Qwen
Alibaba's Qwen model family โ open-weight leaders (Qwen3.6-27B) and closed-weight pivot (Qwen3.7 Max/Plus); SEA-LION regional variant
Qwen
Entity page for Qwen โ Alibaba's model family and one of the most-covered open-weight lines in this knowledge base.
Overview
Qwen (้ไนๅ้ฎ) is developed by Alibaba's Qwen team. The 2026 story has two chapters:
- Open-weight leadership โ Qwen3.6-27B (Apache 2.0) as frontier-adjacent dense model
- Closed-weight pivot โ Qwen3.7 Max/Plus competing with Opus and GPT-5.5 via API
Current models (Jun 2026)
| Model | Type | Highlights |
|---|---|---|
| Qwen3.7 Max | Closed API | 90/100 BenchLM, 69.7 Terminal-Bench 2.0, 44.5 Apex, 80.4% SWE-Verified, 1M context |
| Qwen-AgentWorld-397B-A17B | Open MoE | 58.71 AgentWorldBench (beats GPT-5.4), 7-domain LWM, 256K context |
| Qwen-AgentWorld-35B-A3B | Open MoE | 56.39 AgentWorldBench, 7-domain LWM, Apache 2.0 |
| Qwen3.7 Plus | Closed multimodal | Vision+video agent, $0.32/$1.28 |
| Qwen3.6-27B | Open dense | 77.2% SWE-Bench Verified; beats 397B MoE sibling |
| Qwen3.6-35B-A3B | Open MoE | 35B total, 3B active; thinking preservation |
| Qwen-SEA-LION-v4.5-27B | Regional (AISG) | SEA languages, distilled from Qwen3.5-397B |
Strategic shifts
Dense beats MoE (Apr 2026)
Qwen3.6-27B proved a 27B dense model can outperform Alibaba's own 397B MoE on agentic coding โ challenging the "bigger MoE always wins" narrative.
โ Qwen36 27b Dense Beats Moe Agentic Coding Analysis 2026 06 03
Open โ closed pivot (MayโJun 2026)
Qwen3.7 Max moves to proprietary API-only distribution, competing for enterprise revenue against Claude and GPT. Open-weight community goodwill from Qwen3.6 does not automatically transfer.
โ Qwen37 Max Plus Closed Weight Frontier Agent Era 2026 06 16
Caveat: Qwen3.7 Max verbosity (~4ร median output tokens) narrows stated cost advantages.
Key benchmarks
| Model | SWE-bench Pro | SWE-bench Verified | Terminal-Bench 2.0 | Apex | Notes |
|---|---|---|---|---|---|
| Qwen3.7 Max | 60.6% | 80.4% | 69.7 | 44.5 | 35-hour autonomous kernel demo, 1M context |
| Qwen-AgentWorld-397B | โ | โ | โ | โ | 58.71 AgentWorldBench, 7-domain LWM |
| Qwen3.6-27B | โ | 77.2% | โ | โ | Perfect 100/100 tool-eval-bench |
| Qwen3.6-35B-A3B | โ | competitive | โ | โ | Thinking preservation |
Key articles
| Topic | Article |
|---|---|
| Qwen3.7-Max + AgentWorld (Jul 2026) | Qwen3 7 Max Agent Centric Era Long Horizon Execution 2026 07 01 |
| Qwen3.7 Max & Plus | Qwen37 Max Plus Closed Weight Frontier Agent Era 2026 06 16 |
| Qwen3.7 agent comparison | Qwen37 Max Frontier Agent Comparison 2026 05 20 |
| Qwen3.6-27B dense analysis | Qwen36 27b Dense Beats Moe Agentic Coding Analysis 2026 06 03 |
| Qwen3.6-35B-A3B | Qwen36 35b A3b Agentic Coding Thinking Preservation 2026 04 17 |
| SEA-LION regional | Qwen Sea Lion V45 27b Regional Specialization 2026 05 20 |
| vs Gemma 4B | Qwen Vs Gemma 4b Comparison |
| Open agents showdown | Open Source Agents Showdown Qwen36 27b V4pro Gemma4 2026 05 19 |
| Benchmark compilation | Frontier Models Benchmark Compilation 2026 04 15 |
| Embodied AI (Robot) | Qwen Robot Suite Embodied Ai Navigation Manipulation World Model 2026 06 19 |
Related
- Concepts: Frontier Models, Mixture Of Experts, Agentic Coding
- Compared to: DeepSeek-V4-Pro, Gemma 4, GLM-5.2, Claude Opus
- Deployment: Howto Multi Model Routing Layer, Howto Vllm Deployment Guide
Link map
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