update

LICENSE

@@ -1,10 +1,12 @@
+GLM-5.3 License
+
 Copyright (c) 2026 Z.AI
 
-Permission is hereby granted, free of charge, to any person or entity (the "Licensee") obtaining a copy of this software — including the model weights, parameters, configuration files, inference and training code,   and associated documentation (collectively, the "Software") — to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software; to run, deploy, fine-tune, or otherwise modify the Software and create derivative works from it; and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+Permission is hereby granted, free of charge, to any person or entity (the "Licensee") obtaining a copy of this software — including the model weights, parameters, configuration files, inference and training code, and associated documentation (collectively, the "Software") — to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software; to run, deploy, fine-tune, or otherwise modify the Software and create derivative works from it; and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
 1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The Licensee's use of the Software must comply with applicable laws and regulations.
 
-2. "Model as a Service" means giving a third party access to language model inference or fine-tuning (e.g., via API) in a manner that allows such third party to exercise meaningful control over the inputs, parameters, or training data. This does not include (a) end-user products with model capabilities solely embedded within specific features or   harnesses, or (b) mere relaying of requests to models hosted by others.
+2. "Model as a Service" means giving a third party access to language model inference or fine-tuning (e.g., via API) in a manner that allows such third party to exercise meaningful control over the inputs, parameters, or training data. This does not include (a) end-user products with model capabilities solely embedded within specific features or harnesses, or (b) mere relaying of requests to models hosted by others.
 If the Licensee or any of its affiliates operates a Model as a Service business, and the aggregate revenue of the Licensee and its affiliates exceeds 10 billion US dollars (or the equivalent in other currencies) in total over any consecutive 12 months, the Licensee must pass Z.AI's security review before using the Software or its derivative works for any commercial purpose. The scope and method of the security review shall be reasonably determined by Z.AI.
 
 3. THE SOFTWARE AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL Z.AI OR ITS AFFILIATES OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
@@ -15,13 +17,13 @@ For any questions regarding this license, please contact glmlicense@z.ai.
 
 版权所有 (c) 2026 Z.AI
 
-特此免费授予任何获得本软件副本的个人或实体（"被许可方"）—— 包括模型权重、参数、配置文件、推理和训练代码及相关文档（统称"软件"）——不受限制地处理本软件的权利，包括但不限于：使用、复制、修改、合并、发布、分发、再许可和/或销售软件副本；运行、部署、微调或以其他方式修改软件并创建衍生作品；以及允许获得软件的其他人行使上述权利，但须遵守以下条件：
+特此免费授予任何获得本软件副本的个人或实体（"被许可方"）——包括模型权重、参数、配置文件、推理和训练代码及相关文档（统称"软件"）——不受限制地处理本软件的权利，包括但不限于：使用、复制、修改、合并、发布、分发、再许可和/或销售软件副本；运行、部署、微调或以其他方式修改软件并创建衍生作品；以及允许获得软件的其他人行使上述权利，但须遵守以下条件：
 
 1. 上述版权声明和本许可声明应包含在软件的所有副本或实质性部分中。被许可方对软件的使用必须符合适用法律法规。
 
 2. "模型即服务"指以允许第三方对输入、参数或训练数据行使实质性控制的方式，向第三方提供语言模型推理或微调服务（如通过API）。不包括：(a) 模型能力仅嵌入特定功能或框架中的终端用户产品，或(b) 单纯转发请求至他人托管的模型。
-若被许可方或其关联方运营"模型即服务"业务，且被许可方及关联方在任意连续12 个月内累计总收入超过 100亿美元（或等值其他货币），则被许可方在使用软件或其衍生作品进行任何商业用途之前，须通过 Z.AI 的安全审查。安全审查的范围和方式由 Z.AI 合理确定。
+若被许可方或其关联方运营"模型即服务"业务，且被许可方及关联方在任意连续12个月内累计总收入超过100亿美元（或等值其他货币），则被许可方在使用软件或其衍生作品进行任何商业用途之前，须通过Z.AI的安全审查。安全审查的范围和方式由Z.AI合理确定。
 
