• unsloth

• zai-org/GLM-4.6

• en
• zh

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!
For llama.cpp, please use --jinja

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

GLM-4.6

👋 Join our Discord community.


📖 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.

!bench

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:

• topp = 0.95
• topk = 40

Evaluation

• For tool-integrated reasoning, please refer to this doc.
• For search benchmark, we design a specific format for searching toolcall in thinking mode to support search agent, please refer to this. for the detailed template.