LongCat-Flash-Omni Technical Report
We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal
model with 560 billion parameters, excelling at real-time audio-visual
interaction. By adopting a curriculum-inspired progressive training strategy
that transitions from simpler to increasingly complex modality sequence
modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal
capabilities while maintaining strong unimodal capability. Building upon
LongCat-Flash, which adopts a high-performance Shortcut-connected
Mixture-of-Experts (MoE) architecture with zero-computation experts,
LongCat-Flash-Omni integrates efficient multimodal perception and speech
reconstruction modules. Despite its immense size of 560B parameters (with 27B
activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual
interaction. For training infrastructure, we developed a modality-decoupled
parallelism scheme specifically designed to manage the data and model
heterogeneity inherent in large-scale multimodal training. This innovative
approach demonstrates exceptional efficiency by sustaining over 90% of the
throughput achieved by text-only training. Extensive evaluations show that
LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal
benchmarks among open-source models. Furthermore, it delivers highly
competitive results across a wide range of modality-specific tasks, including
text, image, and video understanding, as well as audio understanding and
generation. We provide a comprehensive overview of the model architecture
design, training procedures, and data strategies, and open-source the model to
foster future research and development in the community.
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Meituan LongCat Team
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Bairui Wang
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Bayan
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Bin Xiao
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Bo Zhang
690979c6812eca10f9cc627d
Bolin Rong
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Borun Chen
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Chang Wan
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Chao Zhang
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Chen Huang
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Chen Chen
claimed_verified
2025-11-10T09:32:37.488Z
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Chen Chen
xiaoyan001
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xiaoyan001
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Chen Chen
claimed_verified
2026-01-13T08:25:05.207Z
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chenchen
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user
chenchen2121
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Chengxu Yang
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Chengzuo Yang
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Cong Han
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Dandan Peng
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Delian Ruan
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Detai Xin
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Disong Wang
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Dongchao Yang
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Fanfan Liu
claimed_verified
2026-02-05T10:57:59.013Z
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Fanfan Liu
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Fengjiao Chen
claimed_verified
2025-11-05T16:25:04.231Z
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Fengjiao Chen
Fengjiao
user
Fengjiao
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Fengyu Yang
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Gan Dong
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Gang Huang
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Gang Xu
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Guanglu Wan
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Guoqiang Tan
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Guoqiao Yu
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Haibo Qiu
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Hao Lu
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Hongbo Liu
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Hongyu Xiang
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Jiaheng Wu
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Jian Yang
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Jiaxing Liu
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Jing Huang
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Jingang Wang
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Jinrui Ding
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Juchao Jiang
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Jun Kuang
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2026-04-15T12:10:45.464Z
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Jun Kuang
JKuang96
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JKuang96
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Jun Wang
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Junhui Mei
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Ke Ding
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Kefeng Zhang
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Lei Chen
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Liang Shi
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Limeng Qiao
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Liming Zheng
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Lin Ma
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Liuyang Guo
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Liya Ma
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Luying Sun
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Man Gao
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Mengshen Zhu
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Miao Cao
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Minliang Lin
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Nuo Xu
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Peng Shi
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Qi Zhang
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Qian Fang
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Qian Wang
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Qian Yang
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Quanxiu Wang
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Rongxiang Weng
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Rongxin Guo
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Ruoxuan Liang
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Senbin Yang
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Shanbo Xu
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Shanglin Lei
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Shengze Ye
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Shimin Chen
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Shuaiqi Chen
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Shujie Hu
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Shuo Li
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Siqi Yang
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Siyu Xu
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Siyu Ren
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Song Li
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Songxiang Liu
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Tianhao Bai
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Tianye Dai
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Wei Hong
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Wei Wang
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Weixiao Zhao
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Wengang Cao
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Wenlong Zhu
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Wenlong He
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Xi Su
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Xi Nan
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Xiaohan Zhao
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Xiaohao Wang
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Xiaoyu Zhao
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Xiaoyu Wang
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Xiaoyu Li
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Xin Pan
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Xin Chen
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Xiusong Sun
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Xu Xiang
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Xudong Xing
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Xuezhi Cao
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Xunliang Cai
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Yang Yang
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Yanli Tan
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Yao Yao
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Yerui Sun
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Yi Chen
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Yifan Lu
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Yin Gong
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Yining Zhang
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Yitian Chen
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Yiyang Gan
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Yuchen Tang
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Yuchen Xie
690979c6812eca10f9cc62ea
Yueqian Wang
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Yuewen Zheng
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Yufei Zhang
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Yufeng Zhong
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Yulei Qian
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Yuqi Peng
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Yuwei Jiang
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Zeyang Hu
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Zheng Zhang
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Zhengkun Tian
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Zhiqing Hong
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Zhixiong Zeng
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Zhuqi Mi
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Ziran Li
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Ziwen Wang
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Ziyi Zhao
690979c6812eca10f9cc62fa
Ziyuan Zhuang
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Zizhe Zhao
LongCat-Flash-Omni Technical Report
We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal
model with 560 billion parameters, excelling at real-time audio-visual
interaction. By adopting a curriculum-inspired progressive training strategy
that transitions from simpler to increasingly complex modality sequence
modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal
capabilities while maintaining strong unimodal capability. Building upon
LongCat-Flash, which adopts a high-performance Shortcut-connected
Mixture-of-Experts (MoE) architecture with zero-computation experts,
LongCat-Flash-Omni integrates efficient multimodal perception and speech
reconstruction modules. Despite its immense size of 560B parameters (with 27B
activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual
interaction. For training infrastructure, we developed a modality-decoupled
parallelism scheme specifically designed to manage the data and model
heterogeneity inherent in large-scale multimodal training. This innovative
approach demonstrates exceptional efficiency by sustaining over 90% of the
throughput achieved by text-only training. Extensive evaluations show that
LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal
benchmarks among open-source models. Furthermore, it delivers highly
competitive results across a wide range of modality-specific tasks, including
text, image, and video understanding, as well as audio understanding and
generation. We provide a comprehensive overview of the model architecture
design, training procedures, and data strategies, and open-source the model to
foster future research and development in the community.
LongCat-Flash-Omni, a 560 billion parameter omni-modal model, achieves real-time audio-visual interaction through curriculum-inspired training and modality-decoupled parallelism.
curriculum-inspired progressive training Shortcut-connected Mixture-of-Experts (MoE) zero-computation experts multimodal perception speech reconstruction modality-decoupled parallelism throughput omni-modal benchmarks text understanding image understanding video understanding audio understanding audio generation