GLM-5

GLM-5 represents a major architectural leap from its predecessor, moving to a 744 billion parameter Mixture of Experts design that activates 40 billion parameters per forward pass. This scale-up was paired with DeepSeek Sparse Attention integration, which helps keep deployment costs manageable while preserving the model's long-context capacity. The architecture is explicitly engineered for complex systems engineering and extended agentic workflows, where the model must maintain coherence across many sequential steps and handle intricate, multi-stage tasks.

The development team built a custom reinforcement learning infrastructure called SLIME to push post-training beyond conventional methods, enabling finer-grained iterations that close the gap between a model's base competence and peak performance. On established benchmarks for coding, reasoning, and agentic tasks, GLM-5 achieves best-in-class results among all open-source models, putting it within reach of frontier closed models on practical engineering problems. This positioning makes it particularly well-suited for developers and organizations seeking capable autonomous AI without relying on proprietary APIs.