We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI).
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin.
A Generalized Linear Model (GLM) builds on top of linear regression but offers more flexibility. Think of it like this: instead of forcing your data to follow a straight line and assuming everything is normally distributed, GLMs let you customize how the outcome is modeled.
What Is GLM-5.1? GLM-5.1 is the latest model in Z.ai's General Language Model series, released on . It is a post-training upgrade to GLM-5, sharing the same Mixture-of-Experts (MoE) architecture but with substantially improved coding ability, tool use, and sustained autonomous execution 1.
GLM-5.1 is Z.AI's next-generation flagship foundation model designed for long-horizon agentic engineering tasks. Built on a 754B MoE architecture (40B active parameters), it can work continuously and autonomously on a single task for up to 8 hours, completing the full loop from planning and execution to iterative optimization and delivery. GLM-5.1 achieves state-of-the-art on SWE-Bench Pro (58 ...
GLM-5.1 is a flagship LLM for agentic workflows, coding, and long-horizon reasoning tasks.