GitHub - zai-org/GLM-4.5: GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models (original) (raw)

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Model Introduction

GLM-4.7

GLM-4.7, your new coding partner, is coming with the following features:

More general, one would also witness significant improvements in many other scenarios such as chat, creative writing, and role-play scenario.

bench

Interleaved Thinking & Preserved Thinking

thinking

GLM-4.7 further enhances Interleaved Thinking (a feature introduced since GLM-4.5) and introduces Preserved Thinking and Turn-level Thinking. By thinking between actions and staying consistent across turns, it makes complex tasks more stable and more controllable:

More details: https://docs.z.ai/guides/capabilities/thinking-mode

We also provide the lightweight 30B-A3B model GLM-4.7-Flash, offering a new option for lightweight deployment that balances performance and efficiency.

GLM-4.6

Compared with GLM-4.5, GLM-4.6 brings several key improvements:

We evaluated GLM-4.6 across eight public benchmarks covering agents, reasoning, and coding. Results show clear gains over GLM-4.5, with GLM-4.6 also holding competitive advantages over leading domestic and international models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4.

GLM-4.5

The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.

Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.

We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.

As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of 63.2, in the 3rd place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at 59.8 while maintaining superior efficiency.

For more eval results, show cases, and technical details, please visit our technical report.

Model Downloads

Model Download Links Model Size Precision
GLM-4.7 🤗 Hugging Face 🤖 ModelScope 355B-A32B BF16
GLM-4.7-FP8 🤗 Hugging Face 🤖 ModelScope 355B-A32B FP8
GLM-4.7-Flash 🤗 Hugging Face 🤖 ModelScope 30B-A3B BF16
GLM-4.6 🤗 Hugging Face 🤖 ModelScope 355B-A32B BF16
GLM-4.6-FP8 🤗 Hugging Face 🤖 ModelScope 355B-A32B FP8
GLM-4.5 🤗 Hugging Face 🤖 ModelScope 355B-A32B BF16
GLM-4.5-Air 🤗 Hugging Face 🤖 ModelScope 106B-A12B BF16
GLM-4.5-FP8 🤗 Hugging Face 🤖 ModelScope 355B-A32B FP8
GLM-4.5-Air-FP8 🤗 Hugging Face 🤖 ModelScope 106B-A12B FP8
GLM-4.5-Base 🤗 Hugging Face 🤖 ModelScope 355B-A32B BF16
GLM-4.5-Air-Base 🤗 Hugging Face 🤖 ModelScope 106B-A12B BF16

System Requirements

Inference with Nvidia GPUs

We provide minimum and recommended configurations for "full-featured" model inference. The data in the table below is based on the following conditions:

  1. All models use MTP layers and specify--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 to ensure competitive inference speed.
  2. The cpu-offload parameter is not used.
  3. Inference batch size does not exceed 8.
  4. All are executed on devices that natively support FP8 inference, ensuring both weights and cache are in FP8 format.
  5. Server memory must exceed 1T to ensure normal model loading and operation.

The models can run under the configurations in the table below:

Model Precision GPU Type and Count
GLM-4.5 BF16 H100 x 16
GLM-4.5 FP8 H100 x 8
GLM-4.5-Air BF16 H100 x 4
GLM-4.5-Air FP8 H100 x 2
GLM-4.7-Flash BF16 H100 x 1

Under the configurations in the table below, the models can utilize their full 128K context length:

Model Precision GPU Type and Count
GLM-4.5 BF16 H100 x 32
GLM-4.5 FP8 H100 x 16
GLM-4.5-Air BF16 H100 x 8
GLM-4.5-Air FP8 H100 x 4
GLM-4.7-Flash BF16 H100 x 2

Other Devices

Fine-tuning

The code can run under the configurations in the table below using Llama Factory:

Model GPU Type and Count Strategy Batch Size (per GPU)
GLM-4.5 H100 x 16 Lora 1
GLM-4.5-Air H100 x 4 Lora 1

The code can run under the configurations in the table below using Swift:

Model GPU Type and Count Strategy Batch Size (per GPU)
GLM-4.5 H20 (96GiB) x 16 Lora 1
GLM-4.5-Air H20 (96GiB) x 4 Lora 1
GLM-4.5 H20 (96GiB) x 128 SFT 1
GLM-4.5-Air H20 (96GiB) x 32 SFT 1
GLM-4.5 H20 (96GiB) x 128 RL 1
GLM-4.5-Air H20 (96GiB) x 32 RL 1

Quick Start

Install dependencies (sglang, vllm, etc.) according to the configuration requirements in requirements.txt.

transformers

Please refer to the trans_infer_cli.py code in the inference folder.

vLLM

vllm serve zai-org/GLM-4.7-FP8
--tensor-parallel-size 4
--speculative-config.method mtp
--speculative-config.num_speculative_tokens 1
--tool-call-parser glm47
--reasoning-parser glm45
--enable-auto-tool-choice
--served-model-name glm-4.7-fp8

