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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 strong interface for AI-assisted coding, but the interface and the model are separate layers. If your Claude Code setup supports OpenAI-compatible providers, you can keep the same coding workflow while swapping the backend model.

GLM-5.1 is worth testing in that setup. Z.AI released GLM-5.1 as its flagship model for agentic engineering, with published results including #1 on SWE-Bench Pro, a large improvement over GLM-5 on Terminal-Bench 2.0, and stronger long-horizon behavior on coding tasks that run for many iterations.

If you already like how Claude Code handles files, tools, and iterative edits, this guide shows how to run GLM-5.1 behind that same interface.

If you're comparing model backends for a coding workflow, Apidog can help on the API side. You can document the BigModel endpoint, test OpenAI-compatible responses, and validate how your internal tooling handles different providers before wiring them into production systems.

This guide covers:

  • the exact Claude Code configuration values
  • how the BigModel request path works
  • a small validation workflow
  • common setup issues
  • when GLM-5.1 is worth using inside Claude Code

Why use GLM-5.1 with Claude Code?

There are three practical reasons to try this setup.

1. Keep Claude Code's workflow, change the model

Claude Code is useful because it can inspect files, propose edits, iterate on bugs, and stay inside a coding loop.

If your setup supports custom OpenAI-compatible providers, you can keep that workflow while routing requests to GLM-5.1 instead of the default backend.

2. Test a model built for longer coding sessions

GLM-5.1's strongest published results are focused on long-running, tool-heavy coding tasks rather than short answers. Z.AI showed improvements across hundreds of iterations and thousands of tool calls on optimization tasks.

That maps well to Claude Code-style usage, where you usually run a coding session instead of asking one isolated prompt.

3. Add another cost/performance option

Depending on your workload, GLM-5.1 may be useful as another backend for coding-heavy sessions.

The BigModel API uses quota rather than the usual per-token pricing pattern, so it can be worth comparing against Anthropic or OpenAI backends for your own usage.

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For the full model overview and benchmark context, see what is GLM-5.1.

Prerequisites

Before configuring Claude Code, make sure you have:

  • a BigModel account at https://bigmodel.cn
  • a BigModel API key
  • Claude Code installed locally
  • a Claude Code build or configuration path that supports OpenAI-compatible custom providers

The important point: GLM-5.1 does not require a special GLM SDK. It works through BigModel's OpenAI-compatible API.

Configuration values

You only 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 depends on where your Claude Code setup expects provider settings.

Step 1: Create and store your BigModel API key

Create an API key in the BigModel developer console.

Then save it as an environment variable:

export BIGMODEL_API_KEY="your_api_key_here"
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If you use zsh, add it to:

~/.zshrc
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If you use bash, add it to one of:

~/.bashrc
~/.bash_profile
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Reload your shell:

source ~/.zshrc
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Or, for bash:

source ~/.bashrc
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Verify the variable is available:

echo $BIGMODEL_API_KEY
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If nothing prints, Claude Code will not be able to authenticate with BigModel.

Avoid hardcoding the key in project files. Environment variables are easier to rotate and less likely to be committed by accident.

Step 2: Update Claude Code settings

In many setups, Claude Code stores local settings in:

~/.claude/settings.json
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A minimal OpenAI-compatible provider configuration looks like this:

{
  "model": "glm-5.1",
  "baseURL": "https://open.bigmodel.cn/api/paas/v4/",
  "apiKey": "your_bigmodel_api_key"
}
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If your Claude Code build supports environment variable expansion, prefer that instead of pasting the raw key.

Example:

{
  "model": "glm-5.1",
  "baseURL": "https://open.bigmodel.cn/api/paas/v4/",
  "apiKeyEnv": "BIGMODEL_API_KEY"
}
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The exact field names can vary by Claude Code build, but the pattern is the same:

  • provider mode: OpenAI-compatible
  • base URL: BigModel
  • model: glm-5.1
  • auth: your BigModel API key

If you already configured Claude Code for another OpenAI-compatible provider, this should be a small config change.

Step 3: Test the BigModel API directly

Before debugging Claude Code, confirm the BigModel endpoint works with a 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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This test verifies:

  • your API key is valid
  • the model name is correct
  • the endpoint is reachable
  • BigModel returns an OpenAI-style chat completion response

This is also why the Claude Code integration works: Claude Code only needs a backend that speaks the OpenAI-compatible chat completions format.

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

Step 4: Run a small Claude Code validation task

Do not start with a large repo. First, run a small task to validate the integration.

Good first prompts:

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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You are checking four things:

  • Claude Code accepts the configuration
  • BigModel authentication works
  • GLM-5.1 returns responses in the expected format
  • tool-use behavior inside Claude Code still works cleanly

If those pass, move to a real repository task.

Best tasks for GLM-5.1 inside Claude Code

GLM-5.1 is most useful when the coding task benefits from iteration.

