Qwen3.8-Flash-Next matches a 397B predecessor on 8 of 14 benchmarks at a ninth of the training FLOPs.

The architecture report describes a 125B sparse MoE activating 6B parameters per token, plus 51B of n-gram embedding tables held off the accelerator in host memory with prefetching (arXiv 2608.30320). Against the prior 397B-A17B model it leads on 8 of 14 pre-training benchmarks and trails by at most 2.6 points elsewhere, on a third of the activated parameters, a third of the training tokens and about a ninth of the FLOPs. The design mixes Gated DeltaNet with one full-attention layer per four, adds Qwen Sparse Attention for continued pretraining and a four-branch Gated Residual stream. Pairing it with Muon shifts optimal learning rate and batch size upward and removes the need for batch-size warmup.