Qwen

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Qwen

Qwen is a large language model family developed by the Qwen team at Alibaba Cloud. First released in 2023, the series spans dense and mixture-of-experts architectures across text, vision, and code, with most models published under the Apache 2.0 license. Developers can access Qwen models through Alibaba Cloud's Model Studio (DashScope) using an OpenAI-compatible API, or download weights directly from Hugging Face and GitHub.

General
Company	Qwen / Alibaba Cloud
Founded	2023 (first model release)
Headquarters	Hangzhou, China
Website	qwen.ai
Documentation	Qwen Docs
GitHub	github.com/QwenLM
Hugging Face	huggingface.co/Qwen
Type	LLM Provider / Open-Source AI Lab

Core Products

Qwen3 (Text LLMs)

Qwen3 is the flagship text model family, released in April 2025 under Apache 2.0. It includes dense models from 0.6B to 32B parameters and mixture-of-experts models up to 235B total parameters (22B active). All models support multilingual text generation, reasoning, tool use, and agentic workflows.

Qwen3-Coder

Qwen3-Coder is a coding-specialized model with 480B total parameters and 35B active, trained on 7.5 trillion tokens with a 70% code-focused dataset. Released in July 2025, it achieves state-of-the-art results among open models on SWE-Bench Verified.

Qwen3.6 (Vision-Language)

Qwen3.6 is a multimodal model with a unified vision-language architecture trained on trillions of multimodal tokens. It supports text and image inputs across 201 languages and dialects, with capabilities covering reasoning, coding, and visual understanding.

Qwen-Image-2.0

Qwen-Image-2.0 is a 7B-parameter image generation model supporting photorealism, professional typography, and unified generation-editing workflows, released in February 2026.

Qwen-MT

Qwen-MT is a translation model covering 92 major languages and dialects, reaching over 95% of the global population. It is designed for high-quality translation in production pipelines.

Qwen Code

Qwen Code is an open-source terminal coding agent optimized for the Qwen model series. It supports writing features, fixing bugs, navigating large codebases, and generating pull requests, with GitHub Actions integration available.

Developer Resources

Qwen models are accessible through Alibaba Cloud Model Studio (DashScope) via an OpenAI-compatible API, or as open weights on Hugging Face. The API supports both text-only and multimodal models.

Helpful Links

Qwen Documentation (model cards, quickstarts, and research notes)
Alibaba Cloud Model Studio (API reference and DashScope SDK docs)
GitHub (QwenLM) (open-source model weights, code, and tools)
Hugging Face (Qwen) (download model weights and datasets)
Qwen API Platform (get an API key and start building)
Qwen Studio (web interface for chat, image understanding, and generation)

Key Features

Open weights under Apache 2.0 Most Qwen3 models are released under Apache 2.0, allowing commercial use, fine-tuning, and redistribution without restrictions.

OpenAI-compatible API Qwen models are served through DashScope using the OpenAI-compatible endpoint format, making it straightforward to use Qwen models in existing OpenAI SDK integrations.

Multilingual coverage Qwen3.6 supports 201 languages and dialects. Qwen-MT covers 92 major languages for dedicated translation tasks.

Mixture-of-Experts (MoE) architecture The largest Qwen3 models use MoE, activating only a subset of total parameters per token (for example, 22B of 235B active). This reduces inference cost relative to comparably capable dense models.

Use Cases

Agentic coding workflows Qwen3-Coder and Qwen Code are designed for software development tasks: writing features, fixing bugs, navigating large codebases, and generating pull requests via the terminal or CI pipelines.

Multilingual applications Qwen-MT and Qwen3.6's broad language support make them suitable for translation tools, multilingual chatbots, and localized content pipelines.

Multimodal document and image processing Qwen3.6 handles image understanding, document analysis, and visual reasoning alongside text, enabling applications like document Q&A and visual search.

Edit on GitHub

Qwen AI Technologies Hackathon projects

Discover innovative solutions crafted with Qwen AI Technologies, developed by our community members during our engaging hackathons.

AuraOS

Aura is a sovereign, neuro-symbolic AI operating system designed to make advanced artificial intelligence more transparent, efficient, collaborative, and accountable. Instead of sending every instruction directly to a language model, Aura can locally interpret natural-language intent, organize it into structured semantic and routing representations, identify relevant code or knowledge, and prepare a bounded task before model use is required. Aura combines deterministic routing, a 4,096-primitive lexical system, polysynthetic six-slot intent structures, visual code topology, guarded human-agent workflows, and a Learning Arena where verified experiences can be tested before becoming reusable knowledge. Its Observatory allows users to see how Aura understood an instruction, while the Human Agent Arena supports node-specific dialogue, approval gates, verification, and preserved attempt artifacts—including failed work that may help future refactoring. Aura is built around human authority. Models can propose, explain, investigate, and assist, but they cannot silently expand scope, bypass evidence requirements, or merge changes without approval. The result is an AI architecture focused not only on capability, but on sovereignty, inspectability, learning, and responsible action.

