GLM-5.1 (Reasoning) is a flagship large language model developed by Z.ai (Zhipu AI), released in early 2026 as a post-training refinement of the GLM-5 architecture. Specifically engineered for agentic engineering and complex problem-solving, the model is designed to handle long-horizon tasks that require sustained focus and iterative optimization. It introduces a "Thinking Mode" by default, which allows the model to perform multi-step internal reasoning before providing an output, similar to "System 2" thinking processes.
Technical Architecture
The model utilizes a Mixture-of-Experts (MoE) Transformer architecture with a total of 744 billion parameters, of which approximately 40 billion are active per token during inference. To maintain efficiency at this scale, it incorporates Multi-head Latent Attention (MLA) and Dynamic Sparse Attention (DSA). GLM-5.1 supports a context window of 202,752 tokens, enabling it to process extensive codebases, long documentation, and complex multi-turn execution logs without significant performance degradation.
Agentic and Reasoning Capabilities
GLM-5.1 is optimized for autonomous execution loops, with the capability to work continuously on a single task for up to eight hours. It excels in software engineering benchmarks, notably achieving top-tier scores on SWE-Bench Pro and Terminal-Bench 2.0. Its reasoning capabilities are demonstrated by high performance on mathematical and logic evaluations, such as a 95.3% score on AIME 2026 and an 86.8% score on GPQA. The model is trained to manage a "plan, execute, test, fix, and optimize" cycle autonomously, identifying blockers and revising strategies through repeated iteration.
Prompting and Usage
Thinking behavior is enabled by default in GLM-5.1 to ensure high-quality reasoning. For general tasks, official recommendations suggest using a temperature of 1.0 and top_p of 0.95. For specialized terminal-based or command-line tasks, a lower temperature of 0.7 and a top_p of 1.0 are often preferred to maintain precision in syntax and logical steps. The model supports structured JSON outputs and function calling, which are critical for its integration into agentic frameworks.