Companies sell AI that picks the best model for your question, but it barely beats just randomly grouping similar questions together.

This explores why paid or commercial routing services, which decide which language model should handle each query, often fail to beat much simpler methods, and what that tells us about where the real difficulty lies. The short answer from the corpus is that most routers are working against the same hard limit. Once you test them under fair, shared conditions, their claimed advantages mostly disappear.

The main evidence comes from a large benchmark: about 400,000 test cases across 21 datasets and 33 models. When leading routing methods were evaluated under one shared setup, they performed almost the same, and some commercial routers did worse than a simple baseline Do leading LLM routers actually outperform simpler alternatives?. The surprising detail is that lightweight routers, which just group similar queries into clusters and send each cluster to whichever model does well on it, matched methods that needed trained neural networks. So the sophistication doesn't buy much accuracy. Part of the reason earlier routers looked better is that they were usually tested on different setups. A framework that breaks every router into five comparable components was built partly so that fair, like-for-like comparisons become possible Can five components unify all LLM routing approaches?.

The more interesting question is why everyone ends up in the same place. The same benchmark found that routers all fall well short of the 'oracle', meaning a perfect router that always knows which model will get the answer right. Adding more models to choose from doesn't close that gap. What does hold routers back is **model recall**: failing to recognize which of the available models could have answered a given question correctly What's really holding back LLM routing performance?. That is a prediction problem, and every router faces it, whether it is a clustering heuristic or a commercial product.

That difficulty comes from when routing happens. A router decides before any model has written an answer, so it has to guess from the query alone how hard the query is and which model fits it. It can't judge an actual response Can routers select the right model before generation happens?. Systems like RouteLLM still cut costs by 40–50% this way, so routing does pay off. But the gains come mostly from the coarse call ('this is easy, send it to the cheap model'), which a simple method can make about as well as an elaborate one. Finer judgments, such as knowing that model B gets this particular tricky question right while model A doesn't, are exactly where a pre-generation guess runs out of information.

The takeaway you might not have expected: a router's ceiling is set less by how clever it is and more by how much can be predicted about a model's success from the question alone. If you're choosing a router, a cheap clustering baseline is a serious contender. The open research problem isn't building fancier routers. It's learning to predict which models will succeed on a given question.