GLM

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Overview

GLM-5.3 is Z.ai's latest flagship large language model, delivering state-of-the-art coding and agentic performance among open-source models, with a 50% improvement over its predecessor on Z.ai Code Bench and end-to-end delivery of production-grade results. It demonstrates emergent cybersecurity capability, achieving the best result to date on the CyberGym vulnerability discovery benchmark. With a 1M-token context window, 128K-token maximum output, and three reasoning effort levels, GLM-5.3 excels at long-horizon, complex tasks.

Input

Text

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

Input
$1.4Per 1M tokens
Output
$4.4Per 1M tokens
Input(Implicit Cache)
$0.28Per 1M tokens

Rate Limits & Context

Max Input
1M
Max Output
131K
Max Input (Thinking)
1M
Max Output (Thinking)
131K
Context
1M
Max Reasoning
131K
TPMTokens Per Minute
5M

Built-in Tools

code_interpreterResponses API

web_extractorResponses API

API Reference

Call API

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from openai import OpenAI
import os
client = OpenAI(
    # If the environment variable is not set, replace it with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://maas.qwencloudapi.com/compatible-mode/v1",
)
messages = [{"role": "user", "content": "Who are you"}]
completion = client.chat.completions.create(
    model="glm-5.3",
    messages=messages,
    stream=True
)
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20)
for chunk in completion:
	if chunk.choices:
		delta = chunk.choices[0].delta
		# Collect only the thinking content
		if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
			if not is_answering:
				print(delta.reasoning_content, end="", flush=True)
		# When content is received, start generating the response
		if hasattr(delta, "content") and delta.content:
			if not is_answering:
				print("\n" + "=" * 20 + "Full response" + "=" * 20)
				is_answering = True
			print(delta.content, end="", flush=True)