Most of the LLM routing gap is task type
Read the original on arXiv Computation and Language →The paper investigates why large‑language‑model (LLM) routers—systems that select the best model for each query—often fail to outperform a single best model. By evaluating 14 models on 294 questions across seven task types and three languages, the authors find that a simple static mapping of task type to model improves 21 of the 29 questions that routing could potentially solve, and that learned routers do not significantly exceed this performance. The study highlights that most routing gains stem from task‑type specialization rather than complex learned decision rules.
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