CARROT: A Cost Aware Rate Optimal Router

Seamus Somerstep; Felipe Maia Polo; Allysson Flavio Melo de Oliveira; Prattyush Mangal; Mírian Silva; Onkar Bhardwaj; Mikhail Yurochkin; Subha Maity

arXiv · 2502.03261

CARROT: A Cost Aware Rate Optimal Router

Seamus Somerstep·Felipe Maia Polo·Allysson Flavio Melo de Oliveira·Prattyush Mangal·Mírian Silva·Onkar Bhardwaj·Mikhail Yurochkin·Subha Maity

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Abstract

With the rapid growth in the number of Large Language Models (LLMs), there has been a recent interest in LLM routing, or directing queries to the cheapest LLM that can deliver a suitable response. We conduct a minimax analysis of the routing problem, providing a lower bound and finding that a simple router that predicts both cost and accuracy for each question can be minimax optimal. Inspired by this, we introduce CARROT, a Cost AwaRe Rate Optimal rouTer that selects a model based on estimates of the models' cost and performance. Alongside CARROT, we also introduce the Smart Price-aware ROUTing (SPROUT) dataset to facilitate routing on a wide spectrum of queries with the latest state-of-the-art LLMs. Using SPROUT and prior benchmarks such as Routerbench and open-LLM-leaderboard-v2 we empirically validate CARROT's performance against several alternative routers.

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BibTeX RIS

Seamus Somerstep, Felipe Maia Polo, Allysson Flavio Melo de Oliveira, Prattyush Mangal, Mírian Silva, Onkar Bhardwaj, Mikhail Yurochkin, Subha Maity. 2025-02-05. CARROT: A Cost Aware Rate Optimal Router. https://arxiv.org/abs/2502.03261

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