GLM-5.2 API Guide: 744B Open Model at $4.40/M Output — Nearly 7× Cheaper Than GPT-5.5

Step 4: Install the SDK and Make Your First Call

GLM-5.2 uses an OpenAI-compatible API, so you can use the official zhipuai Python package or just the openai package with a custom base URL.

Install:

pip install zhipuai
# or use openai-compatible:
pip install openai

First call (zhipuai SDK):

from zhipuai import ZhipuAI
import os

client = ZhipuAI(api_key=os.environ["ZHIPU_API_KEY"])

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {"role": "user", "content": "Write a Python function that checks if a string is a palindrome."}
    ]
)

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

Same thing using the OpenAI SDK:

from openai import OpenAI
import os

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-5.2",
    messages=[
        {"role": "user", "content": "Write a Python function that checks if a string is a palindrome."}
    ]
)

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

Both work. The OpenAI-compatible path is useful if your existing codebase already uses the openai package.

Step 5: Streaming Responses

For any UI that shows responses in real time (chatbots, copilots), use streaming:

from zhipuai import ZhipuAI
import os

client = ZhipuAI(api_key=os.environ["ZHIPU_API_KEY"])

stream = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {"role": "user", "content": "Explain how transformers work, step by step."}
    ],
    stream=True
)

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

print()  # newline at end

Step 6: Deep Reasoning Mode

GLM-5.2 has two reasoning effort levels. The default is max. You can set it explicitly:

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {"role": "user", "content": "Prove that there are infinitely many prime numbers."}
    ],
    thinking={"type": "enabled"},
    reasoning_effort="high"   # or "max" (default)
)

# The thinking process is available separately
print("Thinking:", response.choices[0].message.reasoning_content)
print("Answer:", response.choices[0].message.content)

Use reasoning_effort="high" for most tasks — it’s faster and cheaper. Use "max" for hard math, complex code generation, or anything where getting it right on the first try saves you multiple retries.

Step 7: The 1M Context Window in Practice

Here’s how to pass a large document and ask questions about it:

import os
from zhipuai import ZhipuAI

client = ZhipuAI(api_key=os.environ["ZHIPU_API_KEY"])

# Load a large document
with open("your-large-document.txt", "r") as f:
    document = f.read()

response = client.chat.completions.create(
    model="glm-5.2[1m]",   # note the [1m] suffix
    messages=[
        {
            "role": "system",
            "content": "You are a document analyst. Answer questions based only on the provided document."
        },
        {
            "role": "user",
            "content": f"Here is the document:\n\n{document}\n\nQuestion: What are the three main conclusions?"
        }
    ]
)

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

How the Pricing Works: Cached vs Non-Cached

Cache is applied automatically when the prefix of your prompt matches a previous call. No code changes needed. The cache TTL is not published, but Z.ai’s docs suggest it persists for the duration of a session.

GLM-5.2 vs GLM-5.1: Should You Upgrade?

GLM-5.2’s direct predecessor is GLM-5.1 (open-sourced April 2026), not GLM-4.7 — Z.ai has shipped GLM-5, GLM-5.1, and GLM-5.2 in the four months between December 2025 and June 2026. If you’re still on GLM-4.7, the jump is much bigger than this table shows.

The two releases share the same underlying parameter count — GLM-5.2 is mostly an efficiency and long-context reliability upgrade, not a bigger model. The Terminal-Bench jump (63.5 → 81.0) is the most noticeable practical difference.

Upgrade if: your workload is heavy on coding, reasoning, or long-document tasks — there’s no reason to stay on 5.1.
Skip straight to 5.2 if: you’re still on GLM-4.7 or earlier — you’d be skipping two generations either way.

FAQ

Do I need a VPN to use GLM-5.2?
No. The API endpoint (api.z.ai or open.bigmodel.cn) is accessible from outside China without a VPN.

Is the model available for local deployment?
Yes. Weights are on HuggingFace at zai-org/GLM-5.2. You’ll need significant GPU resources for a 744B model — realistically a multi-GPU cluster (vLLM or SGLang recommended). There’s also a quantized version available.

Is the MIT license really no-strings-attached?
The model weights are MIT-licensed. Z.ai has no geographic restrictions on commercial use. That said, you should read the license yourself if you’re deploying in a regulated industry.

Why does my JWT-style API key look so long?
GLM uses JWT tokens as API keys. This is normal — treat it the same as any other API key (store in env vars, never commit to git).

What if I need vision capabilities?
GLM-5.2 doesn’t have vision. Use glm-4.6V for image understanding tasks — it supports the same API format with an image_url content type in the messages array.

Next Steps

• GLM-4 API Complete Guide — full reference for Function Calling, web search, and video generation across the GLM family
• China AI Model Pricing Comparison 2026 — full price table across DeepSeek, Qwen, Kimi, GLM, and more
• Kimi K3 API Guide — another strong open-source model with 1M context and leading frontend scores