GLM-4 API: Complete Developer's Guide to Zhipu AI's Flagship Model

Zhipu AI's GLM-4 series has rapidly emerged as one of China's most capable large language model families, offering impressive performance across text generation, coding, reasoning, and multimodal tasks. For developers building applications that need strong Chinese language capabilities combined with solid English proficiency, the GLM-4 API is a compelling option. In this comprehensive guide, we'll explore everything you need to know about integrating GLM-4 into your projects.

Table of Contents

• What is GLM-4? Understanding Zhipu AI's Flagship Model
• The GLM-4 Model Family: Which One Should You Use?
• Key Features & Capabilities of GLM-4
• Getting Started with GLM-4 API
• GLM-4 API Reference & Endpoints
• Code Examples: Building with GLM-4 API
• GLM-4 Pricing: How Much Does It Cost?
• Real-World Use Cases for GLM-4
• GLM-4 vs Other Chinese LLMs: A Comparison
• Access GLM-4 Easily Through Haotokai

What is GLM-4? Understanding Zhipu AI's Flagship Model

GLM-4 (General Language Model 4) is the fourth-generation large language model developed by Zhipu AI (智谱AI), a leading Chinese AI research company spun out of Tsinghua University's KEG laboratory. Building on the success of previous GLM generations, GLM-4 represents a significant leap in model capabilities.

Zhipu AI, also known for its consumer-facing chatbot 智谱清言 (Zhipu Qingyan), has positioned GLM-4 as a versatile foundation model suitable for a wide range of enterprise and consumer applications. The model has been trained on a massive multilingual corpus with particular emphasis on Chinese language understanding and generation.

The GLM-4 API provides programmatic access to these models through a RESTful interface that's compatible with the OpenAI API format, making it easy for developers already familiar with OpenAI's ecosystem to get started quickly.

The GLM-4 Model Family: Which One Should You Use?

Zhipu AI offers a diverse lineup of GLM-4 models optimized for different use cases. Understanding the differences is crucial for selecting the right model for your application:

Choosing the Right Model

Here's a quick decision framework:

• Start with glm-4-air — It offers the best balance of quality, speed, and cost for most applications.
• Upgrade to glm-4 — When you need maximum reasoning capability and quality for complex tasks.
• Use glm-4-long — For processing entire books, legal documents, or large codebases in a single prompt.
• Choose glm-4v — When your application needs to understand images or visual content.
• Try glm-4-flash — For experimentation, prototyping, or when you're just getting started (free tier available).

Key Features & Capabilities of GLM-4

1. Strong Chinese & English Bilingual Performance

GLM-4 is trained on a balanced multilingual corpus with particular emphasis on Chinese. This makes it ideal for:

• Chinese content generation and copywriting
• Chinese-English translation and localization
• Understanding Chinese cultural nuances and idioms
• Processing Chinese documents and datasets
• Building applications for the Chinese market

2. Impressive Reasoning Capabilities

GLM-4 demonstrates strong performance on reasoning benchmarks, particularly in mathematical problem-solving and logical deduction. The flagship glm-4 model competes favorably with mid-tier international models on standard benchmarks like MMLU, GSM8K, and HumanEval.

3. Function Calling & Tool Use

GLM-4 supports function calling (also known as tool use), allowing developers to connect the model to external tools, APIs, and data sources. This is essential for building:

• AI assistants that can retrieve real-time information
• Applications that interact with databases
• Workflows that require API orchestration
• Agents that can perform actions on behalf of users

4. JSON Mode

The GLM-4 API includes a JSON mode that guarantees the model returns valid JSON output. This is invaluable for:

• Structured data extraction
• API response parsing
• Database operations
• Pipeline integrations where structured output is required

Getting Started with GLM-4 API

Prerequisites

Before you can start using the GLM-4 API, you'll need:

• A Zhipu AI API account (or a Haotokai account for easier access)
• An API key from your dashboard
• Basic REST API knowledge
• Python (or your preferred programming language)

API Key Setup

Store your API key as an environment variable for security:

# Set environment variable (Linux/macOS)
export GLM4_API_KEY="your-api-key-here"

# Windows PowerShell
$env:GLM4_API_KEY = "your-api-key-here"

The Easier Alternative: Haotokai

While you can sign up directly with Zhipu AI, international developers often face challenges with account verification, payment methods, and documentation. Using Haotokai as your API gateway provides several advantages:

• Support for PayPal and international payment methods
• English-language documentation and support
• One API key for GLM-4, DeepSeek, Kimi, Qwen, and more
• Standard OpenAI-compatible API format
• No Chinese phone number or ID required

GLM-4 API Reference & Endpoints

Chat Completions Endpoint

The primary endpoint for interacting with GLM-4 models is the chat completions endpoint:

POST /v4/chat/completions

Key request parameters include:

Embeddings Endpoint

GLM-4 also provides embeddings for semantic search, RAG, and classification tasks:

POST /v4/embeddings

The embedding model (embedding-3) generates 1024-dimensional vectors suitable for most semantic search applications.

