👋 加入我们的 Discord 社区。
📖 查阅 GLM-4.7 的 技术博客、技术报告(GLM-4.5)。
📍 在 Z.ai API 平台 使用 GLM-4.7 API 服务。
👉 一键访问 GLM-4.7。
GLM-4.7,您的全新编码伙伴,具备以下特性:
在聊天、创意写作和角色扮演等众多其他场景中,您也能看到显著的性能提升。

基准测试性能。以下表格详细对比了 GLM-4.7 与其他模型(GPT-5-High、GPT-5.1-High、Claude Sonnet 4.5、Gemini 3.0 Pro、DeepSeek-V3.2、Kimi K2 Thinking)在 17 项基准测试(包括 8 项推理基准、5 项编码基准和 3 项智能体基准)上的表现。
| 基准测试 | GLM-4.7 | GLM-4.6 | Kimi K2 Thinking | DeepSeek-V3.2 | Gemini 3.0 Pro | Claude Sonnet 4.5 | GPT-5-High | GPT-5.1-High |
|---|---|---|---|---|---|---|---|---|
| MMLU-Pro | 84.3 | 83.2 | 84.6 | 85.0 | 90.1 | 88.2 | 87.5 | 87.0 |
| GPQA-Diamond | 85.7 | 81.0 | 84.5 | 82.4 | 91.9 | 83.4 | 85.7 | 88.1 |
| HLE | 24.8 | 17.2 | 23.9 | 25.1 | 37.5 | 13.7 | 26.3 | 25.7 |
| HLE (使用工具) | 42.8 | 30.4 | 44.9 | 40.8 | 45.8 | 32.0 | 35.2 | 42.7 |
| AIME 2025 | 95.7 | 93.9 | 94.5 | 93.1 | 95.0 | 87.0 | 94.6 | 94.0 |
| HMMT 2025年2月 | 97.1 | 89.2 | 89.4 | 92.5 | 97.5 | 79.2 | 88.3 | 96.3 |
| HMMT 2025年11月 | 93.5 | 87.7 | 89.2 | 90.2 | 93.3 | 81.7 | 89.2 | - |
| IMOAnswerBench | 82.0 | 73.5 | 78.6 | 78.3 | 83.3 | 65.8 | 76.0 | - |
| LiveCodeBench-v6 | 84.9 | 82.8 | 83.1 | 83.3 | 90.7 | 64.0 | 87.0 | 87.0 |
| SWE-bench Verified | 73.8 | 68.0 | 71.3 | 73.1 | 76.2 | 77.2 | 74.9 | 76.3 |
| SWE-bench 多语言 | 66.7 | 53.8 | 61.1 | 70.2 | - | 68.0 | 55.3 | - |
| Terminal Bench Hard | 33.3 | 23.6 | 30.6 | 35.4 | 39.0 | 33.3 | 30.5 | 43.0 |
| Terminal Bench 2.0 | 41.0 | 24.5 | 35.7 | 46.4 | 54.2 | 42.8 | 35.2 | 47.6 |
| BrowseComp | 52.0 | 45.1 | - | 51.4 | - | 24.1 | 54.9 | 50.8 |
| BrowseComp(带上下文管理) | 67.5 | 57.5 | 60.2 | 67.6 | 59.2 | - | - | - |
| BrowseComp-Zh | 66.6 | 49.5 | 62.3 | 65.0 | - | 42.4 | 63.0 | - |
| τ²-Bench | 87.4 | 75.2 | 74.3 | 85.3 | 90.7 | 87.2 | 82.4 | 82.7 |
编码: 人工智能(AGI)的发展是一场漫长的旅程,基准测试只是评估性能的一种方式。虽然这些指标提供了必要的参考,但最重要的仍是实际使用体验。真正的智能不仅仅是在测试中取得好成绩或更快地处理数据;最终,AGI 的成功将取决于它如何无缝地融入我们的生活——这次的主题是“编码”。

GLM-4.7 进一步增强了自 GLM-4.5 起引入的 交错思维(Interleaved Thinking) 功能,并新增了 保留思维(Preserved Thinking) 和 轮次级思维(Turn-level Thinking)。通过在行动间进行思考并确保跨轮次的一致性,它使复杂任务更稳定、更可控:
更多详情:https://docs.z.ai/guides/capabilities/thinking-mode
默认设置(大多数任务)
1.00.95131072对于多轮智能体任务(τ²-Bench 和 Terminal Bench 2),请开启保留思维模式。
Terminal Bench、SWE Bench Verified
0.71.016384τ^2-Bench
016384在 τ^2-Bench 评估中,我们在零售和电信用户交互中添加了额外提示,以避免因用户错误结束交互而导致的失败模式。对于航空领域,我们应用了 Claude Opus 4.5 发布报告中提出的领域修复方案。
对于本地部署,GLM-4.7 支持包括 vLLM 和 SGLang 在内的推理框架。官方 Github 仓库中提供了详尽的部署说明。
vLLM 和 SGLang 仅在其主分支上支持 GLM-4.7。您可以使用它们的官方 Docker 镜像进行推理。
使用 Docker 的方式如下:
docker pull vllm/vllm-openai:nightly 或使用 pip(必须将 pypi.org 用作索引 URL):
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly使用 Docker 的方式如下:
docker pull lmsysorg/sglang:dev或使用 pip install sglang 从源代码安装。
将 transformers 版本设置为 4.57.3,然后运行:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "zai-org/GLM-4.7"
messages = [{"role": "user", "content": "hello"}]
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=MODEL_PATH,
torch_dtype=torch.bfloat16,
device_map="auto",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1] :])
print(output_text)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-fp8python3 -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对于 GLM-4.7 的智能体任务,请通过添加以下配置开启思维保留模式(仅 sglang 支持):
"chat_template_kwargs": {
"enable_thinking": true,
"clear_thinking": false
}使用 vLLM 和 SGLang 时,发送请求时默认启用思维模式。若需关闭思维开关,需添加 extra_body={"chat_template_kwargs": {"enable_thinking": False}} 参数。
两者均支持工具调用。调用时请使用 OpenAI 风格的工具描述格式。
如果您的研究中使用了我们的成果,敬请考虑引用以下论文:
@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},
}