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

2026-04-29

AI Summary

Current foundation models lack native capabilities for perceiving and acting upon multimodal contexts such as images, videos, and web pages, hindering the development of truly multimodal agents. This work proposes a native foundation model architecture that treats multimodal perception as a core component—rather than a peripheral interface—by deeply integrating perception into the entire pipeline of reasoning, planning, tool use, and execution. Through joint optimization via multimodal training, reinforcement learning, and agent frameworks, the model adopts an end-to-end hierarchical design. It demonstrates exceptional performance in multimodal programming, visual tool invocation, and structured task execution, while preserving strong text-only capabilities, thereby offering a systematic paradigm for building multimodal agents.

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.

Problem

• multimodal agents
• foundation model
• multimodal perception
• agentic capability
• heterogeneous contexts

Innovation

• multimodal agents
• native foundation model
• multimodal perception
• tool use
• hierarchical optimization