Code Examples: Building with GLM-4 API

Basic Python Example with Requests

Here's a simple example of calling the GLM-4 API using Python's requests library:

import os
import requests

def call_glm4_api(prompt, model="glm-4-air", api_key=None):
    """
    Call GLM-4 API with a user prompt.
    """
    api_key = api_key or os.getenv("GLM4_API_KEY")
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": model,
        "messages": [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": prompt
            }
        ],
        "temperature": 0.7,
        "max_tokens": 1024
    }
    
    response = requests.post(
        "https://open.bigmodel.cn/api/paas/v4/chat/completions",
        headers=headers,
        json=payload
    )
    
    if response.status_code == 200:
        result = response.json()
        return {
            "content": result["choices"][0]["message"]["content"],
            "usage": result["usage"]
        }
    else:
        raise Exception(f"API Error: {response.status_code} - {response.text}")

# Example usage
if __name__ == "__main__":
    result = call_glm4_api(
        "Explain the concept of transformer neural networks in simple terms.",
        model="glm-4-air"
    )
    print("Response:", result["content"])
    print(f"\nTokens used: {result['usage']['total_tokens']}")

Using Haotokai Unified API (Recommended for International Developers)

If you're using Haotokai, the code is nearly identical but with the added benefit of being able to switch between multiple model providers with zero code changes:

import os
import requests

def call_haotokai_model(prompt, model="glm-4-air", api_key=None):
    """
    Call any AI model through Haotokai's unified API.
    Supports GLM-4, DeepSeek, Kimi, Qwen, and 50+ other models.
    """
    api_key = api_key or os.getenv("HAOTOKAI_API_KEY")
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": model,
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "temperature": 0.7,
        "stream": False
    }
    
    response = requests.post(
        "https://www.haotokai.com/v1/chat/completions",
        headers=headers,
        json=payload
    )
    
    if response.status_code == 200:
        return response.json()["choices"][0]["message"]["content"]
    else:
        raise Exception(f"Error: {response.status_code} - {response.text}")

# Try different Chinese models with the same code!
models = ["glm-4-air", "glm-4", "qwen2.5-72b-instruct"]

for model in models:
    print(f"\n--- Testing {model} ---")
    answer = call_haotokai_model("What are the key features of your model?", model=model)
    print(f"Answer: {answer[:150]}...")

Function Calling Example

GLM-4's function calling capability lets you build more powerful applications. Here's an example:

import os
import requests
import json

def get_weather(location):
    """Simulated weather API function."""
    return {
        "location": location,
        "temperature": 25,
        "condition": "Sunny",
        "humidity": 65
    }

def glm4_function_call(prompt, api_key=None):
    api_key = api_key or os.getenv("GLM4_API_KEY")
    
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get the current weather for a location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city name, e.g. Beijing, Shanghai"
                        }
                    },
                    "required": ["location"]
                }
            }
        }
    ]
    
    payload = {
        "model": "glm-4",
        "messages": [{"role": "user", "content": prompt}],
        "tools": tools,
        "tool_choice": "auto"
    }
    
    response = requests.post(
        "https://open.bigmodel.cn/api/paas/v4/chat/completions",
        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
        json=payload
    )
    
    result = response.json()
    message = result["choices"][0]["message"]
    
    # If the model wants to call a function
    if message.get("tool_calls"):
        for tool_call in message["tool_calls"]:
            func_name = tool_call["function"]["name"]
            func_args = json.loads(tool_call["function"]["arguments"])
            
            if func_name == "get_weather":
                weather_data = get_weather(func_args["location"])
                
                # Send the result back to the model
                second_payload = {
                    "model": "glm-4",
                    "messages": [
                        {"role": "user", "content": prompt},
                        message,
                        {
                            "role": "tool",
                            "tool_call_id": tool_call["id"],
                            "content": json.dumps(weather_data)
                        }
                    ]
                }
                
                second_response = requests.post(
                    "https://open.bigmodel.cn/api/paas/v4/chat/completions",
                    headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
                    json=second_payload
                )
                
                return second_response.json()["choices"][0]["message"]["content"]
    
    return message["content"]

# Test it
result = glm4_function_call("What's the weather like in Shenzhen today?")
print(result)

Streaming Response Example

For real-time applications like chatbots, use streaming:

import os
import requests
import json

def stream_glm4_response(prompt, model="glm-4-air", api_key=None):
    """
    Stream a response from GLM-4 in real-time.
    """
    api_key = api_key or os.getenv("GLM4_API_KEY")
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
        "Accept": "text/event-stream"
    }
    
    payload = {
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "stream": True
    }
    
    response = requests.post(
        "https://open.bigmodel.cn/api/paas/v4/chat/completions",
        headers=headers,
        json=payload,
        stream=True
    )
    
    full_response = []
    
    for line in response.iter_lines():
        if line:
            line = line.decode('utf-8')
            if line.startswith('data: '):
                data = line[6:]
                if data == '[DONE]':
                    break
                try:
                    chunk = json.loads(data)
                    if "choices" in chunk and len(chunk["choices"]) > 0:
                        delta = chunk["choices"][0].get("delta", {})
                        content = delta.get("content", "")
                        if content:
                            full_response.append(content)
                            print(content, end="", flush=True)
                except json.JSONDecodeError:
                    pass
    
    return ''.join(full_response)

