Qwen
@qwenlm • Alibaba Cloud's general-purpose AI models. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
54%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
14 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (19), Unknown (5), TypeScript (2)
Average size
1.4K
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-10-05(5d ago)GitHub API fallback30 repositories
54%
of the visible star count comes from this organization's top three repositories.
1.4K
stars per repository in this same snapshot.
Python
is the most common language here, with 14 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 30 repositories.
Balanced portfolio across 30 reposTop 3 share 54%
Organization pages work best when you separate portfolio breadth from flagship concentration. In Qwen's case, the visible top three repositories account for about 54% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (19), Unknown (5), TypeScript (2). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | qwenlm/Qwen3-TTS Qwen3-TTS is an open-source series of TTS models developed by the Qwen team at Alibaba Cloud, supporting stable, expressive, and streaming speech generation, free-form voice design, and vivid voice cloning. | Python | 13.7K |
| 2 | qwenlm/Qwen3.8 Qwen3.8 is the large language model series developed by Qwen team, Alibaba Group. | 4.3K | |
| 3 | qwenlm/Qwen3-Omni Qwen3-omni is a natively end-to-end, omni-modal LLM developed by the Qwen team at Alibaba Cloud, capable of understanding text, audio, images, and video, as well as generating speech in real time. | Jupyter Notebook | 4K |
| 4 | qwenlm/Qwen3-ASR Qwen3-ASR is an open-source series of ASR models developed by the Qwen team at Alibaba Cloud, supporting stable multilingual speech/music/song recognition, language detection and timestamp prediction. | Python | 3.7K |
| 5 | qwenlm/Qwen-MM-Plugins Make any agent harness multimodal-native. | Python | 3.1K |
| 6 | qwenlm/Qwen-Image-Layered Qwen-Image-Layered: Layered Decomposition for Inherent Editablity | Python | 2.1K |
| 7 | qwenlm/Qwen-Image-2.1 Qwen's most powerful open-source image generation model | Python | 1.8K |
| 8 | qwenlm/Qwen3-VL-Embedding | Python | 1.4K |
| 9 | qwenlm/Qwen-AgentWorld Qwen-AgentWorld: Language World Models for General Agents | Python | 1K |
| 10 | qwenlm/Qwen3-ASR-Toolkit Official Python toolkit for the Qwen3-ASR API. Parallel high‑throughput calls, robust long‑audio transcription, multi‑sample‑rate support. | Python | 1K |
| 11 | qwenlm/Qwen-VLA The official repository of Qwen-VLA | 769 | |
| 12 | qwenlm/FlashQLA high-performance linear attention kernel library built on TileLang | Python | 721 |
| 13 | qwenlm/Qwen3Guard Qwen3Guard is a multilingual guardrail model series developed by the Qwen team at Alibaba Cloud. | Python | 518 |
| 14 | qwenlm/Qwen-Drive-1.0 An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving | Python | 503 |
| 15 | qwenlm/Qwen3.8-Flash-Next Qwen3.8-Flash-Next is the foundation model developed by Qwen Team, Alibaba Group. | 422 | |
| 16 | qwenlm/open-computer-use MCP-based Computer Use service for Qwen Code and any AI agent — controls macOS, Linux, and Windows via accessibility APIs. | Swift | 284 |
| 17 | qwenlm/Qwen-Live-Harness An open-source agent harness powered by the Qwen Omni Realtime API—see, hear, and act, with built-in memory. | TypeScript | 187 |
| 18 | qwenlm/Qwen-RobotNav Official Repo for Qwen-RobotNav | 184 | |
| 19 | qwenlm/Qwen-RobotManip Official Repo for Qwen-RobotManip | 175 | |
| 20 | qwenlm/Qwen-Image-Bench | Python | 163 |
| 21 | qwenlm/qwen-code-examples A collection of practical examples and best practices for Qwen Code | TypeScript | 143 |
| 22 | qwenlm/E-CommerceBench Long-horizon benchmark where 18 LLM agents got ¥100,000 each and ran simulated online stores for 365 days on real market data: negotiating with suppliers, pricing, managing inventory, keeping cash flow alive. | Python | 108 |
| 23 | qwenlm/RecreationWorld RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents | Python | 92 |
| 24 | qwenlm/WebWorld WebWorld is a large-scale web world model that helps train web agents in a simulated browser, avoiding the latency and safety issues of the real web. | Python | 61 |
| 25 | qwenlm/qwen-code-docs A documentation translation tool specifically designed for Qwen Code | MDX | 52 |
| 26 | qwenlm/RationaleRM | Python | 37 |
| 27 | qwenlm/Confident-Decoding | Python | 34 |
| 28 | qwenlm/Omnilingua-Bench | Python | 8 |
| 29 | qwenlm/qwen-mm-plugins-hub A documentation for Qwen-MM-Plugins | HTML | 6 |
| 30 | qwenlm/D2K-Bench | Python | 1 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.