
Overall Score
43
Top 2%
of AI companies
by Alibaba🇨🇳
Alibaba's open-source LLM family with strong multilingual support
Qwen (Tongyi Qianwen) is Alibaba's family of open-source LLMs ranging from 0.5B to 72B parameters. Known for strong multilingual capabilities and competitive performance.

Overall Score
43
Top 2%
of AI companies
Overall Score
43
GitHub Stars
0
Monthly Visits
38.0M
Community Rating
4.7
Qwen (Tongyi Qianwen) is Alibaba's family of open-source LLMs ranging from 0.5B to 72B parameters. Known for strong multilingual capabilities and competitive performance.
Latest detected signals across traffic, developer activity and community mentions.
HN: "Qwen3.6-35B-A3B: Agentic coding power, now open to all"
Indexed by indexator.ai
Referral growth vs previous period
| Keyword | Volume / Mo | CPC |
|---|---|---|
| qwen | 1,237,500 | $0.28 |
| qwen ai | 488,510 | $0.34 |
| qwen chat | 201,640 | $0.19 |
| квен | 72,270 | $0.08 |
| qwen 3.6 | 131,000 | — |
Qwen Chat is an AI tool designed to offer a broad suite of functionalities that caters to various facets of AI, with applications in chatbot integration, image and video understanding, and image generation. It also allows for advanced document processing, equipping users with the ability to extract ...
Get notified when score, traffic, GitHub or community signals change.
Latest detected signals across traffic, developer activity and community mentions.
HN: "Qwen3.6-35B-A3B: Agentic coding power, now open to all"
Indexed by indexator.ai
Six signals, each 0–100, blended into the overall by their listed weight.
Each dimension is weighted (traffic 20% · community 25% · growth 15% · momentum 15% · innovation 10%) and combined into the overall.
View full analytics →Referral growth vs previous period
| Keyword | Volume / Mo | CPC |
|---|---|---|
| qwen | 1,237,500 | $0.28 |
| qwen ai | 488,510 | $0.34 |
| qwen chat | 201,640 | $0.19 |
| квен | 72,270 | $0.08 |
| qwen 3.6 | 131,000 | — |
Saves
634.9K
Rating
4.7
6 reviews
Views
2.7K
Category
Productivity
Pros · 39
Cons · 10
Rating distribution
User reviews · 1
Super awsome nice working
Supported features
Release history · 7+
Stronger agentic coding across frontend work, terminal operations, automated task execution, and repository-level problem solving. Better long-horizon agent performance through tighter integration of reasoning, memory, and tool execution, with gains on planning and tool-calling benchmarks. Sharper multimodal reasoning for document understanding, visual analysis, video reasoning, visual coding, and fine-grained grounding. Larger default context handling with a 1M context window plus stronger ultra-long-context information extraction. More stable real-world agent workflows with broader assistant compatibility and a preserve_thinking API option to maintain prior reasoning context on multistep tasks.
Native multimodal VLM: open-weights release of Qwen3.5-397B-A17B, positioned as a vision-language model with strong reasoning, coding, agent capability, and multimodal understanding. 397B total, 17B active: sparse MoE design activates 17B parameters per forward pass out of 397B, targeting high capability with lower runtime cost. Hybrid attention for inference efficiency: fuses linear attention (Gated Delta Networks) with sparse MoE to optimize speed and cost without dropping performance. Massive multilingual expansion: language and dialect coverage increases from 119 to 201, aiming for broader global usability. Qwen3.5-Plus hosted tier: 1M context by default with built-in tools and adaptive tool use, plus modes like Auto (tool use), Thinking (deep reasoning), Fast (instant) for different latency and cost needs.
Incorporates hybrid reasoning capabilities for improved adaptability and efficiency. Available in both dense and sparse model architectures. Trained on 36 trillion tokens across 119 languages and dialects. Supports a 128K token context window for larger models. Enhanced reasoning capabilities with modes for both complex logical tasks and general-purpose chat.
Introduced over 100 open-source models, including specialized versions for coding, mathematics, and multimodal tasks. Enhanced capabilities in various domains, outperforming previous versions.
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