Benchmarking GoModel vs LiteLLM (alternative): lessons learned from building a simple benchmark

Benchmarking GoModel vs LiteLLM: lessons learned from building a simple benchmark When I started working on GoModel, I did not plan to spend much time on benchmarking. I assumed benchmarking would be annoying, fragile, and probably much harder than it looked. In my head, it felt like one of those tasks that sounds simple at first, but turns into a mini research project once you actually start. What I learned is the opposite: creating a useful benchmark is much easier than most people think. And one big reason is that AI makes the whole process much easier than it was a few years ago. That was the biggest lesson for me. What is GoModel? GoModel is an open-source AI gateway / LLM proxy written in Go. It sits between your app and model providers like OpenAI, Anthropic, Gemini, Groq, xAI, and Ollama, and exposes a single OpenAI-compatible API. I built it because I wanted a lightweight, production-friendly gateway that was easy to deploy, easy to reason about, and fully open-source. Why I d