Understanding Tone-Dependent Inference Cost in Large Language Models

Abstract:We examine how prompt tone affects both accuracy of the LLM answers and inference cost as reflected in output-token consumption. Experiments were performed to understand the trade-offs between accuracy and inference cost on a 570 Question MMLU dataset for LLM models prompted in seven different tones from sycophantic to threatening. Our results show that the output-token-length variation substantially exceeded accuracy variation across all models. Output-token consumption varied by up to 44.3% across tone conditions. We also analyzed the tradeoff between the accuracy of the answers and the average output token length in the reasoning process. For the ChatGPT models 4o and 5-nano, the rude tone is quite dominant. For the Gemini models 2.5 Flash and 2.5 Flash Lite, the rude and neutral tones are dominant on the Pareto-optimal frontier. We find that prompt tone influences not only answer quality but also the amount of billable inference resources consumed by modern LLMs.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.23915 [cs.CL]

(or arXiv:2607.23915v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.23915 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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