AI RESEARCH
Cost Trade-offs of Reasoning and Non-Reasoning Large Language Models in Text-to-SQL
arXiv CS.AI
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ArXi:2512.22364v2 Announce Type: replace-cross While Text-to-SQL systems achieve high accuracy, existing efficiency metrics like the Valid Efficiency Score prioritize execution time, a metric we show is fundamentally decoupled from consumption-based cloud billing. This paper evaluates cloud query execution cost trade-offs between reasoning and non-reasoning Large Language Models by performing 180 Text-to-SQL query executions across six LLMs on Google BigQuery using the 230 GB StackOverflow dataset.