⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!

The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens.

125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency.

What's new: 🥳
- Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4.
- Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks.
- Strong performance: scoring 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).
- 262K native context, extensible to 1M with YaRN.

We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀

We can't wait to see what you build with Qwen3.8-Flash!👀👇
- Blog: https://qwen.ai/blog?id=qwen3.8-flash-next
- Technical Report: https://github.com/QwenLM/Qwen3.8-Flash-Next/blob/main/tech_report.pdf
- Hugging Face: https://huggingface.co/Qwen/Qwen3.8-Flash-Next?spm=a2ty_o06.30285417.0.0.1d73c921FsyOPe&file=Qwen3.8-Flash-Next
- ModelScope: https://modelscope.cn/models/Qwen/Qwen3.8-Flash-Next?spm=a2ty_o06.30285417.0.0.1d73c921XAP2dV&file=Qwen3.8-Flash-Next