GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language
model with 355B total parameters and 32B activated parameters, featuring a
hybrid reasoning method that supports both thinking and direct response modes.
Through multi-stage training on 23T tokens and comprehensive post-training with
expert model iteration and reinforcement learning, GLM-4.5 achieves strong
performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on
TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer
parameters than several competitors, GLM-4.5 ranks 3rd overall among all
evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B
parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance
research in reasoning and agentic AI systems. Code, models, and more
information are available at https://github.com/zai-org/GLM-4.5.
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GLM-4. 5 Team
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Aohan Zeng
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Xin Lv
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Xin Lv
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Qinkai Zheng
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Zhenyu Hou
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Bin Chen
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Mingdao Liu
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2025-08-19T10:50:15.372Z
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Rui Lu
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david lee
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Jingzhao Du
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Jie Tang
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language
model with 355B total parameters and 32B activated parameters, featuring a
hybrid reasoning method that supports both thinking and direct response modes.
Through multi-stage training on 23T tokens and comprehensive post-training with
expert model iteration and reinforcement learning, GLM-4.5 achieves strong
performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on
TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer
parameters than several competitors, GLM-4.5 ranks 3rd overall among all
evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B
parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance
research in reasoning and agentic AI systems. Code, models, and more
information are available at https://github.com/zai-org/GLM-4.5.
GLM-4.5, a Mixture-of-Experts large language model with 355B parameters, achieves strong performance across agentic, reasoning, and coding tasks using multi-stage training and reinforcement learning.
Mixture-of-Experts hybrid reasoning method multi-stage training expert model iteration reinforcement learning TAU-Bench AIME 24 SWE-bench Verified agentic benchmarks