GLM-5.2: Benchmarks, Architecture and How to Run It
The article reviews GLM-5.2, covering its benchmark performance, architectural details, and a guide on how to run the model. It provides an overview of how GLM-5.2 compares to other LLMs and offers practical steps for local deployment and usage.
Background
- GLM (General Language Model) is a series of large language models by Zhipu AI (Beijing), a top Chinese AI firm founded by Tsinghua University alumni and a major rival to OpenAI, Anthropic, and Meta.
- GLM-5.2 is the latest version. Its key innovation: a "Mixture of Experts" (MoE) architecture that activates only a fraction of its parameters per query, achieving strong performance at lower computational cost than a dense model of comparable size.
- The model reportedly uses extensive reinforcement learning from human feedback (RLHF) and chain-of-thought training data to improve reasoning and factuality over prior GLM versions.
- For English-speaking readers: Zhipu's models are less known in the West than GPT or Claude, but GLM-5.2 signals the rapidly advancing Chinese AI frontier. Its benchmark scores on tests like MMLU, MATH, and HumanEval offer a window into how Chinese models stack up against leading US alternatives.