Alibaba Qwen released open-weight Qwen3.8-Flash, a multimodal Mixture-of-Experts model billed as an early preview of the Qwen4 architecture. The production-scale checkpoint is 125B parameters plus 51B N-gram embeddings, with only 6B activated per token. The new stack combines Gated DeltaNet plus Qwen Sparse Attention, Gated Residual branches, N-gram embeddings that can live in host memory, and a refined Muon optimizer. Qwen said it trained at about one-ninth the cost of Qwen3.7-Plus while beating it across the board, especially on coding and office work: 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision with CI. Native context is 262K tokens, extendable to 1M with YaRN; at 1M context the QSA kernel is up to 7.6x faster in prefill and 4.9x faster in decode. QwenCloud lists roughly $0.15 input / $0.47 output per million tokens, and day-0 runtimes arrived from NVIDIA NeMo, SGLang, Unsloth, OpenRouter, and OpenCode Go.
Key Takeaways
- โ125B/6B MoE hits 62.5 SWE-bench Pro and 58.7 DeepSWE 1.1 at ~1/9 the training cost of Qwen3.7-Plus.
- โQSA hybrid attention is up to 7.6x faster in prefill and 4.9x in decode at 1M context.
- โOpen weights plus day-0 support on QwenCloud, OpenRouter, OpenCode, NeMo, SGLang, and Unsloth.