GLM 5.2 Free API: How to Get Free Tokens and Try GLM-4-Plus

GLM 5.2 (served as glm-4-plus on Zhipu AI's BigModel platform) is one of the most capable open-weight models available today — a 753B MoE architecture that scores 89% on GPQA Diamond and 74.3% on LiveCodeBench. The good news: you do not need to pay to start experimenting. Zhipu AI hands every new account a batch of free trial tokens at registration, and the API is OpenAI-compatible so your existing code works with two line changes. This guide walks through every step, from creating your BigModel account to making your first successful API call.

What Is GLM 5.2 and Why Does It Matter

GLM 5.2 is the flagship model from Zhipu AI (智谱 AI), released in May 2025. Under the hood it is a Mixture-of-Experts model with 753 billion total parameters and 40 billion active parameters per forward pass. That design gives it the raw capability of a large dense model at a fraction of the inference cost.

A few numbers worth knowing before you start:

• Context window: 1,048,576 tokens (1 million) — long enough to fit entire codebases or multi-hundred-page documents
• GPQA Diamond: 89% — near the top of published scientific reasoning benchmarks
• SWE-bench Pro: 62.1% — strong real-world software engineering performance
• LiveCodeBench: 74.3% — competitive on live coding evaluation
• Languages: Chinese and English natively supported, with vision capability via GLM-4V

Pricing for paid usage sits at $1.40 per million input tokens and $4.40 per million output tokens — reasonable for production, but the free trial tier is what lets you evaluate the model at zero cost before committing.

For a deeper look at the benchmark numbers and how GLM 5.2 compares to GPT-4o, Claude 3.5, and Gemini 1.5 Pro, see our GLM 5.2 API guide.

Step 1: Create a BigModel Account

The free trial tokens live on Zhipu AI's developer platform at open.bigmodel.cn. Both Chinese phone numbers and international email addresses are accepted at registration — you do not need a Chinese phone number to sign up.

1. Open https://open.bigmodel.cn in your browser.
2. Click the registration button (注册 / Sign Up).
3. Choose Email registration if you are outside China, or Phone registration if you have a Chinese number.
4. Complete the verification step (email link or SMS code).
5. Fill in the basic profile fields and submit.

Once your account is confirmed, navigate to the dashboard. Look for the API Keys section in the left sidebar. Create a new key and copy it somewhere safe — the platform shows it only once at creation time.

At this point, Zhipu AI should automatically credit your account with a free-trial token quota. The exact amount changes over time with promotional offers, so check open.bigmodel.cn in your dashboard for the current figure rather than relying on any third-party source.

Step 2: Install the OpenAI Python SDK

GLM 5.2's API is fully OpenAI-compatible. You do not need a separate Zhipu-specific client library. Install the standard openai package if you have not already:

pip install openai

Then export your API key as an environment variable:

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

On Windows (PowerShell):

$env:BIGMODEL_API_KEY = "your-api-key-here"

Step 3: Make Your First API Call

The only two things that differ from a standard OpenAI call are the base_url and the model name. Everything else — the request body shape, streaming, tool/function calling, JSON mode — works identically.

Base URL: https://open.bigmodel.cn/api/paas/v4/ Model name: glm-4-plus

Here is a minimal working example:

import os
from openai import OpenAI

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

response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant.",
        },
        {
            "role": "user",
            "content": "Explain the Mixture-of-Experts architecture in two paragraphs.",
        },
    ],
    max_tokens=512,
)

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

Run it with python main.py and you should see a coherent, well-structured explanation within a few seconds. If you get a 401 error, double-check that the BIGMODEL_API_KEY environment variable is set correctly and that the key you copied is the full string without trailing whitespace.

Step 4: Enable Streaming (Optional)

For longer responses or interactive use cases, streaming gives you token-by-token output instead of waiting for the full completion. The change is a single parameter:

import os
from openai import OpenAI

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

stream = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "user", "content": "Write a short poem about large language models."},
    ],
    stream=True,
    max_tokens=256,
)

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

print()  # newline after stream ends

Streaming is particularly useful when you are building a user-facing interface and want to show progress rather than a loading spinner.

Understanding the Free Tier

Free trial tokens are designed for evaluation, not production traffic. A few things to keep in mind:

Token quota: The initial grant covers a meaningful amount of evaluation but is not unlimited. Check your dashboard at open.bigmodel.cn for real-time usage and remaining balance.