SGLang

python3 -m sglang.launch_server
--model-path zai-org/GLM-4.7-FP8
--tp-size 8
--tool-call-parser glm47
--reasoning-parser glm45
--speculative-algorithm EAGLE
--speculative-num-steps 3
--speculative-eagle-topk 1
--speculative-num-draft-tokens 4
--mem-fraction-static 0.8
--served-model-name glm-4.7-fp8
--host 0.0.0.0
--port 8000

The following is a simple method to implement PD-Disaggregation using a single machine with multiple GPUs, P and D each use 4 GPUs for GLM-4.5

python -m sglang.launch_server --model-path zai-org/GLM-4.5-Air --disaggregation-mode prefill --disaggregation-ib-device mlx5_0 --tp-size 4 python -m sglang.launch_server --model-path zai-org/GLM-4.5-Air --disaggregation-mode decode --port 30001 --disaggregation-ib-device mlx5_0 --tp-size 4 --base-gpu-id 4
python -m sglang_router.launch_router --pd-disaggregation --prefill http://127.0.0.1:30000 --decode http://127.0.0.1:30001 --host 0.0.0.0 --port 8000

Parameter Instructions

"chat_template_kwargs": {  
    "enable_thinking": true,  
    "clear_thinking": false  
}

Evaluation

Citation

If you find our work useful in your research, please consider citing the following paper:

@misc{5team2025glm45agenticreasoningcoding, title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models}, author={GLM Team and Aohan Zeng and Xin Lv and Qinkai Zheng and Zhenyu Hou and Bin Chen and Chengxing Xie and Cunxiang Wang and Da Yin and Hao Zeng and Jiajie Zhang and Kedong Wang and Lucen Zhong and Mingdao Liu and Rui Lu and Shulin Cao and Xiaohan Zhang and Xuancheng Huang and Yao Wei and Yean Cheng and Yifan An and Yilin Niu and Yuanhao Wen and Yushi Bai and Zhengxiao Du and Zihan Wang and Zilin Zhu and Bohan Zhang and Bosi Wen and Bowen Wu and Bowen Xu and Can Huang and Casey Zhao and Changpeng Cai and Chao Yu and Chen Li and Chendi Ge and Chenghua Huang and Chenhui Zhang and Chenxi Xu and Chenzheng Zhu and Chuang Li and Congfeng Yin and Daoyan Lin and Dayong Yang and Dazhi Jiang and Ding Ai and Erle Zhu and Fei Wang and Gengzheng Pan and Guo Wang and Hailong Sun and Haitao Li and Haiyang Li and Haiyi Hu and Hanyu Zhang and Hao Peng and Hao Tai and Haoke Zhang and Haoran Wang and Haoyu Yang and He Liu and He Zhao and Hongwei Liu and Hongxi Yan and Huan Liu and Huilong Chen and Ji Li and Jiajing Zhao and Jiamin Ren and Jian Jiao and Jiani Zhao and Jianyang Yan and Jiaqi Wang and Jiayi Gui and Jiayue Zhao and Jie Liu and Jijie Li and Jing Li and Jing Lu and Jingsen Wang and Jingwei Yuan and Jingxuan Li and Jingzhao Du and Jinhua Du and Jinxin Liu and Junkai Zhi and Junli Gao and Ke Wang and Lekang Yang and Liang Xu and Lin Fan and Lindong Wu and Lintao Ding and Lu Wang and Man Zhang and Minghao Li and Minghuan Xu and Mingming Zhao and Mingshu Zhai and Pengfan Du and Qian Dong and Shangde Lei and Shangqing Tu and Shangtong Yang and Shaoyou Lu and Shijie Li and Shuang Li and Shuang-Li and Shuxun Yang and Sibo Yi and Tianshu Yu and Wei Tian and Weihan Wang and Wenbo Yu and Weng Lam Tam and Wenjie Liang and Wentao Liu and Xiao Wang and Xiaohan Jia and Xiaotao Gu and Xiaoying Ling and Xin Wang and Xing Fan and Xingru Pan and Xinyuan Zhang and Xinze Zhang and Xiuqing Fu and Xunkai Zhang and Yabo Xu and Yandong Wu and Yida Lu and Yidong Wang and Yilin Zhou and Yiming Pan and Ying Zhang and Yingli Wang and Yingru Li and Yinpei Su and Yipeng Geng and Yitong Zhu and Yongkun Yang and Yuhang Li and Yuhao Wu and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yuxuan Zhang and Zezhen Liu and Zhen Yang and Zhengda Zhou and Zhongpei Qiao and Zhuoer Feng and Zhuorui Liu and Zichen Zhang and Zihan Wang and Zijun Yao and Zikang Wang and Ziqiang Liu and Ziwei Chai and Zixuan Li and Zuodong Zhao and Wenguang Chen and Jidong Zhai and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang}, year={2025}, eprint={2508.06471}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2508.06471}, }