GLM LLM Models

With 28.5T tokens of training data and stellar benchmark results, GLM-5 is widely regarded as the "ceiling of open-source." It rivals or exceeds top-tier global commercial models in capacity and logic, providing a powerful, high-performance foundation for the global developer ecosystem.

HLE is a high-difficulty benchmark designed to test if AI possesses expert-level human knowledge and reasoning. GLM-5 achieving the top score signifies that its mastery of frontier science and complex logic has reached or surpassed the level of leading closed-source models.

BrowseComp is a definitive leaderboard for "Agentic" capabilities, focusing on complex task planning and execution in real-world web environments. The highest score represents GLM-5’s ability to autonomously navigate browsers and integrate cross-page information, marking it as the premier Web Agent engine.

This architecture provides a massive "knowledge base" of 744 billion parameters while activating only ~40B during inference. For developers, this translates to world-class knowledge density and reasoning depth—surpassing dense models like Llama-3 405B—at lower latency and cost.

Total parameters represent the model's "knowledge capacity," with 744B allowing for a vast storage of world facts and expert logic. Active parameters represent the "computational power" used per inference. Thanks to the MoE architecture, GLM-5 delivers 744B-level intelligence using only 40B of compute, balancing a massive knowledge base with high-speed, cost-effective performance.

The volume of pre-training data determines a model's "breadth of vision." 28.5T tokens is one of the largest datasets globally (roughly double that of Llama-3), encompassing rare languages, specialized academic papers, and vast high-quality code. This ensures GLM-5 possesses superior accuracy and generalization when tackling complex long-tail queries, cross-cultural nuances, and low-level system programming.