Unlock Seamless AI Coding: How to Pair Claude Code with GLM‑4.6 on Windows

Background

Access to Claude large models in China often faces blocks or requires VPNs, making Claude Code inconvenient. Zhipu AI released GLM‑4.6 around the National Day holiday, which offers strong compatibility and can fully support Claude Code.

Claude Code Overview

Claude Code, released by Anthropic in 2024, is a terminal‑based AI programming assistant that provides real‑time code generation, file operations, command execution, multi‑file project understanding, debugging, and refactoring. It integrates deeply with IDEs, supports complex multi‑step automation, and allows developers to describe requirements in natural language for rapid code production.

GLM‑4.6 Model Overview

GLM‑4.6 is a large language model from Zhipu AI (originating from Tsinghua University’s KEG). Trained with large‑scale pre‑training, instruction fine‑tuning, and RLHF, it excels in 128K context windows, function calling, multimodal understanding, and especially Chinese comprehension and coding capabilities.

GLM‑4.6 Core Features

Function Calling: native support for parallel and nested calls with automatic parameter extraction and chaining.

API Compatibility: highly compatible with OpenAI and Anthropic Claude APIs, supporting RESTful, WebSocket, and streaming outputs.

Long‑Text Handling: context window over 200K tokens for efficient document processing and extraction.

Programming Ability: proficient in dozens of languages, capable of code generation, understanding, refactoring, debugging, unit‑test creation, and documentation.

Multimodal Support: text generation, image understanding, PDF/Word parsing, and table data handling.

Reasoning: strong logical, mathematical, and multi‑step planning capabilities.

Chinese Optimization: excellent performance on modern Chinese, classical poetry, and domain knowledge.

Tool Integration: seamless use of calculators, date handlers, data analysis, and web search tools.

Safety: built‑in content filtering and value alignment to avoid harmful output and protect privacy.

GLM‑4.6 Function Call Details

Accurate function selection based on user intent, supporting large function libraries.

Complex parameter handling for nested JSON objects, arrays, optional parameters.

Concurrent function calls with dependency management and parallel execution.

Context‑aware calls that consider dialogue history and support multi‑turn interactions.

Error handling with intelligent parameter completion, type correction, and retry mechanisms.

Technical compatibility with OpenAI Function Calling spec, high performance, flexibility, and extensibility.

Application scenarios: API automation, database queries, external service integration, workflow orchestration, real‑time data processing.

Preparation

Claude Code + GLM‑4.6 works on Windows 11 without WSL; only Git is required. Ensure the following environment:

Operating System: Windows 11 (no WSL needed)

Node.js LTS version

Git Bash (Claude Code depends on Bash, not CMD/PowerShell)

For OpenRouter (Claude‑4.5‑sonnet) usage, refer to the linked documentation.

Installation Steps

Install Node.js

Download the LTS Windows installer from nodejs.org and verify with:

node -v
npm -v

Install Git Bash (Windows Only)

Download and install Git for Windows from the official site.

Install VS Code (Optional)

Download VS Code from code.visualstudio.com . After installation, you may add the "Claude Code for VSCode" extension.

Install Claude Code

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

Activate GLM‑4.6 Service

Register on the Zhipu AI platform and obtain an API key.

Save the key for later configuration.

Configure Environment Variables (User Scope)

Windows (graphical method): add the following variables in the system environment settings:

ANTHROPIC_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxx"
ANTHROPIC_AUTH_TOKEN="sk-xxxxxxxxxxxxxxxxxxxxxx"
ANTHROPIC_BASE_URL="https://open.bigmodel.cn/api/anthropic"

macOS (shell method): add the same variables to ~/.zshrc and reload:

export ANTHROPIC_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxx"
export ANTHROPIC_AUTH_TOKEN="sk-xxxxxxxxxxxxxxxxxxxxxx"
export ANTHROPIC_BASE_URL="https://open.bigmodel.cn/api/anthropic"
source ~/.zshrc

Start Using Claude Code

Open a terminal in your project directory and run: claude Enter natural‑language commands, e.g., "Create an Express server" or "Read config.json". You can also execute one‑off tasks without entering interactive mode:

claude "Create a React component"
claude edit src/app.js "Add error handling"
claude generate "Create unit tests for utils.js"
claude -p "Analyze this project architecture"
claude -c   # continue previous session
claude --dangerously-skip-permissions   # skip permission checks during development
claude --allowedTools "Read" "Write" "Bash(git *)"
claude --model opus   # specify model
cat error.log | claude -p "Analyze this error"
claude mcp   # configure MCP server

Important Tips

Use arrow keys to browse command history.

Press Esc to stop Claude execution (not Ctrl+C).

Press Ctrl+C to exit Claude Code.

Reference files with @filename and run shell commands with !command.

First run requires authentication.

Use absolute paths for MCP server configuration.

Ensure Claude Code has proper file system permissions.

View detailed logs with claude logs.

Suitable Scenarios

For complex engineering tasks, a mixed‑model strategy (choosing the best model per stage) outperforms a single‑model approach. Model strengths differ across design, coding, and bug‑fix phases.

Model Capability Summary

Top Combination : GPT‑5 for planning/debugging + Claude Sonnet for execution.

Design & planning: GPT‑5 provides systematic thinking.

Code generation: Claude Sonnet 4.5 delivers high‑quality, production‑ready code.

Complex bug fixing: GPT‑5 excels in logical reasoning and root‑cause analysis.

Average Combination : Claude Code + GLM‑4.6.

Design: shallow and rushed.

Code quality: inconsistent, patch‑style modifications.

Bug fixing: limited capability, often fails after many attempts.

Final Recommendations

For large‑scale, engineering‑level development, adopt an augment‑style workflow: use GPT‑5 for architecture and complex debugging, and Claude Sonnet for concrete implementation.

For lightweight, daily office tasks (document handling, data summarization, email drafting), Claude Code + GLM‑4.6 or Gemini provides fast response and adequate quality.

Overall, model specialization is key to high‑quality, efficient AI‑assisted development; a toolbox that leverages each model’s strengths outperforms a single‑model solution.