A bit more tl;dr about the model: Qwen3.8-Flash-Next combines 125B MoE parameters with 51B N-gram embeddings, while activating only 6B parameters per token.

The model introduces four major changes:

– Gated DeltaNet + Qwen Sparse Attention – Gated Residual connections – N-gram embeddings – A Muon-based training recipe

Qwen says it required just one-ninth (1/9!!) of Qwen3.7-Plus’s training cost, while scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro and 73.9 on CoWorkBench.

Its new architecture combines Gated DeltaNet with sparse attention that selects small context blocks instead of attending across the entire sequence. Qwen reports up to 7.6× faster prefill and 4.9× faster decoding at a one-million-token context length.

so in sum: Qwen’s early preview of Qwen4 shows where model scaling is heading: more parameters, far less compute per token.

At this point, I’m just as excited about Chinese open-source releases as I am about new OpenAI models.