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Posted on • Originally published at apidog.com

How to use GLM-5.1 with Claude Code: full setup guide

TL;DR

You can use GLM-5.1 with Claude Code by routing Claude Code through the BigModel OpenAI-compatible API. Set the base URL to https://open.bigmodel.cn/api/paas/v4/, use model name glm-5.1, and authenticate with your BigModel API key. Once configured, Claude Code can use GLM-5.1 for coding tasks, repo exploration, refactoring, and longer agent-style workflows.

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Introduction

Claude Code is a powerful interface for AI-assisted coding, but the interface and the model are separate. If your Claude Code setup supports OpenAI-compatible providers, you can swap in a different backend model without disrupting your workflow.

GLM-5.1, released by Z.AI, delivers strong results: #1 on SWE-Bench Pro, a significant jump over GLM-5 on Terminal-Bench 2.0, and better long-horizon performance for multi-iteration coding. If you prefer Claude Code's tooling and workflow, testing GLM-5.1 as a backend is straightforward.

💡 Tip: If you're comparing model backends for coding workflows, Apidog helps you document the BigModel endpoint, mock OpenAI-compatible responses, and test your internal tooling with different providers before integrating them into production.

This guide covers the setup, request flow, what to expect from GLM-5.1 in Claude Code, common problems, and how to assess if this swap fits your workflow.

Why use GLM-5.1 with Claude Code?

Three main reasons:

1. Keep Claude Code's workflow, try a different model

Claude Code can inspect files, propose edits, iterate on bugs, and operate in a coding loop. If your setup allows custom OpenAI-compatible providers, you can keep this workflow while changing the underlying model.

2. GLM-5.1 is optimized for long coding sessions

GLM-5.1 excels in long, iterative coding tasks. Z.AI demonstrated its improvement over hundreds of iterations and thousands of tool calls—ideal for Claude Code-style sessions.

3. Alternative cost/performance option

GLM-5.1 may be a practical alternative for coding-heavy sessions. BigModel API uses quota-based pricing, which can be more predictable for some teams than per-token models from OpenAI or Anthropic.

GLM-5.1 Benchmarks

For model benchmarks and a full overview, see what is GLM-5.1.

What you need before setup

Get these ready:

  1. BigModel account at https://bigmodel.cn
  2. BigModel API key
  3. Claude Code installed locally
  4. A Claude Code build or config path that supports OpenAI-compatible custom providers

GLM-5.1 works because the BigModel API is OpenAI-compatible; there’s no need for a special SDK.

The exact values you need

You need three core values:

Base URL 

https://open.bigmodel.cn/api/paas/v4/
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Model name 

glm-5.1
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Authorization header 

Authorization: Bearer YOUR_BIGMODEL_API_KEY
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Everything else is about where you place these in Claude Code.

Step 1: Create and store your BigModel API key

  1. Open the BigModel developer console and generate an API key.
  2. Store it as an environment variable: 
   export BIGMODEL_API_KEY="your_api_key_here"
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  • For zsh, add to ~/.zshrc
  • For bash, add to ~/.bashrc or ~/.bash_profile
  1. Reload your shell: 
   source ~/.zshrc
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  1. Verify: 
   echo $BIGMODEL_API_KEY
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You should see your key. If not, authentication will fail. Using environment variables is safer for rotation.

Step 2: Update Claude Code settings

Typically, settings are stored in:

~/.claude/settings.json
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Minimal OpenAI-compatible config:

{
  "model": "glm-5.1",
  "baseURL": "https://open.bigmodel.cn/api/paas/v4/",
  "apiKey": "your_bigmodel_api_key"
}
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If your build supports environment variable expansion:

{
  "model": "glm-5.1",
  "baseURL": "https://open.bigmodel.cn/api/paas/v4/",
  "apiKeyEnv": "BIGMODEL_API_KEY"
}
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Adjust field names as needed, but the pattern is:

  • provider: OpenAI-compatible
  • baseURL: BigModel
  • model: glm-5.1
  • auth: your BigModel key

If you’ve used another OpenAI-compatible provider before, this usually takes less than a minute to swap.

Step 3: Understand what Claude Code is doing

Claude Code sends OpenAI-style chat completion requests to BigModel.

Example raw request:

curl https://open.bigmodel.cn/api/paas/v4/chat/completions \
  -H "Authorization: Bearer $BIGMODEL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.1",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function that removes duplicate lines from a file."
      }
    ],
    "max_tokens": 2048,
    "temperature": 0.7
  }'
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No special GLM integration layer is needed—just OpenAI-compatible API formatting.

