GLM 4.5

GLM-4.5 was designed from the ground up as an agent-native foundation model, bringing together reasoning, coding, and tool-use capabilities in a unified architecture. Built on a Mixture of Experts framework that activates 32 billion of its 355 billion total parameters, it delivers efficient inference while maintaining deep computational power. The model introduces dual-mode operation—a thinking mode for multi-step reasoning and complex decision-making, and a non-thinking mode for rapid, real-time responses. This flexibility lets developers toggle between depth and speed depending on the task at hand, whether that's orchestrating autonomous agents, refactoring large codebases, or handling multi-turn dialogues with native tool calling.

The training pipeline scales up agentic capabilities through domain-specific and reasoning-focused pretraining, accumulating 30 trillion tokens across general, specialized, and code-focused data. Reinforcement learning further hones its reasoning, coding, and agent alignment—without requiring traditional feedback signals. Technical innovations include Grouped-Query Attention, Rotary Position Embedding for context handling, a Muon optimizer, and Multi-Token Prediction for faster generation. Performance benchmarks reflect this investment: 98.2% on MATH 500, 72.9% on LiveCodeBench, and strong tool selection quality scores on agent leaderboards. The combination of open-weight availability, MIT licensing, and a design philosophy centered on autonomous agent workloads positions GLM-4.5 as a compelling option for developers building self-improving agents, automated coding pipelines, and complex orchestration systems that need both power and adaptability.