--- language: - en - zh library_name: transformers license: mit pipeline_tag: text-generation model-index: - name: GLM-4.6 results: - task: type: evaluation dataset: name: Artificial Analysis Benchmarks type: artificial_analysis metrics: - name: Artificial Analysis Intelligence Index type: artificial_analysis_intelligence_index value: 44.7 - name: Artificial Analysis Coding Index type: artificial_analysis_coding_index value: 38.7 - name: Artificial Analysis Math Index type: artificial_analysis_math_index value: 44.3 - name: Mmlu Pro type: mmlu_pro value: 0.784 - name: Gpqa type: gpqa value: 0.632 - name: Hle type: hle value: 0.052 - name: Livecodebench type: livecodebench value: 0.561 - name: Scicode type: scicode value: 0.331 - name: Aime 25 type: aime_25 value: 0.443 - name: Ifbench type: ifbench value: 0.367 - name: Lcr type: lcr value: 0.263 - name: Terminalbench Hard type: terminalbench_hard value: 0.27 - name: Tau2 type: tau2 value: 0.769 source: name: Artificial Analysis API url: https://artificialanalysis.ai --- # GLM-4.6

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## Model Introduction 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 Codeใ€Clineใ€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**. ![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench_glm46.png) ## Inference **Both GLM-4.5 and GLM-4.6 use the same inference method.** you can check our [github](https://github.com/zai-org/GLM-4.5) for more detail. ## Recommended Evaluation Parameters For general evaluations, we recommend using a **sampling temperature of 1.0**. For **code-related evaluation tasks** (such as LCB), it is further recommended to set: - `top_p = 0.95` - `top_k = 40` ## Evaluation - For tool-integrated reasoning, please refer to [this doc](https://github.com/zai-org/GLM-4.5/blob/main/resources/glm_4.6_tir_guide.md). - For search benchmark, we design a specific format for searching toolcall in thinking mode to support search agent, please refer to [this](https://github.com/zai-org/GLM-4.5/blob/main/resources/trajectory_search.json). for the detailed template.