GPT‑5 Models Ranked: Which Variant Excels at SQL Tasks?

Overview

In August 2025 the GPT‑5 family was officially released, and the SCALE platform focused on its SQL capabilities. This special benchmark evaluates the GPT‑5 models on a comprehensive set of SQL‑related tasks.

Core Findings

Flagship model analysis: gpt‑5‑chat shows notable “subject‑specific” weaknesses, while gpt‑5‑mini delivers a more balanced overall performance.

Comprehensive capability assessment: Multi‑dimensional evaluation of SQL Understanding, SQL Optimization, and Dialect Conversion reveals distinct strengths and gaps across model variants.

Data‑driven model selection: Performance differences guide scenario‑based model choice.

Evaluation Criteria

SQL Understanding – ability to parse complex queries and user intent.

SQL Optimization – awareness of query efficiency and performance improvements.

Dialect Conversion – capability to translate syntax between mainstream databases.

Results

gpt‑5‑mini (balanced)

Overall score: leading across three dimensions.

SQL Understanding: 80.8 (Execution Accuracy 87.1, Plan Detection 57.1, Syntax Error Detection 74.3)

Dialect Conversion: 75.6 (Large SQL conversion 54.8, Domestic DB 92.1, Logical Equivalence 74.2, Syntax Error Detection 85.7)

SQL Optimization: 68.4 (Logical Equivalence 63.2, Optimization Depth 64.4, Syntax Error Detection 94.7)

Highlights: high execution accuracy, strong reliability, excellent at complex optimization tasks.

Shortcomings: not top‑ranked in regular optimization, limited handling of large, complex SQL conversions.

gpt‑5‑nano (SQL code generator)

SQL Understanding: 77.1 (Execution Accuracy 85.7, Plan Detection 35.7, Syntax Error Detection 75.7)

Dialect Conversion: 66.4 (Large SQL conversion 19.4, Domestic DB 100, Logical Equivalence 80.6, Syntax Error Detection 69)

SQL Optimization: 68.7 (Logical Equivalence 89.5, Optimization Depth 55.6, Syntax Error Detection 100)

Highlights: extremely high syntax correctness, solid logical conversion.

Shortcomings: shallow understanding of optimization depth, difficulty with complex, lengthy query migrations.

gpt‑5‑chat (theoretical strength, practical gaps)

SQL Understanding: 62.3 (Execution Accuracy 57.1, Plan Detection 60.7, Syntax Error Detection 84.3)

Dialect Conversion: 55.4 (Large SQL conversion 3.2, Domestic DB 86.8, Logical Equivalence 71.0, Syntax Error Detection 66.7)

SQL Optimization: 56.0 (Logical Equivalence 52.6, Optimization Depth 48.9, Syntax Error Detection 94.7)

Highlights: deep theoretical understanding of complex optimization strategies.

Shortcomings: poor execution accuracy, high error rate in generated SQL, unable to handle large‑scale query migrations.

Conclusion

The GPT‑5 release marks not only a numeric upgrade but also a profound shift toward specialized, scenario‑driven AI for SQL. gpt‑5‑mini exemplifies the value of “scenario‑defined” model selection, while gpt‑5‑nano serves as a robust SQL code generator. The divergence among models underscores the emerging coexistence of general‑purpose LLMs and domain‑specific tools such as SQLFlash and the upcoming SQLShift.

Future Outlook

Introduce new players to broaden market coverage.

Focus on specialized tools like SQLShift for deep dialect conversion analysis.