-3. 软件及其任何输出和结果均按"现状"提供，不附带任何形式的保证，无论是明示还是暗示，包括但不限于适销性、特定用途适用性和不侵权的保证。在任何情况下，Z.AI 或其关联方或版权持有人均不对任何索赔、损害或其他责任承担责任，无论该责任是基于合同、侵权或其他方式，因软件或使用软件而产生或与之相关。
+3. 软件及其任何输出和结果均按"现状"提供，不附带任何形式的保证，无论是明示还是暗示，包括但不限于适销性、特定用途适用性和不侵权的保证。在任何情况下，Z.AI或其关联方或版权持有人均不对任何索赔、损害或其他责任承担责任，无论该责任是基于合同、侵权或其他方式，因软件或使用软件而产生或与之相关。
 
 如对本许可有任何疑问，请联系 glmlicense@z.ai。

README.md

@@ -4,9 +4,87 @@ language:
 - zh
 library_name: transformers
 license: other
+license_name: glm-5.3
 pipeline_tag: text-generation
-release_date: "2026-08-28T15:00:00Z"
-description: "GLM-5.3: Frontier Coding with Emergent Cyber Capabilities"
 ---
 
-# GLM-5.3
+# GLM-5.3
+
+GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks:
+
++ Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam.
++ Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.
+
+![bench_53](https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/bench_53_2.png)
+
+## Benchmark
+
+| Benchmark                    | GLM-5.3   | GLM-5.2 | Kimi K3  | DeepSeek-V4 Pro-0813 | Qwen3.8-Max | Opus 4.8 | Fable 5 (w/ fallback) | GPT-5.6 Sol   |
+|------------------------------|-----------|---------|----------|----------------------|-------------|----------|-----------------------|---------------|
+| Terminal Bench 2.1           | 88.2      | 81.0    | 88.3     | 87.9                 | 86.6        | 85.0     | 88.0                  | **88.8**      |
+| Terminal Bench 3.0           | 28.3      | 4.6     | 17.4     | –                    | –           | 21.1     | 33.7                  | **34.6**      |
+| DeepSWE (v1.1)               | 66.9      | 46.2    | 67.5     | 62.7                 | 56.6        | 58.0     | 69.7                  | **72.7**      |
+| NL2Repo                      | 58.0      | 48.9    | 58.0     | 61.1                 | 55.9        | **69.7** | –                     | –             |
+| ProgramBench (Almost Solved) | 19.0      | 9.5     | 17.5     | –                    | 10.5        | 15.5     | **33.0**              | 23.0          |
+| FrontierSWE                  | 78.1      | 67.5    | –        | –                    | –           | 66.5     | **88.2**              | –             |
+| SWE-Marathon (v1.1)          | 42.5      | 19.4    | 48.1     | –                    | –           | **48.8** | 33.1                  | 42.5          |
+| PostTrainBench               | 39.8      | 31.7    | 32.0     | –                    | –           | 32.9     | **41.8**              | 36.2          |
+| CyberGym                     | **84.5**  | 77.2    | 80.0     | 83.3                 | 78.5        | 78.1     | 83.8                  | 83.6          |
+| ExploitGym (2h / 6h)         | 105 / 130 | 29 / 39 | 36 / 70  | –                    | 14 / 26     | 80 / 120 | 181 / 247             | **216 / 293** |
+| ExploitBench                 | 54.4      | 24.4    | 32.2     | –                    | 28.8        | 40.0     | **78.0**              | 76.5          |
+| Toolathlon Verified          | 73.0      | 59.9    | **76.5** | 74.1                 | 72.5        | 76.2     | 74.7                  | 74.9          |
+| AutomationBench (v1.0.6)     | **48.2**  | 26.2    | 46.7     | 43.2                 | 39.8        | 41.0     | 46.2                  | 45.8          |
+| Agents' Last Exam (ALE-CLI)  | 28.5      | 23.8    | 27.6     | 25.7                 | 27.0        | 25.7     | 23.8                  | **28.6**      |
+| HLE w/ Tools                 | 62.5      | 54.7    | 59.8     | 60.0                 | 56.2        | 57.9     | 63.9                  | **64.5**      |
