阿里千问发布 Qwen3.8-Omni-Flash 全模态模型
🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities!
阿里千问发布 Qwen3.8-Omni-Flash，称其为 Qwen 首个围绕智能体能力构建的全模态模型，将原生音视频理解、推理与工具调用整合在一个模型中。官方称其在音视频能力上接近 Gemini 3.8 Flash，在 WildClawBench-MM 与 UniClawBench 上智能体性能平均提升 19.5 分；支持 1M token 上下文，在 OmniVideoBench 上比静态理解少用 51.8% token，视频输入成本较 Qwen3.5-Omni-Plus 降低约 89%。同时开源 Qwen-MM-Plugins，Qwen-Live Harness 即将发布。
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🚀 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! 👀