Picking a model usually feels like a decision you have to live with. You install that provider's SDK, wire in its key, learn its quirks, and the choice is baked into your code. Switching later is a small migration, so most teams pick one model and stick with it even when a cheaper or better one would suit a given task.
Through an AI gateway, the model is just a string in the request. The gateway exposes around 25 models across OpenAI, Anthropic, and Google, and moving between any of them is a one-token change with the same code and the same credential. That matters more than convenience, because the catalog spans a roughly 100x price range. When swapping is free, model choice stops being a one-time architecture decision and becomes a per-task cost lever. This post shows the swap, the price spread that makes it worth caring about, and a real run across several models. The repo is at the end.
TL;DR
Through the gateway, changing model is changing the model string. Same code, same credential, roughly 25 models across three providers.
The catalog spans about 100x in price, from cheap small models to flagship ones, so which model you pick is usually your biggest cost knob.
