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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

T0 review · 1 major / 2 minor · reviewed 2026-05-11 · grok-4.3

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Pith's one-line read GLM-4.5 reaches 70.1 percent on TAU-Bench and 91 percent on AIME 24 using an open-source 355B-parameter MoE model with only 32B parameters active at once.

desk verdict GLM-4.5 is a practical open MoE release with competitive ARC benchmark numbers, but the evaluation details need checking before the rankings can be taken as settled. read the letter →

arxiv 2508.06471 v1 submitted 2025-08-08 cs.CL

GLM-4.5 Team: Aohan Zeng , Xin Lv , Qinkai Zheng , Zhenyu Hou , Bin Chen , Chengxing Xie , Cunxiang Wang , Da Yin
show 161 more authors
Hao Zeng Jiajie Zhang Kedong Wang Lucen Zhong Mingdao Liu Rui Lu Shulin Cao Xiaohan Zhang Xuancheng Huang Yao Wei Yean Cheng Yifan An Yilin Niu Yuanhao Wen Yushi Bai Zhengxiao Du Zihan Wang Zilin Zhu Bohan Zhang Bosi Wen Bowen Wu Bowen Xu Can Huang Casey Zhao Changpeng Cai Chao Yu Chen Li Chendi Ge Chenghua Huang Chenhui Zhang Chenxi Xu Chenzheng Zhu Chuang Li Congfeng Yin Daoyan Lin Dayong Yang Dazhi Jiang Ding Ai Erle Zhu Fei Wang Gengzheng Pan Guo Wang Hailong Sun Haitao Li Haiyang Li Haiyi Hu Hanyu Zhang Hao Peng Hao Tai Haoke Zhang Haoran Wang Haoyu Yang He Liu He Zhao Hongwei Liu Hongxi Yan Huan Liu Huilong Chen Ji Li Jiajing Zhao Jiamin Ren Jian Jiao Jiani Zhao Jianyang Yan Jiaqi Wang Jiayi Gui Jiayue Zhao Jie Liu Jijie Li Jing Li Jing Lu Jingsen Wang Jingwei Yuan Jingxuan Li Jingzhao Du Jinhua Du Jinxin Liu Junkai Zhi Junli Gao Ke Wang Lekang Yang Liang Xu Lin Fan Lindong Wu Lintao Ding Lu Wang Man Zhang Minghao Li Minghuan Xu Mingming Zhao Mingshu Zhai Pengfan Du Qian Dong Shangde Lei Shangqing Tu Shangtong Yang Shaoyou Lu Shijie Li Shuang Li Shuang-Li Shuxun Yang Sibo Yi Tianshu Yu Wei Tian Weihan Wang Wenbo Yu Weng Lam Tam Wenjie Liang Wentao Liu Xiao Wang Xiaohan Jia Xiaotao Gu Xiaoying Ling Xin Wang Xing Fan Xingru Pan Xinyuan Zhang Xinze Zhang Xiuqing Fu Xunkai Zhang Yabo Xu Yandong Wu Yida Lu Yidong Wang Yilin Zhou Yiming Pan Ying Zhang Yingli Wang Yingru Li Yinpei Su Yipeng Geng Yitong Zhu Yongkun Yang Yuhang Li Yuhao Wu Yujiang Li Yunan Liu Yunqing Wang Yuntao Li Yuxuan Zhang Zezhen Liu Zhen Yang Zhengda Zhou Zhongpei Qiao Zhuoer Feng Zhuorui Liu Zichen Zhang Zijun Yao Zikang Wang Ziqiang Liu Ziwei Chai Zixuan Li Zuodong Zhao Wenguang Chen Jidong Zhai Bin Xu Minlie Huang Hongning Wang Juanzi Li Yuxiao Dong Jie Tang
This is my paper · ORCID
classification cs.CL
keywords mixtureofexpertslargelanguagemodelagentictasksreasoningcodingopensourcebenchmarkevaluationreinforcementlearning
checked against bilinear_family_forcedphi_equationconservation_from_balance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents GLM-4.5 as an open-source Mixture-of-Experts model built to handle agentic tasks, complex reasoning, and coding problems. It describes a multi-stage training process on 23 trillion tokens followed by expert iteration and reinforcement learning that produces a hybrid reasoning capability allowing both extended thinking traces and direct answers. The model posts the listed benchmark scores and ranks near the top of evaluated systems despite activating far fewer parameters than some denser competitors. A smaller 106B-parameter variant is also released to broaden access. The work aims to supply capable tools for building practical AI agents and technical problem solvers.

