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. Our analysis reveals that reasoning models process 44.5% fewer bytes than non-reasoning counterparts while maintaining equivalent correctness at 96.7% to 100%, and that execution time correlates weakly with query cost at $r=0.16$, indicating that speed optimization does not imply cost efficiency. Non-reasoning models also exhibit extreme cost variance of up to 3.4$\times$, producing outliers exceeding 36 GB per query, over 20$\times$ the best model's 1.8 GB average, due to missing partition filters and inefficient joins. We identify these prevalent inefficiency patterns and provide deployment guidelines to mitigate financial risks in cost-sensitive enterprise environments.

翻译：尽管文本到SQL系统已实现较高准确率，但现有效率指标（如有效效率评分）优先考虑执行时间——我们证明该指标与基于消耗量的云计费模式存在根本性脱节。本文通过在Google BigQuery平台上使用230GB的StackOverflow数据集，对六种大语言模型执行180次文本到SQL查询，系统评估了推理型与非推理型大语言模型的云查询执行成本权衡。分析表明：推理模型在保持96.7%至100%等效正确率的同时，处理字节数比非推理模型减少44.5%；执行时间与查询成本相关性较弱（r=0.16），表明速度优化并不等同于成本效益。非推理模型还表现出高达3.4倍的极端成本波动，部分异常查询超过36GB/次，达到最优模型平均1.8GB的20倍以上，其主要原因为分区过滤缺失和低效连接操作。我们识别了这些普遍存在的低效模式，并为成本敏感型企业环境提供了降低财务风险的部署指南。