Claude Code Multi-Model Launcher: Unified AI Provider Access

After months of testing various AI models with Claude Code - from my positive experiences with DeepSeek v3.1 and GLM-4.5 to the frustrating encounter with Qwen3-Coder-Plus - I’ve developed a practical solution for developers who want to easily access multiple AI providers through a unified interface.

I’m excited to share this multi-model launcher. This isn’t just another configuration - it’s a streamlined approach that addresses the core challenges I faced when working with different models.

The Problem: The Hassle of Multiple Configurations

When I started testing different models with Claude Code, I quickly encountered several frustrating issues:

1. Configuration complexity - Each model required different setup processes
2. Environment management - Juggling multiple API keys and endpoints

After my negative experience with Qwen3-Coder-Plus (which you can read about in my previous post), I realized I needed a more systematic approach - one that would let me access different models without the configuration overhead.

The Solution: A Smart Launcher Script

The result is a sophisticated shell script that acts as a smart launcher for Claude Code, setting up the right environment variables for your chosen AI provider with a simple --model parameter.

How It Works

The .claude.sh script works by:

1. Defining a function - It creates a claude function that sets up the environment
2. Parsing the --model parameter - It supports both --model model_name and --model=model_name formats
3. Setting environment variables - Based on the model name, it configures the appropriate variables
4. Running the real command - It executes the actual claude binary with the proper settings

Supported Providers

The script currently supports four major Chinese AI providers:

Installation: Three Simple Steps

1. Install Claude Code (if not already installed)

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

2. Add the Script to Your Shell Configuration

Add this line to your .zshrc or .bashrc:

source /path/to/your/repo/.claude.sh

3. Set Up Your API Keys

Configure the environment variables for the providers you want to use:

export ZHIPU_API_KEY=your_zhipu_key
export DASHSCOPE_API_KEY=your_alibaba_key
export DEEPSEEK_API_KEY=your_deepseek_key
export MOONSHOT_API_KEY=your_moonshot_key

And that’s it! You’re ready to start using any supported model.

Usage: Simple Model Selection

The beauty of this setup is its simplicity. Instead of managing multiple configurations, you can select your model with a single parameter:

# Use Zhipu's GLM-4.5 for complex coding tasks
claude --model glm-4.5

# Use DeepSeek for cost-effective development
claude --model deepseek-chat

# Try Kimi for creative problem-solving
claude --model kimi-k2-0905-preview

# Use default Claude model (bypasses the script)
claude

The script provides helpful feedback:

🚀 Intercepted model 'glm-4.5'. Routing to zhipu provider...

If you forget to set an API key, it gives you a clear error:

❌ ZHIPU_API_KEY environment variable not set!
Please set it with: export ZHIPU_API_KEY=your_api_key

Key Features That Address Real Challenges

1. Automatic Provider Selection

The script uses a mapping system that automatically selects the correct provider based on the model name. No more memorizing which endpoint goes with which model.

2. Environment Variable Validation

One of the most frustrating experiences is realizing you forgot to set an API key mid-task. The script validates all required environment variables before making any requests.

3. Full Compatibility

The script maintains 100% compatibility with the standard Claude Code interface. All parameters, flags, and functionality work exactly as expected.

4. Flexible Model Parameter

The script supports both --model model_name and --model=model_name formats, giving you the flexibility to use whichever style you prefer.

5. Long Timeout for Complex Tasks

Requests use a 10-minute timeout (API_TIMEOUT_MS=600000) to accommodate the longer processing times needed for complex coding tasks.

The Technical Implementation

Provider Configuration System

The script uses an associative array to configure provider mappings. Each entry contains:

• Environment variable name for the API key
• Base URL for the provider’s Anthropic-compatible API
• Comma-separated list of supported models

typeset -A PROVIDERS=(
  zhipu "ZHIPU_API_KEY|https://open.bigmodel.cn/api/anthropic|glm-4.5"
  alibaba "DASHSCOPE_API_KEY|https://dashscope.aliyuncs.com/api/v2/apps/claude-code-proxy|qwen3-coder-plus"
  deepseek "DEEPSEEK_API_KEY|https://api.deepseek.com/anthropic|deepseek-chat"
  moonshot "MOONSHOT_API_KEY|https://api.moonshot.cn/anthropic|kimi-k2-0905-preview"
)

Provider Selection

The script match the requested model against all providers, setting the appropriate environment variables for the selected provider.

Error Handling

Comprehensive error handling provides clear feedback for common issues:

• CLI not found (with installation instructions)
• Missing API key (with the specific environment variable needed)
• Provider routing confirmation

Practical Benefits: Why This Approach Works

For Model Comparison

This setup makes it incredibly easy to compare different models. No more switching configurations or remembering different endpoints - just change the --model parameter and you’re testing a different AI.

For Cost Optimization

You can easily choose between more expensive, high-quality models for complex tasks and more cost-effective models for routine development.

For Redundancy

If one provider has downtime or rate limits, you can instantly switch to another without changing anything but the model parameter.

For Specialized Use Cases

Different models excel at different tasks. With this setup, you can:

• Use GLM-4.5 for complex architectural decisions
• Switch to DeepSeek for cost-sensitive production work
• Try Kimi for creative problem-solving

Getting Started Today

The full code and setup instructions are available in my GitHub repository: https://github.com/myimilo/my-vibe-coding-setup.

This launcher has significantly improved my development workflow with AI models. Instead of fighting with configurations, I can focus on choosing the right tool for each task - and that makes all the difference in practical, day-to-day coding work.