GLM-5.2
GLM-5.2 is a large language model from the GLM-5 series, designed for long-horizon agentic tasks and complex coding. It features a solid 1M-token context and improved architecture for efficiency. It is intended for developers and researchers working on advanced AI applications.
✨ Key features
- Solid 1M-token context for long-horizon tasks
- Advanced coding with flexible reasoning effort levels
- IndexShare architecture reduces per-token FLOPs by 2.9x
- Improved MTP layer increases speculative decoding acceptance length
- Strongest open-source model on standard coding benchmarks
- Available in BF16 and FP8 precision variants
🎯 Use cases
- Complex software engineering and code generation
- Long-horizon agentic tasks requiring sustained reasoning
- Handling very long documents or contexts up to 1M tokens
- Deploying as a high-performance coding assistant
📦 Installation
🧰 Requirements: Requires a compatible inference framework (e.g., SGLang, vLLM, Transformers) and sufficient GPU resources for a 744B-parameter model.
To use GLM-5.2, download the model from Hugging Face or ModelScope. For local serving, use a supported framework like SGLang or vLLM. Refer to the respective framework's documentation for installation and usage. Example download:
# Using Hugging Face CLI
huggingface-cli download zai-org/GLM-5.2
🚀 Usage
Example using Transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "zai-org/GLM-5.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
prompt = "Write a Python function to compute Fibonacci numbers."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))
⚠️ Good to know
GLM-5.2 is a large model requiring significant computational resources; it may not be suitable for low-resource environments.
❓ FAQ
What is the context length of GLM-5.2?
GLM-5.2 supports a solid 1M-token context.
What reasoning_effort values does GLM-5.2 accept?
GLM-5.2 accepts only 'high' and 'max' for reasoning_effort, with 'max' as the default.
How does GLM-5.2 compare to GLM-5.1 on coding benchmarks?
GLM-5.2 significantly improves over GLM-5.1, e.g., 81.0 vs 62.0 on Terminal-Bench 2.1.
What is the model size of GLM-5.2?
GLM-5.2 has 744B total parameters with 40B active.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.