Cost Trade-offs of Reasoning and Non-Reasoning Large Language Models in Text-to-SQL
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912950215770112 |
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| author | Deochake, Saurabh Mukhopadhyay, Debajyoti |
| author_facet | Deochake, Saurabh Mukhopadhyay, Debajyoti |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cost Trade-offs of Reasoning and Non-Reasoning Large Language Models in Text-to-SQL Deochake, Saurabh Mukhopadhyay, Debajyoti Databases Artificial Intelligence Distributed, Parallel, and Cluster Computing I.2.7; H.3.3; H.2.3 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. |
| title | Cost Trade-offs of Reasoning and Non-Reasoning Large Language Models in Text-to-SQL |
| topic | Databases Artificial Intelligence Distributed, Parallel, and Cluster Computing I.2.7; H.3.3; H.2.3 |
| url | https://arxiv.org/abs/2512.22364 |