Alibaba’s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture
Alibaba’s Qwen team has released Qwen3.8-Flash-Next, an open-weight multimodal Mixture-of-Experts model built for cost per token. The checkpoint pairs a 125B backbone with a 51B N-gram embedding table and a 4B multi-token prediction module. Only 6B parameters activate per token. The team positions it as an early preview of the architecture that will underpin Qwen4, the same role Qwen3-Next played for Qwen3.5. Four changes carry the release: a Gated DeltaNet and Qwen Sparse Attention hybri
Alibaba's Qwen team released a multimodal AI model designed for cost efficiency, pairing a 125 billion parameter backbone with specialized components that keep only 6 billion parameters active per token. The model uses four architectural innovations: a hybrid attention system combining linear-attention layers with sparse attention, gated residual connections, an embedding table for bigram and trigram lookups, and a new optimizer called Muon. This release serves as an early preview of the architecture planned for a future version, similar to how an earlier release previewed a previous version. The model requires substantial computing resources to run, needing multi-GPU nodes rather than single machines, though it cost roughly one-ninth as much to train as a comparable prior model.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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