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Models2026-08-14 · source-backed
The Hugging Face page is marked "Upcoming release" with no model card, license, architecture details, context length or benchmarks, after Alibaba promised both Qwen3.8-Max and the 27B weights for the week of August 10. A ModelScope countdown pointed at August 15. Unsloth signals suggest quantized builds around 17GB, consistent with a 4-bit 27B-class model.
Each link below shares sources, entities, or timing with this story.
Staged on ModelScope for 23:00 Beijing time August 26, it's roughly 125B parameters plus a separate N-gram embedding table of about 51B, activating 6B per token, with GDN gated-delta hybrid layers and Qwen Sparse Attention. Alibaba frames it as a technology preview of the Qwen...
Somebody diffed the configs. Zero architectural changes. Same 64 layers, same 5,120 hidden dimension, same hybrid Gated DeltaNet → FFN / Gated Attention → FFN block structure as Qwen3.6-27B. The r/LocalLLaMA post showing this hit 945 upvotes and 157 comments, and Hugging Face...
Alibaba announced it August 3: sparse MoE with ~95B active per token, 1M context, 128k max output, $2/M input and $6/M output with $0.25/M cached. 67.4 on Terminal-Bench 2.1 (up from 61.0 for 3.7 Max), #4 on Frontend Code Arena at 1,668 Elo, #2 on Vals Index among open-weight...
Alibaba released Qwen3.6-27B on April 22. Dense architecture. Open weights. 77.2% on SWE-bench Verified, within 3.7 points of Claude Opus 4.6. On SkillsBench, it scores 48.2% versus its own 397B MoE predecessor's 30.0%. That's a 77% improvement with 14.8x fewer parameters. Let...
The August 14 report covers January through August 2026: model repos grew from 2.43M to 2.96M, datasets from 711K to 1M, and 85.6% of models have under 200 lifetime downloads (Hugging Face). Chinese labs shipped monthly parameter ceilings of 754B to 2.78T against sub-130B for...
Moonshot AI dropped Kimi K2.6 today and the numbers are hard to ignore. One trillion parameters total, 32 billion active per token across 384 experts, 256K context window, and native multimodal input. It scores 58.6 on SWE-Bench Pro versus GPT-5.4's 57.7 and Claude Opus 4.6's...
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