AITOPAI热报
ModelsSelected78°

Qwen3.8-Omni-Flash, Qwen's first omni-modal model with agentic capabilities

🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native...

官方账号阿里通义 Qwen@Alibaba_Qwen
Post
View the post
Why we picked it

For users needing to process long-form audio-video content, Qwen3.8-Omni-Flash is more affordable than its predecessor, with a 89% reduction in video input costs.

Alibaba's Qwen3.8-Omni-Flash is a new model that combines audio-video understanding with reasoning and tool use. It approaches Gemini 3.8 Flash in audio-video capabilities and scored +19.5 points on average in agent performance across WildClawBench-MM & UniClawBench. It uses 1M-token context for agentic perception and reduces video input costs by about 89% compared with Qwen3.5-Omni-Plus.

Original · 阿里通义 Qwen

🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native...

🚀 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! 👀 - Bl qwen.ai/blog?id=qwen3.… kqYF - Qwenclo qwencloud.com/models/qwen3.8… LAnD - Qwen Stud chat.qwen.ai aVNZ - A alibabacloud.com/help/en/model-… 5YUt - Qwen-MM-Plugi github.com/QwenLM/Qwen-MM… DP3d - Qwen-Live Harness: coming s github.com/QwenLM/Qwen-Li… Ibdy 💬 46 🔄 74 ❤️ 534 👀 18989 📊 129 ⚡

#Qwen3.8-Omni-Flash#omni-modal#audio-video#agentic models#Qwen#Alibaba

Keep reading

Recent stories with the same tag or section
ModelsUS vs ChinaSelected73°03:56
Qwen3.8-Omni-Flash: Qwen's multimodal model for long-horizon agent tasks

Omni-modal models are shifting from perception demos to executing multi-step agent work on audio and video, a capability most Western agent stacks still lack. Builders can plug Qwen-MM-Plugins into existing harnesses or use Qwen-Live-Harness for real-time pipelines without building memory and tool routing from scratch. As an open release from Alibaba (Qwen), it gives developers in Southeast Asia and beyond a free alternative to closed multimodal APIs.

#Qwen3.8-Omni-Flash#Qwen#open-source models#multimodal
DAIR.AI@dair_aiSource
ModelsUS vs ChinaSelected73°03:16
Qwen3.8-Omni-Flash targets long-horizon multimodal agent tasks

Shipping agent harnesses and plugins with the model means developers can wire real-time audio and video into existing frameworks without building the plumbing themselves. The one-million-token context window makes long-form video and audio workloads practical for teams building editors, translators or live assistants. Builders comparing open multimodal options against closed APIs should benchmark Qwen3.8-Omni-Flash's agent performance on their own tasks.

#Qwen3.8-Omni-Flash#Qwen#multimodal#agents
DAIR.AI@dair_aiSource