How to Use GLM-4.5 with Claude Code (2026)

GLM-4.5 is Zhipu AI's latest large language model, offering strong multilingual capabilities, improved reasoning, and competitive performance on coding benchmarks. Claude Code, Anthropic's CLI-based AI coding assistant, supports custom model providers through its configuration system. This guide walks you through connecting GLM-4.5 to Claude Code so you can leverage both tools in your development workflow.

Why Combine GLM-4.5 with Claude Code?

There are several practical reasons to integrate GLM-4.5 into your Claude Code setup:

Multilingual support: GLM-4.5 excels at Chinese and other Asian language tasks, complementing Claude's English-first strengths.
Cost optimization: Route simpler tasks to GLM-4.5 while reserving Claude for complex reasoning.
Model diversity: Different models catch different bugs. Using multiple models improves code review coverage.
Fallback availability: If one provider has downtime, you can switch to the other without changing your workflow.

Prerequisites

Before starting, make sure you have:

Claude Code installed (version 1.0 or later):

npm install -g @anthropic-ai/claude-code
claude --version

A Zhipu AI API key: Sign up at open.bigmodel.cn and generate an API key from the console.
Node.js 18+ installed on your system.

Step 1: Get Your GLM-4.5 API Key

Navigate to the Zhipu AI developer console and create a new API key:

Log in to open.bigmodel.cn.
Go to API Keys in the sidebar.
Click Create API Key and copy the generated key.
Store it securely -- you will need it in the next step.

Step 2: Configure an OpenAI-Compatible Proxy

GLM-4.5 exposes an OpenAI-compatible API endpoint. You can use this directly with tools that support custom OpenAI base URLs. The base URL for Zhipu AI's API is:

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

Set your environment variables:

export GLM_API_KEY="your-zhipu-api-key-here"
export GLM_BASE_URL="https://open.bigmodel.cn/api/paas/v4"

To make these persistent, add them to your shell profile (~/.bashrc, ~/.zshrc, etc.):

echo 'export GLM_API_KEY="your-zhipu-api-key-here"' >> ~/.zshrc
echo 'export GLM_BASE_URL="https://open.bigmodel.cn/api/paas/v4"' >> ~/.zshrc
source ~/.zshrc

Step 3: Use LiteLLM as a Bridge

The most reliable way to connect GLM-4.5 to Claude Code is through LiteLLM, which acts as a universal proxy between different LLM providers. Install it:

pip install litellm

Create a LiteLLM config file at ~/litellm_config.yaml:

model_list:
  - model_name: glm-4.5
    litellm_params:
      model: openai/glm-4.5
      api_key: os.environ/GLM_API_KEY
      api_base: https://open.bigmodel.cn/api/paas/v4
  - model_name: glm-4.5-flash
    litellm_params:
      model: openai/glm-4.5-flash
      api_key: os.environ/GLM_API_KEY
      api_base: https://open.bigmodel.cn/api/paas/v4

Start the LiteLLM proxy:

litellm --config ~/litellm_config.yaml --port 4000

Step 4: Point Claude Code at the Proxy

Now configure Claude Code to use GLM-4.5 through the LiteLLM proxy. You can do this per-session using the --model flag and environment variables:

ANTHROPIC_BASE_URL=http://localhost:4000 claude --model glm-4.5

Or for a more permanent setup, create a wrapper script:

#!/bin/bash
# ~/bin/claude-glm
export ANTHROPIC_BASE_URL=http://localhost:4000
claude --model glm-4.5 "$@"

Make it executable:

chmod +x ~/bin/claude-glm

Now you can run:

claude-glm "explain this function and suggest improvements"

Step 5: Verify the Connection

Test the setup with a simple prompt:

ANTHROPIC_BASE_URL=http://localhost:4000 claude --model glm-4.5 -p "What model are you? Respond in one sentence."

You should see a response identifying itself as GLM-4.5. If you get a connection error, verify that:

The LiteLLM proxy is running on port 4000.
Your GLM_API_KEY environment variable is set.
The Zhipu AI API endpoint is reachable from your network.

GLM-4.5 Model Variants

Zhipu AI offers several model variants. Here is a comparison:

Model	Context Window	Best For	Speed	Cost
`glm-4.5`	128K tokens	Complex reasoning, coding	Medium	Higher
`glm-4.5-flash`	128K tokens	Fast responses, simple tasks	Fast	Lower
`glm-4.5-long`	1M tokens	Large codebase analysis	Slow	Higher
`glm-4.5-vision`	128K tokens	Image + code tasks	Medium	Higher

Practical Use Cases

Translate documentation to Chinese:

claude-glm "translate this README.md to Chinese, preserving all markdown formatting"

Code review with a different perspective:

git diff --staged | claude-glm -p "review these changes for potential bugs"

Generate bilingual comments:

claude-glm "add bilingual (English/Chinese) JSDoc comments to all exported functions in src/utils/"

Troubleshooting

Problem	Solution
Connection refused on port 4000	Start LiteLLM with `litellm --config ~/litellm_config.yaml --port 4000`
401 Unauthorized	Check that your `GLM_API_KEY` is valid and not expired
Model not found	Verify the model name in your LiteLLM config matches exactly
Slow responses	Try `glm-4.5-flash` for faster inference
Garbled output	Ensure your terminal supports UTF-8 encoding

Performance Comparison

In our testing, here is how GLM-4.5 compares to Claude when used through Claude Code:

Task	Claude Sonnet 4	GLM-4.5	Notes
Python code generation	Excellent	Good	Claude handles edge cases better
Chinese documentation	Good	Excellent	GLM-4.5 produces more natural Chinese
Code review	Excellent	Good	Both catch common issues
Large file analysis	Good	Good	GLM-4.5-long handles 1M tokens
Response speed	Fast	Fast	GLM-4.5-flash is competitive

Conclusion

Integrating GLM-4.5 with Claude Code gives you access to a powerful multilingual model directly in your terminal-based development workflow. The LiteLLM proxy approach is clean, flexible, and lets you switch between models without changing your tooling.

If you are building applications that need AI-powered media generation -- images, videos, audio, or talking avatars -- check out Hypereal AI. Hypereal provides a unified API with pay-as-you-go pricing and access to the latest generative models, so you can focus on building your product instead of managing infrastructure.