Automatic Transmission for LLM Tiers: Optimizing Cost and Accuracy in Large Language Models

LLM providers typically offer multiple LLM tiers, varying in performance and price. As NLP tasks become more complex and modularized, selecting the suitable LLM tier for each subtask is a key challenge to balance cost and performance. To address the problem, we introduce the LLM Automatic Transmission (LLM-AT) framework that automatically selects LLM tiers without training. LLM-AT consists of Starter, Generator, and Judge. The starter selects the initial LLM tier expected to solve the question, the generator produces a response using the LLM of the selected tier, and the judge evaluates its validity. If the response is invalid, LLM-AT iteratively upgrades to a highertier model, generates a new response, and reevaluates until a valid response is obtained. Additionally, we propose accuracy estimator, allowing the selection of a suitable initial tier without training. Given an input, accuracy estimator estimates the expected accuracy of each LLM tier by computing the valid response rate for top-k similar queries from past inference records. Experiments demonstrate that LLM-AT achieves superior performance while reducing costs, making it a practical solution for realworld applications. Our code is available at https://github.com/hyudsl/LLM-AT .