Simple AI routers that cluster similar questions can match fancy trained ones at picking the best model, for less cost.

This explores whether a router that sorts incoming questions into groups by meaning, then sends each group to its best-performing model, can keep up with routers built on trained neural networks. The corpus gives a fairly direct answer: yes, mostly. When a 400,000-instance benchmark covering 21 datasets and 33 models tested routers under the same conditions, the leading methods ended up performing about the same. Lightweight clustering-based routers matched the trained ones without any neural network training, and some commercial routers didn't beat a simple baseline at all Do leading LLM routers actually outperform simpler alternatives?. So the extra machinery in a neural router doesn't reliably buy better choices.

The clustering approach looks strong even outside head-to-head tests. Avengers-Pro embeds each query, finds its semantic cluster, and sends it to the model that does best on that cluster. It beat GPT-5-medium on accuracy by 7%, or matched it at 27% lower cost. An earlier version, using ten 7B models plus routing, beat GPT-4.1 and GPT-4.5 Can routing beat building one better model?. For comparison, difficulty-predicting routers like RouteLLM and Hybrid-LLM cut costs by 40–50% by guessing how hard a query is before any model generates an answer Can routers select the right model before generation happens?. Both kinds of router make one cheap decision up front, so they win on cost for the same reason. That shared structure may explain why they perform so similarly.

The less obvious finding is why they all stall. Every router, clustered or neural, still falls well short of the 'oracle', a perfect router that always knows which model will get the answer right. The gap mostly isn't caused by having too few models to choose from. It comes from failing to recognize which of the available models could answer a given question correctly What's really holding back LLM routing performance?. Cluster-based and neural routers seem to hit the same limit: they can't fully predict what each model is capable of. A more elaborate router doesn't fix that.

If you want to compare routers yourself, one framework breaks every router into five interchangeable parts and treats routing as a step-by-step decision process. That lets you swap one part at a time and see where the gains actually come from, rather than comparing whole systems that differ in many ways at once Can five components unify all LLM routing approaches?. Two related ideas suggest where the field may go next. A deployed router naturally logs which model handled what and how it turned out, and those logs can become training data for improving the models themselves Can a routing harness generate its own training data automatically?. In multi-agent systems, the same match-by-meaning idea shows up as capability vectors: agents are described by embeddings and looked up by similarity, with budget and policy limits applied, instead of being wired together by hand Can semantic capability vectors replace manual agent routing?.

The takeaway: for routing, how well you understand each model's strengths matters more than how sophisticated the router is. Simple clustering is a strong default. The real gains are in better predicting which model can actually solve a given query.