# Qwen Team presents natively multimodal Qwen3.8-Omni-Flash

Qwen3.8-Omni-Flash is trained for long-horizon agent tasks across text, audio, and video, using the Qwen3.8-Next sparse mixture-of-experts design and a one-million-token context window. Two open-source frameworks, Qwen-MM-Plugins and Qwen-Live-Harness, add multimodal and real-time agent support.

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Source: DAIR.AI · Published 9/25/2026, 12:16:45

The Qwen Team presents Qwen3.8-Omni-Flash, a natively multimodal model trained for long-horizon agent tasks across text, audio, and video, including video editing and long-form audio and video translation. It uses the sparse mixture-of-experts design of Qwen3.8-Next and 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 the model. 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. The [Qwen3.8-Omni paper](https://academy.dair.ai/papers/qwen3-8-omni-towards-native-omni-modal-agents-2609.25611) covers the model.

Tags: Qwen, Multimodal, AI agents, Open source

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