# Example
stream_glm4_response("Write a short story about a programmer learning AI.")

GLM-4 Pricing: How Much Does It Cost?

One of the biggest advantages of GLM-4 is its competitive pricing. Here's how the models compare:

Cost Optimization Tips

• Start with glm-4-air — For most use cases, it provides more than enough quality at 85% savings compared to the flagship model.
• Use glm-4-long for RAG — Its 1M context window is perfect for document-heavy applications without needing a separate vector database.
• Leverage glm-4-flash for testing — Free tier lets you prototype without incurring costs.
• Use Haotokai for unified billing — Mix and match models, pay with PayPal, and get consolidated billing.

Real-World Use Cases for GLM-4

1. Customer Support & Chatbots

GLM-4's strong Chinese language capabilities make it ideal for customer support applications targeting Chinese-speaking users. The glm-4-air model is fast and cost-effective enough for high-volume chatbot deployments.

2. Content Generation & Copywriting

From marketing copy to blog posts to social media content, GLM-4 excels at generating high-quality Chinese content. It understands cultural references, idioms, and tone nuances that Western models often miss.

3. RAG & Knowledge Bases

With glm-4-long's 1M token context window and the embedding API, GLM-4 is well-suited for building retrieval-augmented generation systems. It can process entire documents and provide accurate, cited responses.

4. Code Generation & Assistance

GLM-4 demonstrates solid coding capabilities across multiple programming languages. It can generate code, debug issues, explain complex codebases, and help with software architecture decisions.

5. Multimodal Applications

With glm-4v, you can build applications that understand both text and images — from content moderation to product description generation to educational tools that analyze diagrams and charts.

GLM-4 vs Other Chinese LLMs: A Comparison

How does GLM-4 stack up against other popular Chinese language models? Let's compare:

GLM-4 stands out for its exceptional value proposition. The glm-4-air model offers surprisingly good quality at just $0.01 per million tokens — making it one of the most cost-effective models available. For teams building applications that primarily serve Chinese users, GLM-4 offers quality that rivals or exceeds much more expensive alternatives.

Access GLM-4 Easily Through Haotokai

While you can use GLM-4 directly through Zhipu AI, international developers often encounter friction with payment methods, account verification, and documentation. Haotokai solves these problems by providing a unified gateway to GLM-4 and other top Chinese AI models.

Why Developers Choose Haotokai for GLM-4

✅ PayPal & International Payments

No Chinese bank account or Alipay required. Haotokai supports PayPal, credit cards, and other international payment methods, making it easy for developers worldwide to access GLM-4.

✅ One API Key, All Models

Access GLM-4 alongside DeepSeek, Kimi, Qwen, Claude, and 50+ other models — all with a single API key and unified billing. No more juggling accounts across multiple platforms.

✅ OpenAI-Compatible Format

Haotokai uses the standard OpenAI API format, so you can use existing OpenAI SDKs, libraries, and tools with minimal changes. Switching models is as simple as changing the model parameter.

✅ Competitive Pricing

Get GLM-4 at competitive rates with volume discounts available for high-usage customers. Haotokai also offers free credits for new users to try the service risk-free.

✅ English Support & Documentation

Full English documentation, API references, and customer support — no language barrier to slow you down.

Getting Started with Haotokai

1. Visit haotokai.com and create an account
2. Top up your balance using PayPal (or other payment methods)
3. Copy your API key from the dashboard
4. Start building with GLM-4 — it's that simple!

Conclusion

Zhipu AI's GLM-4 series represents a significant milestone in Chinese AI development, offering world-class language capabilities at a fraction of the cost of Western alternatives. Whether you're building customer support chatbots, content generation tools, RAG systems, or code assistants, GLM-4 provides a compelling combination of quality, versatility, and affordability.

For international developers, the easiest way to access GLM-4 is through Haotokai, which eliminates the typical friction points of Chinese AI services — payment method restrictions, language barriers, and complex account setup. With Haotokai's unified API, you can also experiment with and deploy multiple Chinese models without managing separate accounts.

The Chinese AI ecosystem is evolving rapidly, and GLM-4 is at the forefront of that evolution. For developers building applications that need strong Chinese language capabilities or looking for cost-effective alternatives to Western models, GLM-4 is definitely worth adding to your AI toolkit.