Qwen3-Omni Technical Report
We present Qwen3-Omni, a single multimodal model that, for the first time,
maintains state-of-the-art performance across text, image, audio, and video
without any degradation relative to single-modal counterparts. Qwen3-Omni
matches the performance of same-sized single-modal models within the Qwen
series and excels particularly on audio tasks. Across 36 audio and audio-visual
benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall
SOTA on 22, outperforming strong closed-source models such as Gemini-2.5-Pro,
Seed-ASR, and GPT-4o-Transcribe. Qwen3-Omni adopts a Thinker-Talker MoE
architecture that unifies perception and generation across text, images, audio,
and video, yielding fluent text and natural real-time speech. It supports text
interaction in 119 languages, speech understanding in 19 languages, and speech
generation in 10 languages. To reduce first-packet latency in streaming
synthesis, Talker autoregressively predicts discrete speech codecs using a
multi-codebook scheme. Leveraging the representational capacity of these
codebooks, we replace computationally intensive block-wise diffusion with a
lightweight causal ConvNet, enabling streaming from the first codec frame. In
cold-start settings, Qwen3-Omni achieves a theoretical end-to-end first-packet
latency of 234 ms. To further strengthen multimodal reasoning, we introduce a
Thinking model that explicitly reasons over inputs from any modality. Since the
research community currently lacks a general-purpose audio captioning model, we
fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which
produces detailed, low-hallucination captions for arbitrary audio inputs.
Qwen3-Omni-30B-A3B, Qwen3-Omni-30B-A3B-Thinking, and
Qwen3-Omni-30B-A3B-Captioner are publicly released under the Apache 2.0
license.
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Jin Xu
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Zhifang Guo
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2025-09-23T10:10:28.512Z
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GuoZhifang
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ZhifangGuo
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Hangrui Hu
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Yunfei Chu
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2025-09-23T10:10:44.908Z
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Yunfei Chu
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faychu
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Xiong Wang
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Jinzheng He
admin_assigned
2025-09-23T10:10:52.346Z
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Jinzheng He
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Yuxuan Wang
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Xian Shi
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Ting He
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Xinfa Zhu
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Yuanjun Lv
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Yongqi Wang
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2025-09-23T10:11:22.199Z
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Yongqi Wang
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Cyanbox
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Dake Guo
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He Wang
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Linhan Ma
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2025-09-23T10:11:37.816Z
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Linhan Ma
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Lhma-aslp
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Pei Zhang
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Xinyu Zhang
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Hongkun Hao
admin_assigned
2025-09-23T10:11:50.894Z
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hongkun hao
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Zishan Guo
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guozishan
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Baosong Yang
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Baosong Yang
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Bin Zhang
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Ziyang Ma
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2025-09-23T10:12:04.210Z
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Ziyang Ma
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BoJack
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Xipin Wei
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Shuai Bai
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Keqin Chen
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2025-09-23T10:12:38.284Z
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Keqin Chen
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chenkq
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Xuejing Liu
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Peng Wang
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Mingkun Yang
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2025-09-23T10:12:49.920Z
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Mingkun Yang
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Dayiheng Liu
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Dayiheng Liu
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Losin94
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Xingzhang Ren
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Bo Zheng
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Rui Men
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Fan Zhou
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Bowen Yu
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Jianxin Yang
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2025-09-23T10:10:19.273Z
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jianxin Yang
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yangjianxin
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Le Yu
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Jingren Zhou
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Zhou
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Junyang Lin
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Junyang Lin
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JustinLin610
Qwen3-Omni Technical Report
We present Qwen3-Omni, a single multimodal model that, for the first time,
maintains state-of-the-art performance across text, image, audio, and video
without any degradation relative to single-modal counterparts. Qwen3-Omni
matches the performance of same-sized single-modal models within the Qwen
series and excels particularly on audio tasks. Across 36 audio and audio-visual
benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall
SOTA on 22, outperforming strong closed-source models such as Gemini-2.5-Pro,
Seed-ASR, and GPT-4o-Transcribe. Qwen3-Omni adopts a Thinker-Talker MoE
architecture that unifies perception and generation across text, images, audio,
and video, yielding fluent text and natural real-time speech. It supports text
interaction in 119 languages, speech understanding in 19 languages, and speech
generation in 10 languages. To reduce first-packet latency in streaming
synthesis, Talker autoregressively predicts discrete speech codecs using a
multi-codebook scheme. Leveraging the representational capacity of these
codebooks, we replace computationally intensive block-wise diffusion with a
lightweight causal ConvNet, enabling streaming from the first codec frame. In
cold-start settings, Qwen3-Omni achieves a theoretical end-to-end first-packet
latency of 234 ms. To further strengthen multimodal reasoning, we introduce a
Thinking model that explicitly reasons over inputs from any modality. Since the
research community currently lacks a general-purpose audio captioning model, we
fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which
produces detailed, low-hallucination captions for arbitrary audio inputs.
Qwen3-Omni-30B-A3B, Qwen3-Omni-30B-A3B-Thinking, and
Qwen3-Omni-30B-A3B-Captioner are publicly released under the Apache 2.0
license.
Qwen3-Omni, a multimodal model, achieves state-of-the-art performance across text, image, audio, and video, using a Thinker-Talker MoE architecture and a lightweight causal ConvNet for efficient streaming synthesis.
multimodal model Thinker-Talker MoE architecture audio tasks audio-visual benchmarks open-source SOTA closed-source models Gemini-2.5-Pro Seed-ASR GPT-4o-Transcribe fluent text natural real-time speech text interaction speech understanding speech generation first-packet latency Talker discrete speech codecs multi-codebook scheme block-wise diffusion causal ConvNet cold-start settings multimodal reasoning Thinking model audio captioning model Qwen3-Omni-30B-A3B Qwen3-Omni-30B-A3B-Thinking Qwen3-Omni-30B-A3B-Captioner