How to Use GLM-4.6 API: Complete Developer Guide (2026)

How to Use GLM-4.6 API: Complete Developer Guide (2026)

Zhipu AI's GLM-4.6 is one of the most capable large language models to come out of China, competing with GPT-4o and Claude Sonnet on major benchmarks. It supports Chinese and English natively, offers competitive pricing, and provides an OpenAI-compatible API that makes migration straightforward. This guide covers everything you need to get started.

What Is GLM-4.6?

GLM-4.6 is the latest model in Zhipu AI's GLM (General Language Model) family. It is a large multimodal model that handles text generation, code, reasoning, tool use, and vision tasks. Key highlights:

• Strong bilingual performance (Chinese and English)
• 128K context window
• Function calling and tool use support
• Vision capabilities (image understanding)
• OpenAI-compatible API format
• Competitive pricing (significantly cheaper than GPT-4o)

GLM Model Lineup

Model	Context Window	Strengths	Pricing (per 1M tokens)
GLM-4.6	128K	Best overall performance	~$2.00 input / $6.00 output
GLM-4.6-Flash	128K	Fast, cost-effective	~$0.10 input / $0.30 output
GLM-4V-Plus	8K	Vision + text	~$3.00 input / $9.00 output
GLM-4.6-Long	1M	Ultra-long context	~$1.00 input / $3.00 output

Prices are approximate and may vary. Check the Zhipu AI platform for current rates.

Step 1: Create a Zhipu AI Account

1. Visit open.bigmodel.cn (Zhipu AI's developer platform).
2. Click "Sign Up" and register with your email or phone number.
3. Complete identity verification (required for API access).
4. New accounts receive free trial credits -- typically enough for several thousand API calls.

Step 2: Generate an API Key

1. Log in to the Zhipu AI developer console.
2. Navigate to API Keys in the left sidebar.
3. Click "Create API Key."
4. Copy the key and store it securely.

export ZHIPU_API_KEY="your-api-key-here"

Step 3: Make Your First API Call

The GLM-4.6 API follows the OpenAI chat completions format, making it easy to integrate if you already work with OpenAI or other compatible APIs.

Python Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ZHIPU_API_KEY"],
    base_url="https://open.bigmodel.cn/api/paas/v4"
)

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 find the longest palindromic substring in a string. Use dynamic programming."}
    ],
    temperature=0.7,
    max_tokens=2048
)

print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")

JavaScript / TypeScript Example

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ZHIPU_API_KEY,
  baseURL: "https://open.bigmodel.cn/api/paas/v4",
});

async function main() {
  const response = await client.chat.completions.create({
    model: "glm-4.6",
    messages: [
      { role: "system", content: "You are a helpful coding assistant." },
      {
        role: "user",
        content:
          "Write a TypeScript function to debounce API calls with proper generic typing.",
      },
    ],
    temperature: 0.7,
    max_tokens: 2048,
  });

  console.log(response.choices[0].message.content);
  console.log(`Tokens used: ${response.usage?.total_tokens}`);
}

main();

cURL Example

curl https://open.bigmodel.cn/api/paas/v4/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ZHIPU_API_KEY" \
  -d '{
    "model": "glm-4.6",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain how transformer attention mechanisms work."}
    ],
    "temperature": 0.7,
    "max_tokens": 1024
  }'

Step 4: Use Streaming Responses

For real-time applications, use streaming to get tokens as they are generated:

stream = client.chat.completions.create(
    model="glm-4.6",
    messages=[
        {"role": "user", "content": "Write a comprehensive guide to Rust error handling."}
    ],
    stream=True
)

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

Step 5: Use Function Calling

GLM-4.6 supports function calling (tool use), letting the model interact with external APIs and databases:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g., Beijing, 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"
)

# Check if the model wants to call a function
message = response.choices[0].message
if message.tool_calls:
    for tool_call in message.tool_calls:
        print(f"Function: {tool_call.function.name}")
        print(f"Arguments: {tool_call.function.arguments}")

Step 6: Use Vision Capabilities

GLM-4V-Plus supports image understanding. Send images as base64 or URLs:

import base64

with open("diagram.png", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="glm-4v-plus",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this system architecture diagram in detail."},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{image_b64}"}
                }
            ]
        }
    ]
)

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

GLM-4.6 vs. Other LLM APIs

Feature	GLM-4.6	GPT-4o	Claude Sonnet	Gemini 2.0 Flash
Input price (per 1M tokens)	~$2.00	$2.50	$3.00	$0.10
Output price (per 1M tokens)	~$6.00	$10.00	$15.00	$0.40
Context window	128K	128K	200K	1M
Chinese language quality	Excellent	Good	Good	Good
English language quality	Very good	Excellent	Excellent	Good
Coding ability	Strong	Excellent	Excellent	Good
Function calling	Yes	Yes	Yes	Yes
Vision	Yes (GLM-4V)	Yes	Yes	Yes
OpenAI-compatible API	Yes	Native	No (own format)	No (own format)

GLM-4.6 offers the best price-to-performance ratio for applications that need strong Chinese language support. For English-only applications, GPT-4o and Claude Sonnet still have an edge in reasoning and coding.

Error Handling Best Practices

Build robust error handling into your integration:

from openai import OpenAI, APIError, RateLimitError, APIConnectionError
import time

def call_glm(messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="glm-4.6",
                messages=messages,
                timeout=30
            )
            return response.choices[0].message.content
        except RateLimitError:
            wait = 2 ** attempt
            print(f"Rate limited, waiting {wait}s...")
            time.sleep(wait)
        except APIConnectionError:
            print("Connection error, retrying...")
            time.sleep(1)
        except APIError as e:
            print(f"API error: {e}")
            break
    return None

Tips for Getting the Best Results

Use GLM-4.6-Flash for simple tasks. It is 20x cheaper than the full GLM-4.6 and handles straightforward generation, summarization, and classification well.

Prompt in the target language. While GLM-4.6 is bilingual, prompting in the same language as your expected output produces better results. Mix languages only when necessary.

Leverage the long context. GLM-4.6-Long supports up to 1M tokens of context. Use it for analyzing entire codebases, long documents, or multi-document retrieval.

Use system prompts effectively. GLM-4.6 follows system prompts well. Set clear instructions about output format, language, and style upfront.

Frequently Asked Questions

Do I need a Chinese phone number to sign up? Email registration is available for international users, though some features may require additional verification. The API itself works globally.

Is GLM-4.6 censored? The model follows Chinese content regulations. Certain political and sensitive topics may receive filtered responses. For technical and business use cases, this is rarely an issue.

Can I use the OpenAI Python library? Yes. Since the API follows the OpenAI format, you can use the official openai Python package by changing the base URL and API key.

How does latency compare to GPT-4o? Latency depends on your location. From Asia, GLM-4.6 is typically faster. From North America and Europe, GPT-4o usually has lower latency due to server proximity.

Wrapping Up

GLM-4.6 is a strong choice for developers who need a capable, affordable LLM API -- especially for applications serving Chinese-speaking users. The OpenAI-compatible format makes migration painless, and the pricing is competitive. Start with the free trial credits, test your use case, and scale up from there.

If you also need AI media generation capabilities like image, video, or avatar creation alongside your LLM integration, consider a unified platform.

Try Hypereal AI free -- 35 credits, no credit card required.