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arXiv · 2604.26752

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

V Team·Wenyi Hong·Xiaotao Gu·Ziyang Pan·Zhen Yang·Yuting Wang·Yue Wang·Yuanchang Yue·Yu Wang·Yanling Wang·Yan Wang·Xijun Liu·Wenmeng Yu·Weihan Wang·Wei Li·Shuaiqi Duan·Sheng Yang·Ruiliang Lv·Mingdao Liu·Lihang Pan·Ke Ning·Junhui Ji·Jinjiang Wang·Jing Chen·Jiazheng Xu·Jiale Zhu·Jiale Cheng·Ji Qi·Guobing Gan·Guo Wang·Cong Yao·Zijun Dou·Zihao Zhou·Zihan Wang·Zhiqi Ge·Zhijie Li·Zhenyu Hou·Zhao Xue·Zehui Wang·Zehan Qi·Zehai He·Yutao Zhang·Yusen Liu·Yukuo Cen·Yuchen Li·Yuan Wang·Yu Yang·Yongbin Liu·Yijian Lu·Yifan Xu·Yanzi Wang·Yanxiao Zhao·Yanfeng Wang·Yadong Xue·Yabo Xu·Xinyu Zhang·Xinyu Liu·Xiao Liu·Wenyi Zhao·Wenkai Li·Tianyu Tong·Tianshu Zhang·Shudan Zhang·Shengdong Yan·Qinkai Zheng·Mingde Xu·Licheng Bao·lat Long long·Jiaxing Xu·Jiaxin Fan·Jiawen Qian·Jiali Chen·Jiahui Lin·Jiadai Sun·Haozhi Zheng·Haoran Wang·Haochen Li·Hanyu Lai·Han Xu·Fan Yang·Dan Zhang·Da Yin·Chuangxin Zhao·Chengcheng Wu·Boyan Shi·Bowen Lv·Bowei Jia·Bo Li·Bin Chen·Baoxu Wang·Peng Zhang·Debing Liu·Bin Xu·Juanzi Li·Minlie Huang·Yuxiao Dong·Jie Tang
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Abstract

We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.

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V Team, Wenyi Hong, Xiaotao Gu, Ziyang Pan, Zhen Yang, Yuting Wang, Yue Wang, Yuanchang Yue, Yu Wang, Yanling Wang, Yan Wang, Xijun Liu, Wenmeng Yu, Weihan Wang, Wei Li, Shuaiqi Duan, Sheng Yang, Ruiliang Lv, Mingdao Liu, Lihang Pan, Ke Ning, Junhui Ji, Jinjiang Wang, Jing Chen, Jiazheng Xu, Jiale Zhu, Jiale Cheng, Ji Qi, Guobing Gan, Guo Wang, Cong Yao, Zijun Dou, Zihao Zhou, Zihan Wang, Zhiqi Ge, Zhijie Li, Zhenyu Hou, Zhao Xue, Zehui Wang, Zehan Qi, Zehai He, Yutao Zhang, Yusen Liu, Yukuo Cen, Yuchen Li, Yuan Wang, Yu Yang, Yongbin Liu, Yijian Lu, Yifan Xu, Yanzi Wang, Yanxiao Zhao, Yanfeng Wang, Yadong Xue, Yabo Xu, Xinyu Zhang, Xinyu Liu, Xiao Liu, Wenyi Zhao, Wenkai Li, Tianyu Tong, Tianshu Zhang, Shudan Zhang, Shengdong Yan, Qinkai Zheng, Mingde Xu, Licheng Bao, lat Long long, Jiaxing Xu, Jiaxin Fan, Jiawen Qian, Jiali Chen, Jiahui Lin, Jiadai Sun, Haozhi Zheng, Haoran Wang, Haochen Li, Hanyu Lai, Han Xu, Fan Yang, Dan Zhang, Da Yin, Chuangxin Zhao, Chengcheng Wu, Boyan Shi, Bowen Lv, Bowei Jia, Bo Li, Bin Chen, Baoxu Wang, Peng Zhang, Debing Liu, Bin Xu, Juanzi Li, Minlie Huang, Yuxiao Dong, Jie Tang. 2026-04-29. GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents. https://arxiv.org/abs/2604.26752

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