The Problem: One Model Doesn't Fit All

As AI models proliferate, choosing the right one for your task has become increasingly difficult. Different models excel at different things:

1. GPT-4o excels at creative writing and nuanced conversations
2. Claude is known for careful reasoning and following instructions
3. Gemini offers strong multimodal capabilities and long context windows
4. Llama 3 provides excellent performance for open-source enthusiasts
5. Mistral delivers impressive results with lower latency

When you're building AI-powered applications or just trying to find the best model for your workflow, you need to actually compare outputs side-by-side. Opening multiple browser tabs to different AI playgrounds is tedious. Copy-pasting prompts between them is error-prone. And tracking which model performs best for specific tasks? Nearly impossible.

I needed a tool to compare models simultaneously, with real-time streaming, conversation history, and cost tracking—all in one place.

The Vision: A Desktop Command Center for AI Models

I set out to build Model Faceoff: an Electron desktop app that serves as a single interface for comparing any AI model from any provider. The core principles:

Model Faceoff in Action

Side-by-Side Model Comparison

Compare responses from multiple AI models simultaneously. Each model streams its response in real-time, with full markdown rendering and syntax highlighting for code blocks.

Model Selection

Choose from 100+ models across OpenAI, Anthropic, Google, Meta, Mistral, and more—all with a single API key.

Conversation History

All conversations saved locally with full search. Replay any comparison and see how different models responded.

API Logs & Debugging

Full visibility into every API call with request/response details, latency metrics, and token counts for debugging and optimization.

Key Features

The Tech Stack

The Architecture: Electron's Multi-Process Model

Electron enforces security through process isolation:

• Main Process: Node.js environment running main.ts—manages windows, handles system APIs, initializes database, runs auto-updater
• Renderer Process: Browser environment running React—completely isolated from Node.js APIs for security
• Preload Script: Secure bridge exposing specific IPC channels via contextBridge

IPC Communication Pattern

Every feature follows a strict IPC contract with typed responses:

// All IPC handlers return IPCResponse<T>
interface IPCResponse<T> {
  success: boolean;
  data?: T;
  error?: { code: string; message: string };
}

// Renderer calls main via window.api
const result = await window.api.conversations.create({
  modelIds: ['gpt-4o', 'claude-3-opus'],
  title: 'My comparison'
});

// Type-safe access to response
if (result.success) {
  console.log(result.data.conversationId);
}

This pattern provides compile-time safety for all cross-process communication. TypeScript catches type mismatches before runtime, and the standardized response format makes error handling predictable.

The Database Design: Simple and Effective

SQLite with WAL mode provides concurrent read access while maintaining data integrity. The schema is intentionally minimal:

Core Tables

• settings: Key-value store for configuration (API key, preferences, window state)
• conversations: Parent records with title, model IDs, timestamps
• messages: Individual messages with role, content, model ID, token counts
• api_logs: Full request/response logging for debugging

The synchronous API of better-sqlite3 simplifies code significantly—no async/await ceremony for database operations. This makes the codebase more readable and eliminates entire categories of race conditions.

The Streaming Challenge: Real-Time Token Display

Streaming responses from multiple models simultaneously requires careful coordination:

1. User sends prompt → Main process receives via IPC
2. Main process initiates parallel fetch calls to OpenRouter for each selected model
3. As chunks arrive, main process emits events back to renderer via IPC
4. Renderer updates UI in real-time, accumulating tokens per model
5. On completion, full response saved to database with token counts

The challenge: Electron's IPC is event-based, not streaming. Solution: chunked events with model identification:

// Main process emits chunks as they arrive
mainWindow.webContents.send('stream-chunk', {
  modelId: 'gpt-4o',
  chunk: 'Here is the ',
  done: false
});

// Renderer accumulates chunks per model
window.api.onStreamChunk((data) => {
  setResponses(prev => ({
    ...prev,
    [data.modelId]: prev[data.modelId] + data.chunk
  }));
});

This architecture handles any number of concurrent streams without blocking the UI.

Cross-Platform Distribution

Model Faceoff runs on macOS, Windows, and Linux with platform-specific installers:

Electron Forge's publisher-github integration automatically uploads release artifacts to GitHub Releases. The electron-updater package checks for new versions on startup and offers one-click installation.

The Challenges and Trade-Offs

The Results: One Interface for All Models

Conclusion: The Right Tool for the Right Job

Model Faceoff proves that Electron desktop apps remain a powerful choice for developer tools. The combination of web technologies for rapid UI development with Node.js for system access creates applications that feel native while shipping cross-platform.

The key insight: AI model selection shouldn't require faith. Comparing outputs side-by-side reveals differences that specs and benchmarks can't capture. What reads well in a GPT-4o response might lack the precision Claude brings to the same prompt.

With OpenRouter handling the API complexity, the app stays focused on what matters: making comparison effortless.

Stop guessing which model is best. Download Model Faceoff and see for yourself.