I spent a lot of time researching LLM routing last year and also came to the conclusion that it's generally not worth the effort. It's too hard to understand the difficulty of a query a priori.

One specific challenge I was seeing is that difficulty depends a lot on what information is retrievable by the agent. Consider the question "what is the 5-state busy beaver number?" (https://en.wikipedia.org/wiki/Busy_beaver). In 2023 this would be a Mythos-tier research problem, but a solution was proved in 2024 so today any minimally intelligent model with a web search tool can just fetch the answer. You don't know which queries will be basic summarization and which will be deep reasoning until you get going.

You also have problems with short prompts whose results depend highly on the understanding of nuance. The router is going to have to mostly solve the prompt to decide which model to send it to.

What you actually want is a model that can conclude either "I know the answer to this with confidence" and answer, or "I think I don't know the answer to this, I should ask another model and I know which one". But I don't think LLMs can really bring their uncertainty to the surface in that way yet? Their internal confidence can be measured and returned, so you could probably front a more powerful model with a knowledgeable assistant, but they can't consciously mark their own homework?