Rate limits: The free tier enforces lower rate limits than paid plans. According to the official documentation, limits vary by account tier. If you hit a 429 (rate limit exceeded) error, add a brief retry with exponential backoff or simply wait a moment before retrying.

Expiration: Free trial credits may have an expiration date. Zhipu AI's dashboard shows the validity window next to each credit grant. Use your credits before they expire.

What counts as a token: GLM 5.2 tokenizes Chinese and English text differently. As a rough guide, one English word is approximately 1–1.5 tokens; one Chinese character is approximately 1.5–2 tokens. Images (via GLM-4V) are counted separately based on resolution.

When you exhaust the free tier, the API returns a quota-exceeded error rather than silently continuing. At that point you can upgrade to a paid plan at $1.40 per million input tokens — competitive pricing for a 1M-context model of this capability level.

Community Plan vs. Paid Plan

The free tier is fully featured — you get the same glm-4-plus model, the same 1M context window, vision, and tool calling. The only practical differences are the credit cap and lower rate limits.

Try GLM 5.2 Without Signing Up

If you want to test GLM 5.2 before creating a BigModel account, glm5.app offers a no-signup playground where you can send prompts directly to GLM-4-Plus and see responses immediately. It is useful for quick sanity checks, prompt experimentation, and sharing results with teammates who do not have API credentials.

Try GLM 5.2 free on glm5.app — no account required

Common Issues and Fixes

401 Unauthorized The API key is wrong, missing, or has not been activated yet. Regenerate the key in your BigModel dashboard and re-export the environment variable.

429 Rate Limited You have exceeded the free tier's request rate. Wait a few seconds and retry, or implement exponential backoff. On paid plans the limits are substantially higher.

Model not found Double-check that you are using model="glm-4-plus" (not glm-5.2 or gpt-4). The model name on the BigModel platform is glm-4-plus.

SSL or connection errors The base URL must be exactly https://open.bigmodel.cn/api/paas/v4/ including the trailing slash. Some OpenAI SDK versions are sensitive to the trailing slash — if you see path-related 404 errors, try adding or removing it.

Quota exhausted Your free trial credits are used up. Log into the dashboard to check your balance and consider upgrading to the pay-as-you-go plan.

Using the Open-Weight Version Locally

If you prefer not to call a hosted API at all, Zhipu AI also publishes open weights for the smaller GLM-4-9B-Chat variant on HuggingFace under an MIT license at https://huggingface.co/THUDM. This is not the full GLM-4-Plus model (which requires large-scale infrastructure), but it is sufficient for many NLP tasks and runs on a single consumer GPU with quantization.

For production-grade quality with the full 753B MoE architecture, the hosted API at open.bigmodel.cn remains the practical path.

Next Steps

Once you have confirmed your free-tier API calls are working, a few directions are worth exploring:

• Long context: GLM 5.2's 1M-token context window is one of its defining strengths. Try feeding it a large document or codebase and asking cross-cutting questions.
• Tool calling: The API supports OpenAI-style function definitions. Equip the model with search, calculator, or database tools and observe how it orchestrates them.
• Vision (GLM-4V): Attach base64-encoded images to your messages using the standard content-array format. GLM-4V can describe, analyze, and reason over images in both English and Chinese.
• Batch processing: For offline workloads, the BigModel platform offers a batch endpoint with lower per-token pricing according to official documentation — worth checking if you need to process thousands of documents.

For detailed endpoint documentation, streaming edge cases, and production deployment patterns, see the GLM 5.2 API integration guide on this site.

Ready to move beyond the playground and start building? The BigModel free tier gives you enough tokens to prototype a complete feature. When you are ready to scale, the paid plan costs less than comparable closed models at the same capability level.

Start building with GLM 5.2 — explore the playground at glm5.app

Sources

• Zhipu AI BigModel developer platform: https://open.bigmodel.cn
• GLM-4-9B-Chat open weights (MIT license): https://huggingface.co/THUDM
• GLM-4-Plus API documentation (Chinese): https://open.bigmodel.cn/dev/api
• GPQA Diamond benchmark (Google DeepMind): https://arxiv.org/abs/2311.12022
• SWE-bench leaderboard: https://www.swebench.com
• LiveCodeBench: https://livecodebench.github.io
• OpenAI Python SDK (used for OpenAI-compatible calls): https://github.com/openai/openai-python