Qwen 团队发布 Qwen3.8-Omni-Flash 全模态模型

模型采用 Qwen3.8-Next 的稀疏 MoE 架构，上下文窗口达 100 万 tokens。

通过共训练策略在扩展智能体能力到音视频的同时保持文本性能。同时开源两个框架：Qwen-MM-Plugins 为现有智能体工具添加音视频支持，Qwen-Live-Harness 支持实时多模态交互、上下文记忆管理、工具调用和子智能体委派。

Era of the omni models is upon us.

This is a great report by the Qwen Team on their omni model.

They present Qwen3.8-Omni-Flash, a natively multimodal model trained for long-horizon agent tasks across text, audio and video, such as video editing and long-form audio and video translation.

It uses the sparse mixture-of-experts design of Qwen3.8-Next with a context window of one million tokens. A co-training strategy keeps text performance while carrying agent skills over to audio and video tasks.

Two open-source frameworks come with it. Qwen-MM-Plugins adds audio and video support to existing agent harnesses, and Qwen-Live-Harness handles real-time multimodal interaction with context and memory management, tool use and sub-agent delegation.

Paper: https://t.co/PxFrmGc5R1

本条情报归属 AITOP「模型」核心栏目，重点聚焦中国 AI 产业与国产大模型生态。在跨区域产业观察坐标中，该动态直接关联「中美大模型技术代差与商业化对照」「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Qwen3.8-Omni-Flash、Qwen、多模态、智能体，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

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