+| GDPval-AA v2                 | **1769**  | 1508    | 1682     | 1590                 | 1739        | 1588     | 1743                  | 1730          |
+
+### Serve GLM-5.3 Locally
+
+GLM-5.3 supports deployment with the following frameworks. Feel free to try them out:
+
+- [SGLang](https://github.com/sgl-project/sglang) — see [cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-5.3)
+- [vLLM](https://github.com/vllm-project/vllm) — see [recipes](https://recipes.vllm.ai/zai-org/GLM-5.3)
+- [TokenSpeed](https://github.com/lightseekorg/tokenspeed) — see [here](https://lightseek.org/tokenspeed/recipes/models#glm-5-3)
+- [Transformers](https://github.com/huggingface/transformers) — see [transformers docs](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/glm_moe_dsa.md)
+- [KTransformers](https://github.com/kvcache-ai/ktransformers) — see [tutorial](https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/GLM-5.2-Tutorial.md)
+- [Unsloth](https://github.com/unslothai/unsloth) — see [guide](https://unsloth.ai/docs/models/GLM-5.3)
+- For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see [here](https://github.com/zai-org/GLM-5/blob/main/example/ascend.md).
+
+### Note
+
+- GLM-5.3 supports controlling the thinking budget through the `reasoning_effort` parameter, which accepts three levels: `low`, `high`, and `max`. It defaults to `max` if not passed (or if set to any other value). To use `low` or `high`, pass them explicitly. For benchmark and leaderboard reproduction, keep the default `max`.
+- In the chat template for GLM-5.3, `clear_thinking` defaults to `false` if not passed. For chat scenarios, explicitly pass `clear_thinking=true`.
+
+## Footnotes
+
+- **HLE w/ tools**: We use sampling parameters of `temperature=1.0` and `top_p=0.95` for evaluation, with a maximum generation length of `163,840` tokens. The evaluation is conducted with a maximum context length of `300,000` tokens, using a context management strategy. We use GPT-5.6-luna (medium) as the judge model.
+- **NL2Repo**: We evaluated NL2Repo with `temperature=1.0`, `top_p=1.0`, and `max_new_tokens=64k` under 1M context. To prevent hacking, we use rule-based and a LLM-based judgement to prevent malicious behaviors (e.g., unauthorized pip or curl operations).
+- **DeepSWE**: We run DeepSWE using the mini-swe-agent harness with `temperature=0.95`, `top_p=1.0`, `timeout=6h` and 400K context.
+- **Terminal-Bench 2.1**: We evaluate in Claude Code 2.1.207 with `temperature=1.0`, `top_p=1`, `max_new_tokens=65536` with 6h timeout.
+- **Terminal-Bench 3.0**: We evaluate Terminal-Bench-3 tasks with the Claude Code 2.1.207 harness (reasoning effort=max, 400K context, and 128K maximum output), reporting avg@3 over three rollouts per task. Each rollout runs in an isolated container built from the task's official image, and is capped at 600 agent turns with a 10-hour timeout. Tool Search is disabled, and the artifacts each agent produces are scored by the task's official separate verifier.
+- **Agent's Last Exam (CLI)**: We evaluate ALE using the official evaluation protocol with the Claude Code harness (reasoning effort=max, 1M context, and 64K maximum output). Each of the 105 tasks runs in an isolated Docker container using the resources declared in its Task Card. The default timeout is 4 hours, with task-specific limits taking precedence (up to 8 hours). Tool Search is disabled, and results are scored by the official ALE evaluators.
