AI HOT (Curated Pool)Sep 18, 2026, 01:18 UTC+8

Qwen launches Qwen3.8-Omni-Flash, a native omnimodal model built for audio-visual agent workflows

Qwen 发布原生全模态模型 Qwen3.8-Omni-Flash，主打音视频智能体任务交付

Qwen3.8-Omni-Flash is a native omnimodal model that shifts focus from audio-visual understanding to task planning, tool use, and delivery in real-world workflows. It supports a 1M-token context window, with average scores across 29 evals up over 25% vs Qwen3.5-Omni-Plus. API pricing for audio input dropped over 98%, and audio-visual input over 93%. It gained 36.5 points on WildClawBench-MM and 22.3 on AgenticVBench; AliMeeting DER fell from 88.11 to 3.35. Qwen claims overall audio performance exceeds Gemini 3.8 Flash, with audio-visual performance close to it. Qwen-Live Harness is open-sourced for real-time interaction, and Qwen-MM-Plugins now supports tool use and workflows for long-form audio and video.

Why it matters: Qwen pushes omnimodal models from understanding to task delivery, backed by concrete benchmarks and pricing. Score stays at 82 rather than higher because it's a launch-day post with no third-party validation or cross-source cluster yet.

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Take

This one's worth opening because Qwen is drawing a clear line: their omnimodal model isn't just about understanding audio and video anymore—it plans tasks, calls tools, and delivers finished work like video edits, MV production, and film commentary. It packs a 1M-token context window, with average scores across 29 evals up over 25% versus the previous Qwen3.5-Omni-Plus. The pricing drop is the wild part: audio input API cost fell over 98%, audio-visual input over 93%.

I'd discount this a bit since it's an official blog and they picked the benchmarks. But a few numbers are genuinely strong: AliMeeting DER crashed from 88.11 to 3.35, which is a massive jump in multi-speaker recognition. WildClawBench-MM gained 36.5 points and AgenticVBench gained 22.3, so the agentic audio-visual claims have some weight. They say overall audio performance beats Gemini 3.8 Flash and audio-visual is close to it—I'd wait for third-party benchmarks before buying that fully.

The thing to watch: they open-sourced Qwen-Live Harness and Qwen-MM-Plugins alongside the model. That's more useful than just dropping weights—it gives you the engineering blueprint for plugging the model into real workflows with tool use and real-time interaction.

Source: AI HOT (Curated Pool) · qwen.ai
Published: Sep 18, 2026, 01:18 UTC+8 · 12 days ago