GLM-4.5

📖 Check out the GLM-4.5 technical blog, technical report, and Zhipu AI technical documentation. 📍 Use GLM-4.5 API services on Z.ai API Platform (Global) or Zhipu AI Open Platform (Mainland China). 👉 One click to GLM-4.5.

Model Introduction

The GLM-4.5 series models are foundation models designed for intelligent agents. 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 blog or technical report.

The model code, tool parser and reasoning parser can be found in the implementation of transformers, vLLM and SGLang.

Model Downloads

You can directly experience the model on Hugging Face or ModelScope or download the model by following the links below.

System Requirements

Inference

We provide minimum and recommended configurations for "full-featured" model inference. The data in the table below is based on the following conditions:

1. All models use MTP layers and specify --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 to ensure competitive inference speed.
2. The cpu-offload parameter is not used.
3. Inference batch size does not exceed 8.
4. All are executed on devices that natively support FP8 inference, ensuring both weights and cache are in FP8 format.
5. Server memory must exceed 1T to ensure normal model loading and operation.

The models can run under the configurations in the table below:

Under the configurations in the table below, the models can utilize their full 128K context length:

Fine-tuning

The code can run under the configurations in the table below using Llama Factory:

The code can run under the configurations in the table below using Swift:

| Model | GPU