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.

翻译：我们提出GLM-5V-Turbo，这是迈向多模态智能体原生基础模型的一步。随着基础模型越来越多地部署在真实环境中，智能体能力不仅依赖于语言推理，还依赖于感知、解释和作用于图像、视频、网页、文档及图形用户界面（GUI）等异构上下文的能力。GLM-5V-Turbo围绕这一目标构建：多模态感知被整合为推理、规划、工具使用和执行的核心组件，而非作为语言模型的辅助接口。本报告总结了GLM-5V-Turbo在模型设计、多模态训练、强化学习、工具链扩展以及与智能体框架集成方面的主要改进。这些进展使得该模型在多模态编码、视觉工具使用及基于框架的智能体任务中展现出强大性能，同时保持了具有竞争力的纯文本编码能力。更重要的是，我们的开发过程为构建多模态智能体提供了实践洞见，凸显了多模态感知、分层优化以及可靠端到端验证的核心作用。