Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal
  Perception and Generation
Authors: name: Inclusion AI, name: Bowen Ma, name: Cheng Zou, name: Canxiang Yan, name: Chunxiang Jin, name: Chunjie Shen, name: Dandan Zheng, name: Fudong Wang, name: Furong Xu, name: GuangMing Yao, name: Jun Zhou, name: Jingdong Chen, name: Jianing Li, name: Jianxin Sun, name: Jiajia Liu, name: Jianjiang Zhu, name: Jianping Jiang, name: Jun Peng, name: Kaixiang Ji, name: Kaimeng Ren, name: Libin Wang, name: Lixiang Ru, name: Longhua Tan, name: Lan Wang, name: Mochen Bai, name: Ning Gao, name: Qingpei Guo, name: Qinglong Zhang, name: Qiang Xu, name: Rui Liu, name: Ruijie Xiong, name: Ruobing Zheng, name: Sirui Gao, name: Tianqi Li, name: Tinghao Liu, name: Weilong Chai, name: Xinyu Xiao, name: Xiaomei Wang, name: Xiaolong Wang, name: Xiao Lu, name: Xiaoyu Li, name: Xingning Dong, name: Xuzheng Yu, name: Yi Yuan, name: Yuting Gao, name: Yuting Xiao, name: Yunxiao Sun, name: Yipeng Chen, name: Yifan Mao, name: Yifei Wu, name: Yongjie Lyu, name: Ziping Ma, name: Zhiqiang Fang, name: Zhihao Qiu, name: Ziyuan Huang, name: Zizheng Yang, name: Zhengyu He
Organization: inclusionAI

We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a
sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion
total parameters, of which only 6.1 billion are active per token. This
architecture enables highly efficient scaling (dramatically improving
computational efficiency while significantly expanding model capacity) and
empowers stronger unified multimodal intelligence across vision, speech, and
language, representing a key step toward Artificial General Intelligence (AGI).
Compared to its predecessor, the upgraded version exhibits substantial
improvements across multimodal understanding and generation. We significantly
advance speech recognition capabilities, achieving state-of-the-art performance
in contextual ASR and highly competitive results in dialect-aware ASR. In image
generation, Ming-Flash-Omni introduces high-fidelity text rendering and
demonstrates marked gains in scene consistency and identity preservation during
image editing. Furthermore, Ming-Flash-Omni introduces generative segmentation,
a capability that not only achieves strong standalone segmentation performance
but also enhances spatial control in image generation and improves editing
consistency. Notably, Ming-Flash-Omni achieves state-of-the-art results in
text-to-image generation and generative segmentation, and sets new records on
all 12 contextual ASR benchmarks, all within a single unified architecture.