IBM Research: Model Routing Is Deceptively Complex — A Framework for Getting It Right

IBM Research published a detailed technical breakdown of model routing — the practice of dynamically dispatching requests to different models based on query characteristics — and explains why naive implementations fail at scale. The post covers the full complexity surface: latency vs. accuracy tradeoffs, cold-start problems, distribution shift in routing signals, and the challenge of defining 'correct' routing when ground truth is ambiguous. For developers building multi-model pipelines or cost-optimizing inference by mixing frontier and smaller models, this is directly actionable — it surfaces failure modes that aren't obvious until you're in production. The piece also introduces a framework for thinking about routing as a first-class system design problem, not an afterthought. As multi-model architectures become standard, routing logic will increasingly determine the practical performance ceiling of AI products.