+- **Toolathlon Verified**: We obtain all results via the official evaluation service and report pass@1 averaged over 3 independent runs.
+- **AutomationBench**: We evaluate on AutomationBench **v1.0.6**, incorporating the fix for the `null`-type handling issue introduced in [PR #13]([#](https://github.com/zapier/AutomationBench/pull/13)).
+- **GDPval-AA v2**: Models are evaluated by Artificial Analysis.
+- **CyberGym**: We evaluate GLM-5.3 in Claude Code 2.1.207 (max reasoning effort, no web tools with `temperature=1.0`, `top_p=1.0`, `max_new_tokens=128000`). All evaluations are under unlimited timeout per task and results are single-run Pass@1 over 1,507 tasks. To simulate real-world usage scenarios, we place the agent inside the task container. We also remove all Git-related information and apply a domain whitelist (allowing only essential domains such as pypi.org and deb.debian.org for basic tool installation) to prevent the agent from cheating.
+- **ExploitGym**: We evaluate GLM-5.3, Kimi-K3 and Qwen3.8 Max in Claude Code 2.1.207 (max reasoning effort, no web tools with `temperature=1.0`, `top_p=1.0`, `max_new_tokens=128000`). The reported results are single-run Pass@1 on 869 tasks under two timeout budgets: 2 hours and 6 hours, which are calculated as the API inference time rescaled by per-model tokens per second rate (per-model TPS sourced from Artificial Analysis; that is, we rescale GLM-5.3's results by 115 TPS, Kimi K3's results by 40 TPS and Qwen3.8 Max's results by 47 TPS), plus the non-API overhead. We also apply a domain whitelist (allowing only essential domains such as pypi.org and deb.debian.org for basic tool installation) to prevent the agent from cheating.
+- **ExploitBench**: We evaluate GLM-5.3 in Claude Code 2.1.207 (max reasoning effort, no web tools with `temperature=1.0`, `top_p=1.0`, `max_new_tokens=128000`). Following the official evaluation settings, we limit the maximum number of interaction rounds between the agent and the environment to 300, and compute the average coverage score over all 41 tasks across 3 revisions. The coverage result of a task is determined by taking the union of capabilities achieved across all revisions, and the average score is obtained by averaging the results. We also apply a domain whitelist (allowing only essential domains such as pypi.org and deb.debian.org for basic tool installation) to prevent the agent from cheating.
+- **FrontierSWE**: The evaluation was conducted by [Proximal](https://www.proximal.ai/) with 1M context length, max effort level, and 128K maximum output tokens. Dominance score reported as of 2026/08/14.
+- **PostTrainBench**: We evaluate GLM-5.3 using Claude Code 2.1.207 with max effort level, `temperature = 1.0`, `top_p = 1.0`, `max_new_tokens = 128000`, and a 1M-token context window. We report the weighted average over 3 runs. Runs that fail to produce a score fall back to the official zero-shot base-model baseline score. For checks intended to prevent the use of third-party APIs, we removed the original pattern-matching-based checks, as they produced false positives when a local vLLM endpoint was accessed through the OpenAI SDK. Instead, we use an LLM agent to inspect solutions for external API usage.
