GLM-5.3

Generic Info

• Publisher: Zhipu AI (Z.ai)
• Release Date: September 2026
• Architecture: Sparse Attention MoE Transformer with Post-Training Scaling
• Context Window: 1,000,000 tokens (1M tokens)
• License: GLM Community Open License / Apache 2.0 (Flash variants)
• Key Capabilities: Terminal Bench 3.0 SOTA, CyberGym Security Analysis, Code Generation, 1M Context

GLM-5.3 pushes post-training scaling to new heights, delivering a 50% improvement over GLM-5.2 on in-house coding evaluations and achieving open-source state-of-the-art on Terminal Bench 3.0 and Agents' Last Exam. Furthermore, scaled post-training unlocks emergent cyber capabilities, making GLM-5.3 the world leader on CyberGym for vulnerability discovery, automated exploitation defense, and multi-step security patching.

Hello World Guide

Run GLM-5.3 locally with Hugging Face transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "zai-org/GLM-5.3"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Audit this system architecture for concurrent race conditions and write an automated test to reproduce the issue."
messages = [
    {"role": "system", "content": "You are GLM-5.3, an expert autonomous terminal coding agent."},
    {"role": "user", "content": prompt}
]

inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=1024,
    temperature=0.5
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

Industry Usage