GLM-4.5 is designed as a foundational model for agent-oriented applications, using a Mixture-of-Experts architecture with 355B total parameters and 32B active parameters per forward pass, paired alongside the lighter GLM-4.5-Air variant at 106B total and 12B active parameters. Training combines a 15-trillion-token general-domain pretraining phase with targeted fine-tuning on code, reasoning, and agent-specific datasets, followed by reinforcement learning to sharpen reasoning, coding, and agent performance, all feeding into a 128K-token context window.

The model is optimized for tool invocation, web browsing, software engineering, and front-end development, fitting naturally into code-centric agent environments such as Claude Code and Roo Code while still supporting broader agent applications through tool-calling APIs. A hybrid reasoning design lets it switch between a Thinking Mode for complex reasoning and tool use and a Non-Thinking Mode for fast responses, giving developers control over latency versus depth. Open-weight availability under Z.AI makes GLM-4.5 a practical base for teams building agentic systems who want to self-host or fine-tune while still accessing frontier-style reasoning and coding behavior.