alpindale / GLM-4.6-INT8

huggingface.co
Total runs: 55
24-hour runs: 0
7-day runs: 4
30-day runs: 29
Model's Last Updated: October 02 2025
text-generation

Introduction of GLM-4.6-INT8

Model Details of GLM-4.6-INT8

GLM-4.6-INT8

👋 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:

  • top_p = 0.95
  • top_k = 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.

Runs of alpindale GLM-4.6-INT8 on huggingface.co

55
Total runs
0
24-hour runs
4
3-day runs
4
7-day runs
29
30-day runs

More Information About GLM-4.6-INT8 huggingface.co Model

More GLM-4.6-INT8 license Visit here:

https://choosealicense.com/licenses/mit

GLM-4.6-INT8 huggingface.co

GLM-4.6-INT8 huggingface.co is an AI model on huggingface.co that provides GLM-4.6-INT8's model effect (), which can be used instantly with this alpindale GLM-4.6-INT8 model. huggingface.co supports a free trial of the GLM-4.6-INT8 model, and also provides paid use of the GLM-4.6-INT8. Support call GLM-4.6-INT8 model through api, including Node.js, Python, http.

alpindale GLM-4.6-INT8 online free

GLM-4.6-INT8 huggingface.co is an online trial and call api platform, which integrates GLM-4.6-INT8's modeling effects, including api services, and provides a free online trial of GLM-4.6-INT8, you can try GLM-4.6-INT8 online for free by clicking the link below.

alpindale GLM-4.6-INT8 online free url in huggingface.co:

https://huggingface.co/alpindale/GLM-4.6-INT8

GLM-4.6-INT8 install

GLM-4.6-INT8 is an open source model from GitHub that offers a free installation service, and any user can find GLM-4.6-INT8 on GitHub to install. At the same time, huggingface.co provides the effect of GLM-4.6-INT8 install, users can directly use GLM-4.6-INT8 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

GLM-4.6-INT8 install url in huggingface.co:

https://huggingface.co/alpindale/GLM-4.6-INT8

Url of GLM-4.6-INT8

GLM-4.6-INT8 huggingface.co Url

Provider of GLM-4.6-INT8 huggingface.co

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