SmartBandit Router

BanditRoute treats each AI model as an "arm" in a multi-armed bandit problem. Using Thompson Sampling conditioned on task type (code, math, QA, creative), it learns over time which model to send each query to balancing correctness against token cost and latency via a custom reward function, rather than optimizing for accuracy alone. No routing rule was ever hand-written; behaviors like sending math questions to the strongest model and easy QA to a cheap one emerged entirely from feedback. The project is validated two ways: a full simulation (2000 queries, 4 mock model profiles) that shows clean convergence and ~93% cost savings, and a live integration that runs the same bandit logic against real free-tier models via the OpenRouter API proving the approach works beyond simulation, not just in theory.

AI Classroom Edge Intelligence

AI Classroom Edge Intelligence is a privacy-first classroom AI platform built for schools that need useful AI without sending every piece of student information to the cloud. The platform evaluates each task by privacy level, connectivity, and complexity, then routes it to Offline Edge Mode, a Local Classroom Server, or Fireworks AI Cloud Assist. Sensitive or restricted information stays local. Eligible anonymized, high-complexity tasks are sent through a secure Express backend to Fireworks Serverless using Qwen3.7 Plus. The project includes an AMD Model Router, Edge Runtime Monitor, Rural Connectivity Simulator, Privacy and Local Data Ownership Console, Classroom Digital Twin, and teacher approval workflow. Live results display the provider, model, route, privacy classification, latency, safety note, and AI response. API keys remain server-side, and the privacy guard blocks sensitive requests from cloud inference. The project was inspired by rural schools where connectivity can be unreliable and student privacy is critical. Instead of acting as a simple chatbot, it serves as an intelligent routing and decision-support system. Teachers review, edit, approve, or reject recommendations before instructional actions are recorded. The current implementation includes a working browser interface, real backend routing, live Fireworks Serverless integration, server-side key protection, and Docker containerization. Local AMD AI PC inference, GPU/NPU acceleration, device telemetry, and production synchronization are clearly identified as future work. The long-term vision is a school-owned AI platform combining local intelligence, optional cloud reasoning, persistent classroom evidence, and teacher oversight for rural and underserved communities.

Claption

Claption is a containerized video captioning app built for the AMD Developer Hackathon ACT II Video Captioning track. It processes short video clips by sampling frames with FFmpeg, generating a grounded visual fact sheet with Fireworks AI, and then rewriting that factual summary into four required caption styles: formal, sarcastic, humorous-tech, and humorous-non-tech. To improve leaderboard performance, Claption separates factual understanding from tone generation, then uses an internal LLM judge loop to score captions for accuracy, tone match, humor quality, and hallucination risk. Weak captions are automatically repaired before final export. The project includes a Next.js demo app, batch CLI, Docker container, sample outputs, and judge-ready setup instructions.

DispatchAI is an autonomous emergency grid

During mass-casualty events or natural disasters, human dispatchers become severely overwhelmed by chaotic, unstructured reports . Crucial seconds are lost parsing text, mapping locations, and finding the right medic, which causes delayed response times when seconds mean the difference between life and death dispatchAI is a next-generation autonomous emergency grid powered by AMD designed to eliminate this human bottleneck . It provides autonomous coordination at scale by using Natural Language Processing to instantly ingest unstructured emergency reports via Telegram webhooks . Our system scales to handle 10 simultaneous mass-casualty incidents in under 10 seconds, successfully reducing average dispatch latency by 90% . The architecture operates as an Event-Driven Asynchronous Pipeline, utilizing a FastAPI backend and a tactical React/Vite dashboard . At its core is the "AMD Brain," which is powered by AMD Instinct™ MI300X Accelerators running the Qwen2.5-14B-Instruct model . We chose this model because it offers the perfect balance of deep reasoning for complex medical routing and high inference speed directly on AMD hardware . The LLM is rigorously prompt-engineered to align with human dispatcher logic, matching the exact skills needed with the closest available volunteer . Furthermore, the system includes dynamic "Follow-Up" logic via Telegram to clarify vague emergencies, and features a 15-second graceful fallback to pure proximity math to ensure enterprise reliability without AI hallucinations . DispatchAI ensures the right help arrives exactly when it is needed

Vdcap.AI

Vdcap.AI is a full-stack, automated video captioning platform that generates and compares four distinct caption styles side-by-side. Designed for creators and marketers, the system accepts video uploads and extracts audio and keyframes using FFmpeg and OpenCV. If audio is present, it uses cloud Speech-to-Text APIs Fireworks (Whisper and LLM available on Fireworks AI) for high-fidelity transcription. If the video is mute, the pipeline falls back to visual frame analysis. The transcript and visual context are processed via Fireworks AI using the Llama-3.1-8B-Instruct and GLM models. These generate four tailored caption styles: Formal (professional), Sarcastic (witty), Humorous-Tech (developer-oriented), and Humorous-NonTech (casual). FFmpeg overlay filters burn these captions directly onto separate video streams. On the frontend, a handcrafted dark-mode glassmorphism interface presents the results in a synchronized 4-quadrant player. Seeking, playing, or pausing any video instantly syncs the other three, allowing real-time comparison of subtitle styles. Users can export their captions as individual SRT files, structured JSON data, captioned MP4s, or a complete ZIP package including an interactive HTML summary report. Vdcap.AI also contains a modular pipeline using LoRA adapters for fine-tuning caption models on MSR-VTT and ActivityNet datasets, making it fully ready for custom AI deployment.