How to Use the GLM-5 API

Developers who build intelligent applications constantly evaluate frontier models for superior reasoning, coding, and long-horizon agentic performance. GLM-5, Zhipu AI’s latest flagship, delivers state-of-the-art results among open-weight models while remaining accessible through a robust API. Engineers integrate GLM-5 to power complex systems, autonomous agents, and production-grade AI workflows.

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This guide walks you through every stage: understanding the model, reviewing its benchmarks, obtaining access, authenticating requests, and implementing advanced features. Consequently, you will deploy GLM-5 confidently in your projects.

What Is GLM-5?

Zhipu AI developed GLM-5 as a 744-billion-parameter Mixture-of-Experts (MoE) model with approximately 40 billion active parameters. The architecture builds on previous GLM iterations but introduces significant enhancements. Engineers increased pre-training data from 23 trillion to 28.5 trillion tokens. They also incorporated DeepSeek Sparse Attention (DSA) to maintain long-context performance while reducing inference costs. Furthermore, the team created a novel asynchronous reinforcement learning framework called Slime, which dramatically improves post-training efficiency.

GLM-5 shifts focus from casual chat interactions toward “agentic engineering.” It excels at long-horizon planning, multi-step tool use, document generation (including .docx, .pdf, and .xlsx files), and complex software engineering tasks. The model supports a 200K token context window and generates up to 128K output tokens. These specifications enable developers to process massive codebases or long documents in a single prompt.

Moreover, Zhipu AI released GLM-5 weights under the permissive MIT license on Hugging Face and ModelScope. Teams therefore run the model locally with vLLM or SGLang, even on non-NVIDIA hardware such as Huawei Ascend chips. The official API, however, provides the fastest and most scalable path for production use.

GLM-5 Benchmarks: Leading Open-Weight Performance

GLM-5 establishes new records among open-source models across reasoning, coding, and agentic benchmarks. It narrows the gap with proprietary frontier models and, in several categories, surpasses them.

Key reasoning benchmarks include:

• Humanity’s Last Exam (HLE): 30.5 (base) → 50.4 (with tools)
• AIME 2026 I: 92.7
• HMMT Nov. 2025: 96.9
• IMOAnswerBench: 82.5
• GPQA-Diamond: 86.0

Coding performance stands out:

• SWE-bench Verified: 77.8
• SWE-bench Multilingual: 73.3
• Terminal-Bench 2.0 (verified): 56.2

Agentic capabilities shine brightest:

• BrowseComp: 62.0 (75.9 with context management)
• Vending Bench 2: $4,432.12 final balance — first among open models

These numbers demonstrate that GLM-5 handles real-world software engineering, long-term planning, and multi-tool orchestration at levels competitive with Claude Opus 4.5 and GPT-5.2.

The model also achieves strong multilingual results and maintains low hallucination rates thanks to targeted RL training. Consequently, enterprises adopt GLM-5 for mission-critical applications where reliability matters.

How to Access the GLM-5 API

Accessing the GLM-5 API requires only a few straightforward steps:

1. Create an account — Visit z.ai (international) or open.bigmodel.cn (China mainland) and register or log in.
2. Top up your balance — Navigate to the billing page and add credits. Free trial credits are often available for new users.
3. Generate an API key — Go to the API Keys management section, click “Create new key,” and copy the token immediately. Store it securely—never commit it to version control.
4. Choose your endpoint — Use the general base URL https://api.z.ai/api/paas/v4/ for most applications. Coding-specific workloads can use the dedicated coding endpoint when applicable.

Engineers who complete these steps gain immediate access to the glm-5 model identifier.

Authenticating and Making Your First Request

Authentication follows the standard Bearer token pattern. Include the header Authorization: Bearer YOUR_API_KEY with every request.

Here's how to make your first API call using curl:

curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "glm-5",
    "messages": [
      {"role": "system", "content": "You are a world-class software architect."},
      {"role": "user", "content": "Design a scalable microservices architecture for an e-commerce platform."}
    ],
    "temperature": 0.7,
    "max_tokens": 2048
  }'

Python implementation using the OpenAI client library:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.z.ai/api/paas/v4/"
)

response = client.chat.completions.create(
    model="glm-5",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain how to implement sparse attention in transformers."}
    ],
    temperature=0.6,
    max_tokens=1024
)

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

For those preferring the official SDK:

from zai import ZaiClient

client = ZaiClient(api_key="YOUR_API_KEY")
response = client.chat.completions.create(
    model="glm-5",
    messages=[...]
)

Advanced API Features and Parameters

GLM-5 supports several powerful parameters:

• Streaming (real-time output):

stream = client.chat.completions.create(
    model="glm-5",
    messages=[...],
    stream=True
)

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

• Tool calling (for autonomous agents):

{
  "model": "glm-5",
  "messages": [...],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "calculate_metrics",
        "description": "Calculate business metrics",
        "parameters": {
          "type": "object",
          "properties": {...}
        }
      }
    }
  ]
}

• Structured JSON output:

{
  "response_format": {"type": "json_object"}
}

Production Best Practices

1. Error handling: Implement retry logic for rate limits (429) and network issues
2. Caching: Cache frequent requests to reduce costs
3. Secret management: Store API keys in AWS Secrets Manager or Vault
4. Monitoring: Track token usage and response times
5. Batching: Combine multiple requests when possible

Real-World Use Cases

1. Autonomous agents:

# Example of a coding agent
client.chat.completions.create(
    model="glm-5",
    messages=[
        {"role": "system", "content": "You are an expert Python developer."},
        {"role": "user", "content": "Write a FastAPI endpoint that processes CSV uploads"}
    ],
    tools=[...]  # Add git, execution, etc.
)

1. Document processing:

# Process a 100-page PDF
client.chat.completions.create(
    model="glm-5",
    messages=[
        {"role": "user", "content": f"Summarize this document: {pdf_text}"}
    ],
    max_tokens=4096
)

Troubleshooting

Common issues and solutions:

Symptom	Solution
401 Unauthorized	Verify API key is correct and active
429 Too Many Requests	Implement exponential backoff
Empty responses	Check max_tokens isn't too low
JSON parse errors	Validate your request format

For full documentation, visit docs.z.ai.

Next Steps

To quickly experiment with GLM-5:

1. Create a free account at z.ai
2. Generate an API key
3. Try the Python examples above
4. Explore the GLM-5 API reference

The model's combination of open weights, powerful API, and leading benchmarks makes it ideal for developers demanding both capability and flexibility.