FLAGSHIP GA + VENDOR NUMBERS CONFIRMED (verified 2026-06-09): **Qwen 3.7 Max** shipped 2026-05-20 ('Qwen3.7: The Agent Frontier' on the official Qwen blog) -- the benchmark figures that circulated in aggregators are now vendor-published: **SWE-Bench Pro 60.6%, Terminal-Bench 2.0-Terminus 69.7%, GPQA 92.4%**, plus SWE-Multilingual 78.3%, SciCode 53.5%, QwenSVG 1608. PROPRIETARY / API-only on DashScope/Model Studio. Official pricing: **$2.50/M input, $7.50/M output list -- with a 50% launch promo ($1.25/$3.75) running until 2026-06-22**. Separately, **Qwen3.7-Plus** launched ~2026-06-02 on Bailian/Model Studio: multimodal (image + video input, text output) with GUI-agent, deep reasoning, and tool invocation at $0.40/$1.60 per 1M -- press-reported agent scores (ScreenSpot Pro 79.0, AndroidWorld 81.0) await vendor tables. Both models confirmed on Alibaba's official Model Studio model list
2026-06-09•Qwen blog (qwen.ai/blog?id=qwen3.7), Alibaba Cloud pricing campaign (alibabacloud.com/campaign/qwen-discount), Alibaba Model Studio model list (help.aliyun.com/zh/model-studio/models), VentureBeat (Qwen3.7-Plus)
INTERPRETABILITY RELEASE (2026-05-01): Alibaba released **Qwen-Scope** -- an open-source suite of **sparse autoencoder (SAE) weights** for the Qwen3 and Qwen3.5 model families, plus inference + visualization tooling. SAEs decompose internal model activations into interpretable feature directions (Anthropic / Google DeepMind have been the highest-profile labs publishing this style of interpretability work; Alibaba is the first major Chinese lab to ship SAE weights openly). Practical implication: researchers and red-teamers can now probe Qwen3.5 internals (feature attribution, concept steering, refusal-circuit analysis) without training their own SAEs -- a multi-month / multi-GPU effort. Adds to Qwen's open-weights story even as the Qwen 3.6 flagship line goes closed-weights. Materially raises the floor for safety / alignment research on Chinese open-weight families.
2026-05-01•Qwen blog (qwen.ai/blog?id=qwen-scope)
Qwen3.6-27B dropped 2026-04-22 (~48h after Max-Preview) as the new open-weights coding champion. DENSE 27B, NOT MoE -- all params active, simpler to deploy. Apache 2.0 open weights with no commercial restrictions. Multimodal: text + image + video. 262K native context (extensible to ~1M). Verified benchmarks: SWE-bench Verified 77.2%, SWE-bench Pro 53.5%, Terminal-Bench 2.0 59.3% (matches Claude Opus 4.5 exactly), MMLU-Pro 86.2%, GPQA Diamond 87.8%, AIME 2026 94.1%, MMMU-Pro 75.8%. Notably BEATS the Qwen3.5-397B-A17B MoE flagship on coding from a single consumer GPU -- this displaces the 35B-A3B as the open-weights centerpiece for most users. Quants from unsloth + mlx-community shipped same week
2026-04-22•qwen.ai/blog?id=qwen3.6-27b, HuggingFace Qwen/Qwen3.6-27B, implicator.ai analysis
Qwen 3.6-Max-Preview launched 2026-04-20 as Alibaba's first flagship shipped WITHOUT open weights -- #1 across SWE-bench Pro, Terminal-Bench 2.0, SciCode, QwenClawBench, and QwenWebBench. 256K context, text-only (no vision at launch), APIs compatible with OpenAI + Anthropic SDKs for easy swap-in. Signals Alibaba's pivot toward closed flagships while keeping mid-size models open. If you picked Qwen for the 'best open weights' story, the best model is no longer open -- but the 3.6-27B Apache 2.0 dense released 48 hours later mostly closes that complaint for non-frontier coding workloads
2026-04•Decrypt, CNTechPost
CRITICAL (2026-04-15): Qwen Code free OAuth tier discontinued. Alibaba throttled from 1,000 -> 100 free requests/day before fully cutting it. To keep using Qwen Code you now need an Alibaba Cloud Coding Plan (~$50/mo), BYOK via OpenRouter / Fireworks / Cerebras, or local inference on your own hardware via Ollama/vLLM. The 'free open-source AI' framing no longer applies to hosted access -- only to self-hosted deployment
2026-04•Decrypt, GitHub QwenLM/qwen-code issues #3316
Qwen3.6-35B-A3B open-weights release on 2026-04-16 is the open-model story of April. Sparse MoE with 35B total / 3B active, vision-language multimodal, Apache 2.0, 262K native context (extensible to ~1M). Benchmarks are best-in-class for open weights: SWE-bench Verified 73.4% (vs Gemma 4-31B 52%), AIME 2026 92.7, GPQA Diamond 86.0, MMMU 81.7 (beats Claude Sonnet 4.5 at 79.6), Terminal-Bench 2.0 51.5%. Simon Willison's pelican-drawing test favored Qwen3.6-35B-A3B over Claude Opus 4.7 -- a high-signal community endorsement
2026-04•Hugging Face Qwen3.6-35B-A3B, Alibaba Cloud announcement, Simon Willison, MarkTechPost, buildfastwithai review
Issues here are sourced from our editorial sweeps, not real-time telemetry. Newer issues may exist.