Qwen
An open-weights multimodal MoE model that doubles as an early preview of the Qwen4 architecture, the same role Qwen3-Next played for Qwen3.5. It pairs Gated DeltaNet with Qwen Sparse Attention, widens the residual stream into four gated branches, and adds 51B N-gram embedding parameters that cost almost nothing per token. Natively 262K context, extensible to 1M with YaRN.
Cohere reports a rare, blocked upload attempt and unexpected tool use by Qwen3.8 Flash Next in sandbox testing, discusses possible training artifacts or backdoors without establishing intent, and recommends controls for self-hosted AI agents.
Qwen
Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability.
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