Zhipu AI (智谱AI) has been one of China's most consistent AI labs, and GLM-4.6 represents their latest flagship model. If you're building applications that need strong Chinese language understanding, tool use, or cost-effective AI capabilities, GLM-4.6 deserves a serious look.

This guide covers everything developers need to know: features, API setup, code examples, and how GLM-4.6 compares to the competition.

What Is GLM-4.6?#

GLM-4.6 is the latest iteration of Zhipu AI's General Language Model (GLM) series. It builds on the GLM-4 architecture with significant improvements in reasoning, instruction following, and multimodal capabilities.

Key features:

• 128K context window — process long documents, codebases, and conversations
• Strong bilingual performance — excellent in both Chinese and English
• Tool/function calling — native support for structured tool use
• Code generation — competitive with GPT-4o for Python, JavaScript, and more
• Vision capabilities — GLM-4V variant handles image understanding
• Web search integration — built-in web search for up-to-date information
• Cost-effective — significantly cheaper than GPT-4o and Claude

GLM-4.6 Model Variants#

GLM-4.6 Performance Benchmarks#

GLM-4.6 is competitive on English benchmarks and leads on Chinese-specific evaluations — making it the top choice for Chinese-language applications.

Getting Started with GLM-4.6 API#

Option 1: Zhipu AI Direct (BigModel Platform)#

# Install Zhipu SDK
pip install zhipuai

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your-zhipu-key")

response = client.chat.completions.create(
 model="glm-4.6",
 messages=[
 {"role": "user", "content": "Explain transformer architecture in simple terms"}
 ]
)

print(response.choices[0].message.content)

Option 2: Crazyrouter (OpenAI-Compatible)#

Crazyrouter provides GLM-4.6 through an OpenAI-compatible API — no SDK changes needed:

from openai import OpenAI

client = OpenAI(
 api_key="your-crazyrouter-key",
 base_url="https://api.crazyrouter.com/v1"
)

response = client.chat.completions.create(
 model="glm-4.6",
 messages=[
 {"role": "system", "content": "You are a helpful coding assistant."},
 {"role": "user", "content": "Write a Python function to merge two sorted arrays"}
 ],
 max_tokens=2048
)

print(response.choices[0].message.content)

Code Examples#

Function Calling / Tool Use#

GLM-4.6 has strong native tool-use capabilities:

import json

tools = [
 {
 "type": "function",
 "function": {
 "name": "get_weather",
 "description": "Get current weather for a location",
 "parameters": {
 "type": "object",
 "properties": {
 "location": {
 "type": "string",
 "description": "City name, e.g., 'Beijing' or 'San Francisco'"
 },
 "unit": {
 "type": "string",
 "enum": ["celsius", "fahrenheit"]
 }
 },
 "required": ["location"]
 }
 }
 }
]

response = client.chat.completions.create(
 model="glm-4.6",
 messages=[
 {"role": "user", "content": "What's the weather like in Shanghai today?"}
 ],
 tools=tools,
 tool_choice="auto"
)

# GLM-4.6 will return a tool call
tool_call = response.choices[0].message.tool_calls[0]
print(f"Function: {tool_call.function.name}")
print(f"Arguments: {tool_call.function.arguments}")

Streaming Responses#

stream = client.chat.completions.create(
 model="glm-4.6",
 messages=[
 {"role": "user", "content": "Write a comprehensive guide to Python async/await"}
 ],
 stream=True,
 max_tokens=4096
)

for chunk in stream:
 if chunk.choices[0].delta.content:
 print(chunk.choices[0].delta.content, end="", flush=True)

Node.js — Chat with History#

import OpenAI from 'openai';

const client = new OpenAI({
 apiKey: 'your-crazyrouter-key',
 baseURL: 'https://api.crazyrouter.com/v1'
});

const messages = [
 { role: 'system', content: 'You are a senior software architect.' },
 { role: 'user', content: 'Design a microservices architecture for an e-commerce platform.' }
];

const response = await client.chat.completions.create({
 model: 'glm-4.6',
 messages,
 max_tokens: 4096
});

console.log(response.choices[0].message.content);

// Continue the conversation
messages.push(response.choices[0].message);
messages.push({ role: 'user', content: 'Now add a recommendation engine to this architecture.' });

const followUp = await client.chat.completions.create({
 model: 'glm-4.6',
 messages,
 max_tokens: 4096
});

console.log(followUp.choices[0].message.content);

cURL — Quick Test#

curl https://api.crazyrouter.com/v1/chat/completions \
 -H "Authorization: Bearer your-crazyrouter-key" \
 -H "Content-Type: application/json" \
 -d '{
 "model": "glm-4.6",
 "messages": [
 {"role": "user", "content": "用中文解释什么是微服务架构，以及它的优缺点"}
 ],
 "max_tokens": 2048
 }'

GLM-4.6 Pricing#

GLM-4.6-Flash (Budget Option)#

GLM-4.6-Flash is one of the cheapest capable models available — ideal for high-volume applications where cost matters more than peak performance.

GLM-4.6 vs GPT-4o vs Claude Sonnet#

When to Choose GLM-4.6#

• Chinese-language applications: Best Chinese understanding and generation
• Budget-conscious projects: Significantly cheaper than GPT-4o
• Bilingual applications: Strong in both Chinese and English
• High-volume processing: GLM-4.6-Flash is extremely cost-effective

When to Choose Alternatives#

• Peak English performance: GPT-4o or Claude Sonnet 4.5
• Complex coding tasks: Claude Sonnet 4.5 leads in code generation
• Longest context: Claude offers 200K tokens

Frequently Asked Questions#

Is GLM-4.6 available outside China?#

Yes, through API aggregators like Crazyrouter. Zhipu AI's direct platform (bigmodel.cn) is also accessible internationally, though the interface is primarily in Chinese.

Does GLM-4.6 support function calling?#

Yes, GLM-4.6 has native function/tool calling support that's compatible with the OpenAI function calling format. It works reliably for structured data extraction, API orchestration, and agent workflows.

What's the difference between GLM-4.6 and GLM-4.6-Flash?#

GLM-4.6 is the full-capability model optimized for quality. GLM-4.6-Flash is a smaller, faster variant optimized for speed and cost — it's about 5x cheaper but slightly less capable on complex reasoning tasks.

Can I fine-tune GLM-4.6?#

Zhipu AI offers fine-tuning through their platform. For custom fine-tuning needs, the open-source ChatGLM variants are available on Hugging Face.

How does GLM-4.6 handle code generation?#

GLM-4.6 is competitive with GPT-4o for most coding tasks, particularly in Python and JavaScript. It's especially strong at generating code with Chinese comments and documentation.

Summary#

GLM-4.6 is a capable, cost-effective model that excels in Chinese-language tasks while remaining competitive in English. For developers building bilingual applications or looking to reduce AI costs without sacrificing too much quality, it's an excellent choice.

Access GLM-4.6 alongside GPT-4o, Claude, Gemini, and 300+ other models through Crazyrouter's unified API. Switch between models with a single line of code.