LLM API Test

🚀 A comprehensive tool for testing and comparing Large Language Model API performance

🌐 🚀 Live Demo - Try it Now!

Languages: English | 中文 | العربية | Deutsch | Español | Français | 日本語

📖 Overview

LLM API Test is a powerful, web-based tool designed to benchmark and compare the performance of various Large Language Model APIs. Whether you're evaluating different providers, optimizing your AI applications, or conducting research, this tool provides comprehensive metrics and insights.

✨ Key Features

🔌 API Support

• OpenAI: GPT-3.5, GPT-4, and latest models
• Google Gemini: Gemini Pro, Gemini Pro Vision
• Custom APIs: Any OpenAI-compatible API endpoint

📊 Performance Metrics

• Response Time: First token latency measurement
• Output Speed: Tokens per second calculation
• Success Rate: API reliability tracking
• Quality Assessment: Response comparison tools

🌐 User Experience

• Multilingual Interface: 7 languages supported
• Responsive Design: Works on desktop and mobile
• Real-time Results: Live performance monitoring
• History Tracking: Persistent test records

☁️ Deployment Options

• Local Development: Simple HTTP server setup
• Static Hosting: Compatible with any static host

🚀 Quick Start

Prerequisites

• Modern web browser (Chrome, Firefox, Safari, Edge)
• Node.js and npm installed
• API keys for the services you want to test

Local Development

1. Clone the repository

   git clone https://github.com/qjr87/llm-api-test.git
   cd llm-api-test

2. Install dependencies and start the server

   npm install
   npm start

   Alternative methods:

   # Using Python 3
   python -m http.server 8000
   
   # Using PHP
   php -S localhost:8000

3. Open in browser Navigate to http://localhost:8000

🔧 Configuration Guide

API Setup

1. Select Protocol: Choose your API provider (OpenAI, Gemini, or Custom)
2. Enter Endpoint: API URL (auto-filled for standard providers)
3. Add API Key: Your authentication key
4. Configure Models: Specify which models to test

Test Parameters

• Test Rounds: Number of iterations per model
• Prompts: Custom test prompts or use defaults
• Concurrency: Parallel request handling

Example Configuration

// OpenAI Configuration
Protocol: "openai"
API URL: "https://api.openai.com/v1/chat/completions"
API Key: "sk-..."
Models: "gpt-3.5-turbo,gpt-4"

// Gemini Configuration
Protocol: "gemini"
API URL: "https://generativelanguage.googleapis.com/v1beta/models"
API Key: "AIza..."
Models: "gemini-pro"

🚀 Deployment

Static Hosting

Deploy to any static hosting service:

• Vercel: vercel --prod
• Netlify: Drag and drop the project folder
• GitHub Pages: Enable in repository settings
• Firebase Hosting: firebase deploy

Docker Deployment

FROM nginx:alpine
COPY . /usr/share/nginx/html
EXPOSE 80
CMD ["nginx", "-g", "daemon off;"]

docker build -t llm-api-test .
docker run -p 8080:80 llm-api-test

📁 Project Structure

llm-api-test/
├── 📄 index.html          # Main application interface
├── 🧠 app.js             # Core application logic & test orchestration
├── 🔌 api-handlers.js    # API protocol implementations
├── 🎨 styles.css         # Responsive UI styling
├── 🌍 i18n.js           # Internationalization & language support
└── 📜 LICENSE           # MIT License

Core Components

• APITester Class: Main test orchestration and UI management
• APIHandler Class: Protocol-specific API implementations
• I18n System: Multi-language support with dynamic loading
• Results Engine: Real-time performance metrics calculation

🛠️ Tech Stack

Frontend

• HTML5: Semantic markup and accessibility
• CSS3: Modern styling with Flexbox/Grid
• Vanilla JavaScript: No framework dependencies
• Web APIs: Fetch, LocalStorage, Internationalization

Architecture

• Modular Design: Separation of concerns
• Event-Driven: Reactive UI updates
• Progressive Enhancement: Works without JavaScript
• Mobile-First: Responsive design principles

Deployment

• Static Hosting: Universal compatibility
• CDN Ready: Global content distribution

📊 Performance Metrics Explained

Metric	Description	Good Range
First Token Time	Time to receive first response token	< 2 seconds
Output Speed	Tokens generated per second	> 10 tokens/sec
Success Rate	Percentage of successful requests	> 95%
Total Time	Complete response generation time	Varies by length

🤝 Contributing

We welcome contributions! Here's how you can help:

Development Setup

1. Fork the repository
2. Clone your fork locally
3. Create a feature branch

   git checkout -b feature/amazing-feature

4. Make your changes
5. Test thoroughly
6. Commit with clear messages

   git commit -m "feat: add amazing feature"

7. Push to your fork
8. Submit a Pull Request

Contribution Guidelines

• Follow existing code style
• Add tests for new features
• Update documentation
• Ensure cross-browser compatibility

Areas for Contribution

• 🌐 Additional language translations
• 🔌 New API provider support
• 📊 Enhanced metrics and visualizations
• 🎨 UI/UX improvements
• 🐛 Bug fixes and optimizations

❓ FAQ

How do I get API keys?

• OpenAI: Visit platform.openai.com
• Google Gemini: Get started at ai.google.dev
• Custom APIs: Check your provider's documentation

Why are my tests failing?

• Verify API key is correct and has sufficient credits
• Check if the API endpoint URL is accurate
• Ensure your IP isn't blocked by the provider
• Try reducing concurrency or test rounds

Can I test custom models?

Yes! Use the "Custom" protocol option and provide:

• Your API endpoint URL
• Authentication method
• Model names

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Thanks to all contributors who help improve this tool
• Inspired by the need for transparent AI performance testing
• Built with ❤️ for the AI development community

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⭐ Star this repo if you find it helpful!

Made with ❤️ by qjr87