Alibaba’s Qwen team is set to release Qwen 3.8-Flash-Next on Wednesday, according to ChainCatcher. The model is described as a mixture-of-experts, or MoE, system with 125 billion total parameters and 6 billion parameters activated per token, though the report noted those figures have not yet been officially confirmed.
The team is positioning the release as a preview of the next-generation Qwen 4 architecture rather than its final flagship model. The model is also described as multimodal and built on the upcoming Qwen 4 framework.
Qwen said it is releasing this early version ahead of the broader Qwen lineup so developers can start preparing for the full series. Model weights will be hosted on Hugging Face and ModelScope. The report added that Qwen has not yet published benchmark results or comparison data against its own Qwen 3 series or overseas rivals. As an open-weight model, Qwen 3.8-Flash-Next will allow developers to download, fine-tune, and run it without sending data to a closed API, a setup the report said can help reduce hosted model costs.
Alibaba’s Qwen team will release Qwen 3.8-Flash-Next on Wednesday, according to ChainCatcher. The model is described as a mixture-of-experts, or MoE, model with 125 billion total parameters, while only 6 billion parameters are activated for each token.
The team is presenting it as a preview version of the next-generation Qwen 4 architecture, not the final flagship model. The report also described it as multimodal and built on the upcoming Qwen 4 architecture.
Qwen said the early release is intended to help developers prepare for the full Qwen lineup that will follow. Model weights will be hosted on Hugging Face and ModelScope.
So far, Qwen has not disclosed benchmark results for the model and has not released comparison data against its own Qwen 3 series or overseas competitors. The report also said the specific figures of 125 billion and 6 billion parameters have not yet been officially confirmed.
As an open-weight model, developers will be able to download, fine-tune, and run it without sending data to a closed API. According to the report, that could help lower the cost of hosted models.
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