I Built an AI Router That Decomposes Your Prompt and Dispatches to the Best Model

A developer built Flint, a smart routing engine that decomposes prompts into subtasks and dispatches each to the best-suited LLM, then synthesizes the results. The tool, available via a live demo and OpenAI-compatible API, aims to replace manual model selection by automatically choosing models like Claude, GPT, and Gemini based on the task.

When you call an LLM API, you pick a model and hope it's the right one. Claude for reasoning, GPT for code, Gemini for long context... you're doing the routing manually. I got tired of this, so I built Flint — a smart routing engine that does the model selection for you. Instead of forwarding your prompt to a single model, Flint: You: "Compare Python and Go for an API gateway" ↓ Decompose / Research Analysis qwen3.7 deepseek \ / Synthesize ↓ Structured comparison with performance data Here's what Flint produced for "Compare Python and Go for building an API gateway": 1. Performance — Go compiles to native code, handles tens of thousands of concurrent connections. Python's GIL limits throughput even with async frameworks. 2. Concurrency — Go's goroutines ~4KB stack outperform Python's asyncio by an order of magnitude for I/O-heavy workloads. 3. Ecosystem — Go dominates the gateway space Traefik, Caddy, Kong plugins . Python has frameworks but lacks dedicated gateway projects. The research subtask fetched actual benchmark data, the analysis subtask structured the comparison, and the synthesizer wove it together. One prompt, multiple models, one answer. base url and keep your existing codeThere's a live demo https://flintapi.ai/demo — no signup needed. Type any question and see Flint's routing vs. a direct model call side by side. pip install openai + set base url="https://flintapi.ai/v1" Open to feedback — what would you want a smart router to do that it doesn't do yet?