Good fits

  • bug fixing across multiple files
  • repository exploration and summarization
  • test generation
  • test repair
  • iterative refactoring
  • performance tuning
  • long-running agent loops
  • benchmark-driven code improvement

Less ideal fits

  • pure writing tasks
  • short factual questions
  • very small one-shot edits
  • workflows where Claude's native behavior is more important than the backend swap

The best use case is a sustained coding session where the model needs to inspect, edit, test, and iterate.

GLM-5.1 vs Claude inside Claude Code

GLM-5.1 is not automatically better than Claude for every coding task.

Claude still has strengths in reasoning-heavy edits, instruction following, and some repository navigation workflows. GLM-5.1 is worth benchmarking when your tasks look like SWE-Bench-style coding or long tool-driven sessions.

To compare fairly, run both models on the same repository task and track:

  • code quality
  • number of turns required
  • test pass rate
  • tool-use behavior
  • latency
  • cost or quota usage

A simple comparison format:

| Metric | Claude | GLM-5.1 |
|---|---:|---:|
| Turns to solution |  |  |
| Tests passed |  |  |
| Manual fixes needed |  |  |
| Latency |  |  |
| Cost/quota usage |  |  |
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If GLM-5.1 solves the same task with similar quality and lower effective cost, it may be a good backend option. If Claude consistently produces cleaner changes in your workflow, keep using Claude for those tasks.

Side-by-side testing is more useful than model opinions.

Common problems and fixes

Authentication failed

This usually means the API key is wrong or Claude Code is not reading it.

Check:

  • the key works in a raw curl request
  • the environment variable is loaded in the current shell
  • the config file points to the correct key field
  • the key has no trailing spaces
  • JSON quotes are valid

Run:

echo $BIGMODEL_API_KEY
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Then test:

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": "Say hello"
      }
    ]
  }'
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Model not found

Make sure the model name is exactly:

glm-5.1
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Do not use a longer or guessed version name.

Claude Code ignores the custom provider

Some setups cache settings or require a restart after config changes.

Try:

  1. Save the config file.
  2. Restart Claude Code.
  3. Run a small test prompt.
  4. Confirm the provider settings are loaded from the expected config path.

Requests work, but output quality feels off

This may be a task-fit issue rather than a setup issue.

Try:

  • lowering temperature if your config allows it
  • giving clearer repo-specific instructions
  • asking for a plan before edits
  • using GLM-5.1 on iterative coding tasks instead of general reasoning prompts

Example prompt:

Inspect the failing tests first. Do not edit files yet. Explain the likely root cause and list the files you need to inspect next.
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Then continue with:

Apply the smallest safe fix, run the relevant tests, and summarize the diff.
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Quota drains too fast

GLM-5.1 uses quota multipliers on BigModel. Peak hours cost more than off-peak.

For long coding sessions:

  • run heavy jobs off-peak when possible
  • reduce unnecessary context
  • start with smaller validation tasks
  • avoid repeatedly sending large files unless needed

Testing the integration with Apidog

If you want to validate the setup outside Claude Code, Apidog is useful for testing the BigModel endpoint directly.

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A practical workflow:

  1. Define the BigModel chat completions endpoint in Apidog.
  2. Save a request using model glm-5.1.
  3. Send a normal completion request.
  4. Test error cases, such as invalid auth.
  5. Test rate-limit behavior if applicable.
  6. Mock the endpoint so internal tools can be tested without consuming quota.

Example endpoint:

POST https://open.bigmodel.cn/api/paas/v4/chat/completions
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Example request body:

{
  "model": "glm-5.1",
  "messages": [
    {
      "role": "user",
      "content": "Write a TypeScript function that validates an email address."
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.3
}
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This is useful if your team is building wrappers around AI coding tools or routing traffic between multiple model providers. With Apidog's Smart Mock and Test Scenarios, you can verify API behavior independently from the editor integration.

Should you use GLM-5.1 with Claude Code?

Use GLM-5.1 with Claude Code if you want to test a strong agentic coding model without changing your coding interface.

It is especially worth trying if:

  • you already use Claude Code daily
  • your tasks involve multi-step coding sessions
  • you want another backend option
  • you are cost sensitive
  • you want to benchmark multiple models against the same coding loop

Claude may still be the better fit for short editing help, careful reasoning, or workflows where its native behavior works best for you.

But if you do sustained code work with iterative fixes and tool-heavy agent loops, GLM-5.1 is worth testing.

Conclusion

Using GLM-5.1 with Claude Code requires three main values:

Base URL: https://open.bigmodel.cn/api/paas/v4/
Model: glm-5.1
Auth: Bearer YOUR_BIGMODEL_API_KEY
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Because BigModel exposes an OpenAI-compatible API, the integration is mostly a provider configuration change.

The main reason to do this is practical benchmarking. Run GLM-5.1 on the same Claude Code tasks you already care about, compare the results, and decide whether it deserves a place in your backend options.

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/
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What model name should I enter?

Use:

glm-5.1
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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 pattern works for tools like Cline, Roo Code, and OpenCode.

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

No. It depends on your workflow. The best way to decide is to run the same repository tasks through both and compare the results.

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