Alibaba releases weights for Qwen3.8 models under Apache 2.0 license, including Qwen3.8-27B, which it says beats Qwen3.7-Plus and excels in real-world coding
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M
@alibaba_qwen
Context & Ripple Effects
Alibaba has been iterating on open-weight Qwen models across sizes and architectures, from the 397B-parameter multimodal Qwen3.5 release to smaller variants and a 27B dense Qwen3.6 model. The new release preserves the 27B dense-model form factor while adding native multimodality and a much longer stated context window.
Developers and organizations can now obtain Qwen3.8 weights under Apache 2.0, giving them a deployable 27B multimodal option for coding and office-oriented workflows.
Alibaba broadens distribution for its current Qwen generation while positioning Qwen3.8-27B against its own Qwen3.7-Plus offering on overall performance.
Second-order effects
Teams choosing between hosted frontier models and self-managed models gain a new candidate with native multimodality and 262K stated context, increasing pressure on vendors to differentiate through performance, tooling, or managed deployment.
Alibaba’s parallel releases of dense and MoE models, including the 35B-total-parameter Qwen3.6 MoE for agentic coding, make architecture choice more salient for buyers weighing open-weight coding systems.
Third-order effects
If Alibaba continues pairing compact models with permissive weight releases, capability comparisons will increasingly occur across deployable model formats rather than only among proprietary hosted services.
The Qwen release cadence points toward open weights functioning as a distribution channel for model ecosystems, with deployment control and complementary tooling becoming more important points of competition.
The trend: Open-weight AI competition is shifting toward capable, compact multimodal models that organizations can adopt and run on their own terms.
HOLLY 💩! 27B dense, native vision, 262K context... time to get it running fully local! images + hour-long video, Gated DeltaNet + gated attention, MTP, thinking control per request, and up to 1M context with YaRN. Qwen just made every RTX and DGX owner very happy today 😅
holy shit! look at the table qwen just published for the 27b. beating opus 4.6 max on computer use, 84.3 vs 72.7 on osworld. beating it on mobile use, 81.9 vs 62. beating it on multimodal software engineering. and visual math isn't even close, 94.6 vs 65.5. and i'll verify what
I can't believe it Qwen3.8-27B is matching Opus 4.6 Max... the model that was the best (and the most expensive) just 6 months ago. And you can run it on your laptop. Locally. Fully open weights and under apache license. This level of intelligence in such a small model is sooo
Qwen3.8-27B can now be run locally! ✨ Run on 17GB RAM via Unsloth Dynamic GGUFs. Qwen3.8-27B is by far the strongest model for its size. We also uploaded NVFP4 quants. GGUF: https://huggingface.co/... Guide: https://unsloth.ai/...
Qwen 3.8-27B is finally here the jump from 3.6-27B is kinda insane... every single benchmark went up. Terminal coding: 63.4 → 73.0 SWE-bench Pro: 53.5 → 61.7 DeepSWE: 13.3 → 42.2 Software engineering: 49.3 → 79.0 remember you can run this on a $700 used 3090..
The new Qwen 3.7 27B, running as a 17GB GGUF in LM Studio on my M5 Max laptop, just drew me the best pelican riding a bicycle I've seen from any model that runs on my laptop