+- **SWE-Marathon**: We evaluate GLM-5.3 using Claude Code 2.1.207 with maximum effort level, `temperature = 1.0`, `top_p = 0.95`, `max_new_tokens = 128000`, and a 1M-token context window. For `strip-clone`, the original anti-cheat checks used overly broad import detection that could reject valid implementations. We removed the affected checks and performed llm-based inspection instead to avoid false positives. For `parameter-golf` and `trimul-cuda`, changes to the NVIDIA wheels caused the Docker image builds to fail, so we added `--extra-index-url https://pypi.org/simple` to restore successful builds.
+
+## Citation
+
+If you find GLM-5.3 useful in your research, please cite our technical report:
+
+```bibtex
+@misc{glm5team2026glm5vibecodingagentic,
+      title={GLM-5: from Vibe Coding to Agentic Engineering},
+      author={GLM-5-Team and : and Aohan Zeng and Xin Lv and Zhenyu Hou and Zhengxiao Du and Qinkai Zheng and Bin Chen and Da Yin and Chendi Ge and Chenghua Huang and Chengxing Xie and Chenzheng Zhu and Congfeng Yin and Cunxiang Wang and Gengzheng Pan and Hao Zeng and Haoke Zhang and Haoran Wang and Huilong Chen and Jiajie Zhang and Jian Jiao and Jiaqi Guo and Jingsen Wang and Jingzhao Du and Jinzhu Wu and Kedong Wang and Lei Li and Lin Fan and Lucen Zhong and Mingdao Liu and Mingming Zhao and Pengfan Du and Qian Dong and Rui Lu and Shuang-Li and Shulin Cao and Song Liu and Ting Jiang and Xiaodong Chen and Xiaohan Zhang and Xuancheng Huang and Xuezhen Dong and Yabo Xu and Yao Wei and Yifan An and Yilin Niu and Yitong Zhu and Yuanhao Wen and Yukuo Cen and Yushi Bai and Zhongpei Qiao and Zihan Wang and Zikang Wang and Zilin Zhu and Ziqiang Liu and Zixuan Li and Bojie Wang and Bosi Wen and Can Huang and Changpeng Cai and Chao Yu and Chen Li and Chengwei Hu and Chenhui Zhang and Dan Zhang and Daoyan Lin and Dayong Yang and Di Wang and Ding Ai and Erle Zhu and Fangzhou Yi and Feiyu Chen and Guohong Wen and Hailong Sun and Haisha Zhao and Haiyi Hu and Hanchen Zhang and Hanrui Liu and Hanyu Zhang and Hao Peng and Hao Tai and Haobo Zhang and He Liu and Hongwei Wang and Hongxi Yan and Hongyu Ge and Huan Liu and Huanpeng Chu and Jia'ni Zhao and Jiachen Wang and Jiajing Zhao and Jiamin Ren and Jiapeng Wang and Jiaxin Zhang and Jiayi Gui and Jiayue Zhao and Jijie Li and Jing An and Jing Li and Jingwei Yuan and Jinhua Du and Jinxin Liu and Junkai Zhi and Junwen Duan and Kaiyue Zhou and Kangjian Wei and Ke Wang and Keyun Luo and Laiqiang Zhang and Leigang Sha and Liang Xu and Lindong Wu and Lintao Ding and Lu Chen and Minghao Li and Nianyi Lin and Pan Ta and Qiang Zou and Rongjun Song and Ruiqi Yang and Shangqing Tu and Shangtong Yang and Shaoxiang Wu and Shengyan Zhang and Shijie Li and Shuang Li and Shuyi Fan and Wei Qin and Wei Tian and Weining Zhang and Wenbo Yu and Wenjie Liang and Xiang Kuang and Xiangmeng Cheng and Xiangyang Li and Xiaoquan Yan and Xiaowei Hu and Xiaoying Ling and Xing Fan and Xingye Xia and Xinyuan Zhang and Xinze Zhang and Xirui Pan and Xu Zou and Xunkai Zhang and Yadi Liu and Yandong Wu and Yanfu Li and Yidong Wang and Yifan Zhu and Yijun Tan and Yilin Zhou and Yiming Pan and Ying Zhang and Yinpei Su and Yipeng Geng and Yong Yan and Yonglin Tan and Yuean Bi and Yuhan Shen and Yuhao Yang and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yurong Wu and Yutao Zhang and Yuxi Duan and Yuxuan Zhang and Zezhen Liu and Zhengtao Jiang and Zhenhe Yan and Zheyu Zhang and Zhixiang Wei and Zhuo Chen and Zhuoer Feng and Zijun Yao and Ziwei Chai and Ziyuan Wang and Zuzhou Zhang and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang},
+      year={2026},
+      eprint={2602.15763},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.15763},
+}
+```