For full API walkthrough with Python and Node examples, see how to use the GLM-5.1 API.

Step 4: Run a small validation task first

Before using GLM-5.1 on a large repo, test with a simple prompt:

Write a Python script that scans a folder for JSON files and prints invalid ones.
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Refactor this function for readability and add tests.
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Read this file, explain what it does, and suggest two safe improvements.
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Verify:

  1. Claude Code accepts the config
  2. BigModel authentication works
  3. GLM-5.1 responds in expected format
  4. Tool-use behavior inside Claude Code works

If these pass, try a real repo task.

Best tasks for GLM-5.1 inside Claude Code

GLM-5.1 is strongest on iterative coding sessions:

Good fits:

  • Bug fixing across multiple files
  • Repo exploration and summarization
  • Test generation and repair
  • Iterative refactoring
  • Performance tuning
  • Long-running agent loops
  • Benchmark-driven code improvement

Less ideal:

  • Pure writing tasks
  • Short factual queries
  • One-shot edits
  • Workflows where Claude’s unique style is more valuable than the backend swap

Use GLM-5.1 when you want the model to remain productive over extended sessions.

GLM-5.1 vs Claude inside Claude Code

GLM-5.1 is not universally better than Claude. Claude excels at reasoning-heavy edits, instruction following, and repo navigation. GLM-5.1 is strong enough for benchmarking, especially on SWE-Bench style or long, tool-driven tasks.

To compare:

  • Run the same repo task through both
  • Measure:
    • code quality
    • number of turns needed
    • test pass rate
    • tool use
    • latency
    • cost/quota usage

Choose the backend that delivers the best value for your actual workflow.

Common problems and fixes

Authentication failed

  • Check API key in raw curl request
  • Ensure environment variable is loaded
  • Verify config points to the correct key field
  • Remove trailing spaces/quotes

Model not found

Ensure model name is exactly:

glm-5.1
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No extra versioning.

Claude Code ignores the custom provider

  • Save config
  • Restart Claude Code
  • Run a small test prompt

Output quality feels off

  • Lower temperature if possible
  • Give clearer, repo-specific instructions
  • Use on iterative coding tasks, not general reasoning

Quota drains too fast

GLM-5.1 quota is multiplied during peak hours. Run heavy usage off-peak when possible.

Testing the integration with Apidog

Validate your setup by testing the BigModel endpoint directly in Apidog before or alongside Claude Code.

Testing BigModel in Apidog

A practical workflow:

  1. Define the BigModel chat completions endpoint in Apidog
  2. Save a request using model glm-5.1
  3. Test normal completion responses
  4. Test error cases (invalid auth, rate limits)
  5. Mock the endpoint to test internal tools without consuming quota

Apidog's Smart Mock and Test Scenarios let you verify API behavior independently from your editor integration.

Should you use GLM-5.1 with Claude Code?

Yes, if you want to test a strong coding model without losing the Claude Code workflow.

Ideal if:

  • You use Claude Code daily
  • Your tasks involve multi-step coding sessions
  • You want another backend option
  • You're cost sensitive
  • You want side-by-side benchmarking

If your workflow is mostly short edits or careful reasoning, Claude may remain the better fit. For sustained, tool-driven coding, GLM-5.1 is a top candidate right now.

Conclusion

Using GLM-5.1 with Claude Code is straightforward: get your BigModel API key, set the base URL, and use model glm-5.1. The OpenAI-compatible API makes integration and testing simple.

The main point is to see if GLM-5.1 performs well enough in your Claude Code workflow to justify it as a backend. For long, iterative coding sessions and agent loops, it's worth benchmarking.

FAQ

Can Claude Code use GLM-5.1 directly?

Yes, if your Claude Code setup supports OpenAI-compatible custom providers.

What base URL should I use?

Use https://open.bigmodel.cn/api/paas/v4/.

What model name should I enter?

Use glm-5.1.

Do I need a special GLM SDK?

No. GLM-5.1 works through the BigModel OpenAI-compatible API.

Can I use GLM-5.1 with other coding tools too?

Yes. The same setup works for tools like Cline, Roo Code, and OpenCode.

Is GLM-5.1 better than Claude for all coding tasks?

No. Try both on your tasks and compare results.

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