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Models/Language/GLM-5.2 (max)
Z AI logo
Z AI
Open Weights

GLM-5.2 (max)

Released Jun 2026

View Rankings
Hugging Face
Hugging Face
z.ai
Intelligence
#16
Coding
#20
Arena AI
#30
Context1M
Parameters744B

GLM-5.2 is a flagship large language model developed by Z.ai (formerly Zhipu AI) and released in June 2026. Optimized for "agentic" intelligence, the model is designed to handle long-horizon tasks such as repository-scale software engineering, multi-step autonomous planning, and complex system architecture. It follows the lineage of the GLM (General Language Model) series and is positioned as a high-performance open-weight alternative to proprietary frontier models.

The model utilizes a Mixture-of-Experts (MoE) architecture with approximately 744 billion total parameters, of which roughly 40 billion are active during any single forward pass. This sparse design enables the model to maintain computational efficiency while providing the reasoning depth of a trillion-parameter scale system. GLM-5.2 is notable for its 1 million token context window, supported by architectural optimizations like IndexShare, which reuses indexers across sparse attention layers to reduce FLOPs during long-context processing.

Thinking Modes and Engineering Focus

A defining feature of GLM-5.2 is its dual reasoning effort system, which includes High and Max modes. The Max effort level is specifically tuned for challenging coding and logic tasks, allowing the model to allocate additional internal computation to plan, verify, and self-correct across long execution traces. It supports a massive output capacity of up to 131,072 tokens in a single response, facilitating the generation of entire codebases or exhaustive technical documentation.

GLM-5.2 was trained entirely on domestic Huawei Ascend hardware, signaling a milestone in self-reliant AI infrastructure. It is released under a permissive MIT License, enabling unrestricted commercial use and local hosting. On independent benchmarks like SWE-bench Pro and Terminal-Bench 2.1, the model has demonstrated performance competitive with major proprietary models, making it a primary choice for developers seeking open-weight agentic capabilities.

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