Here's a use case: You want to extend the GPT 5.5 quota in you Codex subscription by routing some % of requests to DeepSeek V4 Pro. A router needs to figure out which requests to route where, for the appropriate difficulty level.
Another use case: You have two models on your local device. One is large and fairly powerful but low, the other is smaller, faster and good at tool calls and chat, but not great for writing and reviewing code. If you route between them per request, you can get a better developer experience with preserved performance.
The linked repo aims to help you achieve these things, as do I with the role-model router and protocol that I linked in another comment.
to add to that, for example at the end of implementing a task, where the model runs the formatters, linters, tests, commits, pushes, this could be done by a very cheap model, and only switch to the main model again if something fails hard
there are some cache-busting considerations, but solvable
indeed. i also wrote elsewhere that the current ideal number of models in a pool is probably 2, so if you route between two both will have warm cache, though not the full cache at all times, so you lose a little but not much.