*This model was published in HF papers on 2025-08-08 and contributed to Hugging Face Transformers on 2025-07-21.* # GLM-4.5, GLM-4.6, GLM-4.7 ## Overview **GLM-4.7**, **GLM-4.6** and **GLM-4.5** language model use this class. The implementation in transformers does not include an MTP layer. ### GLM-4.7 **GLM-4.7**, your new coding partner, is coming with the following features: - **Core Coding**: GLM-4.7 brings clear gains, compared to its predecessor GLM-4.6, in multilingual agentic coding and terminal-based tasks, including (73.8%, +5.8%) on SWE-bench, (66.7%, +12.9%) on SWE-bench Multilingual, and (41%, +16.5%) on Terminal Bench 2.0. GLM-4.7 also supports thinking before acting, with significant improvements on complex tasks in mainstream agent frameworks such as Claude Code, Kilo Code, Cline, and Roo Code. - **Vibe Coding**: GLM-4.7 takes a big step forward in improving UI quality. It produces cleaner, more modern webpages and generates better-looking slides with more accurate layout and sizing. - **Tool Using**: GLM-4.7 achieves significantly improvements in Tool using. Significant better performances can be seen on benchmarks such as τ^2-Bench and on web browsing via BrowseComp. - **Complex Reasoning**: GLM-4.7 delivers a substantial boost in mathematical and reasoning capabilities, achieving (42.8%, +12.4%) on the HLE (Humanity’s Last Exam) benchmark compared to GLM-4.6. More general, one would also witness significant improvements in many other scenarios such as chat, creative writing, and role-play scenario.  **Interleaved Thinking & Preserved 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: - **Interleaved Thinking**: The model thinks before every response and tool calling, improving instruction following and the quality of generation. - **Preserved Thinking**: In coding agent scenarios, the model automatically retains all thinking blocks across multi-turn conversations, reusing the existing reasoning instead of re-deriving from scratch. This reduces information loss and inconsistencies, and is well-suited for long-horizon, complex tasks. - **Turn-level Thinking**: The model supports per-turn control over reasoning within a session—disable thinking for lightweight requests to reduce latency/cost, enable it for complex tasks to improve accuracy and stability. More details: https://docs.z.ai/guides/capabilities/thinking-mode For more eval results, show cases, and technical details, please visit [GLM-4.7 technical blog](https://z.ai/blog/glm-4.7). ### GLM-4.6 Compared with GLM-4.5, **GLM-4.6** brings several key improvements: * **Longer context window:** The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks. * **Superior coding performance:** The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages. * **Advanced reasoning:** GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability. * **More capable agents:** GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks. * **Refined writing:** Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios. 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**. For more eval results, show cases, and technical details, please visit [GLM-4.6 technical blog](https://z.ai/blog/glm-4.6). ### GLM-4.5 The [**GLM-4.5**](https://huggingface.co/papers/2508.06471) series models are foundation models designed for intelligent agents, MoE variants are documented here as Glm4Moe. 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](https://huggingface.co/papers/2508.06471) or [technical blog](https://z.ai/blog/glm-4.5). The model code, tool parser and reasoning parser can be found in the implementation of [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4_moe), [vLLM](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/glm4_moe_mtp.py) and [SGLang](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/glm4_moe.py). ## Glm4MoeConfig [[autodoc]] Glm4MoeConfig ## Glm4MoeModel [[autodoc]] Glm4MoeModel - forward ## Glm4MoeForCausalLM [[autodoc]] Glm4MoeForCausalLM - forward