阿里千问上线原生全模态模型 Qwen3.8-Omni-Flash,支持文本、图片、音频、视频和 1M 上下文。
这代明显加强了音视频 Agent 能力。模型可以自己从视频里找信息、规划任务,再调用外部工具完成剪辑、配音和渲染。官方展示了长会议处理、短剧翻译、电影解说、MV 制作和视频转图文笔记等场景。
面对几小时的长视频,它也不用从头到尾全部处理。模型会先判断答案可能在哪,再逐步定位关键片段。OmniVideoBench 上,准确率从 63.4 提到 67.8,Token 消耗从 145,736 降到 79,117,少了 45.7%。
千问称,Qwen3.8-Omni-Flash 在 30 项评测上的平均成绩比 Qwen3.5-Omni-Plus 提升超过 26%,音视频能力接近 Gemini 3.8 Flash,音频能力整体超过后者。音频输入成本下降超过 98%,音视频输入下降超过 93%。
目前 Qwen3.8-Omni-Flash 模型权重尚未公开,只开放了 API。千问同时开源了 Qwen-MM-Plugins,可以装进 Claude Code、Codex、Gemini CLI、Qwen Code 等 Agent,给它们补上图片、音频、长视频处理和视频剪辑能力。
另一套 Qwen-Live Harness 更像实时调度层。用户可以直接和实时全模态模型说话,再把复杂任务交给 Gemini CLI、Claude Code、Codex 等后台 Agent 持续执行。它会跟踪任务进度,做完后再主动回来告诉你结果。
🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities!
Native audio-video understanding, reasoning, and tool use come together in one model: understand the content, plan the task, execute with tools, and deliver the result.
Highlights: 🥳
- Audio-video intelligence that gets things done: jointly reason over what's seen and heard, and orchestrate tools across long workflows to auto-edit vlogs, translate short videos, and turn movies into recaps.
- A major leap: approaching Gemini 3.8 Flash in audio-video capabilities; +19.5 points on average in agent performance across WildClawBench-MM & UniClawBench.
- 1M-token context with agentic perception: actively explore long videos and locate key moments with higher accuracy, using 51.8% fewer tokens than static understanding on OmniVideoBench.
Video input costs are reduced by about 89% compared with Qwen3.5-Omni-Plus, making long-form audio-video understanding and agentic workflows more affordable than ever.
To help you build apps around Omni, we're also open-sourcing Qwen-MM-Plugins and Qwen-Live Harness! 🛠️
We can't wait to see what you build with Qwen3.8-Omni-Flash! 👀
- Blog:
- Qwencloud:
- Qwen Studio:
- API:
- Qwen-MM-Plugins:
- Qwen-Live Harness: coming soon
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