What carries the argument

The hybrid reasoning method that supports both thinking and direct response modes, built inside a Mixture-of-Experts architecture with 355 billion total parameters but only 32 billion activated per token.

What would settle it

Independent re-evaluation of the model on the same benchmark problems using fresh, publicly documented prompts and code, or testing on a new suite of problems created after the training cutoff, would confirm or refute the claimed scores.

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Extended reading notes

Core claim

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.

Load-bearing premise

That the reported benchmark scores reflect genuine capabilities measured through fair, standardized, and uncontaminated evaluations that allow direct comparison to other models.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters. It features a hybrid reasoning method that supports both thinking and direct response modes. The model undergoes multi-stage training on 23T tokens and post-training with expert model iteration and reinforcement learning. GLM-4.5 reports strong results across agentic, reasoning, and coding (ARC) tasks, including 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. It ranks 3rd overall among evaluated models and 2nd on agentic benchmarks despite having fewer parameters than several competitors. A compact variant, GLM-4.5-Air (106B parameters), is also released, with code and models made available at a GitHub repository.

Significance. If the benchmark results hold under verifiable and standardized conditions, the work advances open-source models for agentic and reasoning tasks by demonstrating competitive performance with an efficient MoE architecture and hybrid reasoning. The public release of both the full and compact models, along with code, is a clear strength that enables reproducibility and community follow-up research on ARC capabilities.

major comments (1)
  1. [Abstract] Abstract: The central performance claims, including the specific scores of 70.1% on TAU-Bench and 64.2% on SWE-bench Verified together with the 3rd overall and 2nd agentic ranking, are presented without any description of the evaluation methodology. Details on agent scaffolding, tool-use protocols, attempt limits, prompting consistency, use of the hybrid thinking mode, and data-contamination controls are required to establish that the results are comparable to those of competing models; their absence undermines confidence in the headline rankings.
minor comments (2)
  1. [Abstract] The phrase 'expert model iteration' in the abstract is used without definition or reference to a methods section; a brief clarification would improve readability.
  2. The efficiency claim ('much fewer parameters than several competitors') would be strengthened by explicitly listing the parameter counts of the referenced competing models in a comparison table.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. The feedback highlights an important point about ensuring transparency in the abstract for benchmark results. We address this directly below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance claims, including the specific scores of 70.1% on TAU-Bench and 64.2% on SWE-bench Verified together with the 3rd overall and 2nd agentic ranking, are presented without any description of the evaluation methodology. Details on agent scaffolding, tool-use protocols, attempt limits, prompting consistency, use of the hybrid thinking mode, and data-contamination controls are required to establish that the results are comparable to those of competing models; their absence undermines confidence in the headline rankings.

    Authors: We agree that the abstract, constrained by length, omits explicit methodology details, which can affect immediate assessment of comparability. The full manuscript contains sections on evaluation protocols that cover agent scaffolding (standard setups for TAU-Bench and SWE-bench), tool-use protocols, attempt limits, prompting strategies, selective use of the hybrid thinking mode, and data-contamination controls via held-out test sets and decontamination procedures. In the revision, we will expand the abstract with a concise clause summarizing these elements and add cross-references to the detailed methodology sections. This change will improve clarity while preserving the abstract's brevity. We do not believe the core results or rankings require alteration, only better contextualization. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely empirical benchmark reporting

full rationale

The paper describes training GLM-4.5 (355B MoE) on 23T tokens with post-training and RL, then reports measured benchmark scores (70.1% TAU-Bench, 91.0% AIME 24, 64.2% SWE-bench Verified). No mathematical derivations, equations, fitted predictions, or first-principles results exist. Claims rest on independent empirical evaluations with no self-definitional loops, fitted-input predictions, or load-bearing self-citations that reduce the central results to inputs by construction. Standard model-release structure; derivation chain is absent.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

As an empirical report on a trained foundation model, the central claims rest on standard machine learning assumptions including the validity of benchmark evaluations and the effectiveness of the described training pipeline; no novel axioms, free parameters, or invented entities are introduced beyond typical hyperparameter choices in LLM training.

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Cite this review

Pith. "Pith review of GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models." pith.science (2026). https://pith.science/paper/2508.06471

@misc{pith2026250806471,
  author       = {Pith},
  title        = {Pith review of: GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2508.06471}},
  note         = {Machine review of arXiv:2508.06471}
}
read the original abstract

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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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith.Foundation.DAlembert.Inevitability bilinear_family_forced unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    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.

  • IndisputableMonolith.Foundation.PhiForcing phi_equation unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    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.

  • IndisputableMonolith.Foundation.LedgerForcing conservation_from_balance unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning

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