically. Our roadmap extends from BGA fan-out (current) through DDR5 length matching, multi-agent thermal/SI co-optimization, and ultimately a schematic-to-silicon autonomous workflow.