Read our How to Run GLM-4.6 Guide!
[!NOTE] Please use latest version of
llama.cpp. This GGUF includes multiple Unsloth chat template fixes!
Forllama.cpp, please use--jinja
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.
GLM-4.6
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📖 Check out the GLM-4.6 technical blog, technical report(GLM-4.5), and Zhipu AI technical documentation.
📍 Use GLM-4.6 API services on Z.ai API Platform.
👉 One click to GLM-4.6.
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 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.
Inference
Both GLM-4.5 and GLM-4.6 use the same inference method.
you can check our github 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 = 0.95top_k = 40