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