Gemini 3 Pro Leads SQL Benchmarks with Deep Understanding, High‑Quality Optimization, and Balanced Dialect Conversion

Abstract and Key Highlights

Gemini 3 Pro was added to the SCALE benchmark for the first time and immediately secured the top position in SQL understanding, ranking No.1 with a total score of 86.0. It also placed No.2 in SQL optimization and No.6 in dialect conversion, demonstrating a well‑balanced performance across all three core dimensions.

Evaluation Purpose and Methodology

The SCALE framework evaluates three core dimensions using a unified dataset to ensure fairness and reproducibility:

SQL Optimization : measures the model’s ability to improve query efficiency and performance.

Dialect Conversion : assesses accuracy when translating SQL between mainstream databases.

SQL Understanding : tests precise parsing of complex query logic and user intent.

All new models released during the month are evaluated under this standardized protocol.

Results and In‑Depth Analysis

SQL Understanding

Total Score: 86.0 (No.1)

Execution Accuracy: 90 (tied No.1) – the model predicts complex query results with high precision.

Execution‑Plan Detection: 64.3 (tied No.1) – solid but leaves room for deeper plan analysis.

Syntax Error Detection: 87.1 (tied No.1) – excellent at spotting SQL syntax violations.

These strengths make Gemini 3 Pro reliable for data analysis, reporting, and production‑level fault diagnosis.

Observed Issues:

Inconsistent handling of structured output: the model outputs null as the string "NULL", confusing SQL NULL semantics with JSON.

Insufficient understanding of write‑operation execution plans: it misclassifies UPDATE statements as full‑table scans ( type: "ALL") instead of recognizing index‑based access.

SQL Optimization

Total Score: 72.7 (No.2)

Logical Equivalence: 73.7 (No.11) – maintains query logic after transformation.

Optimization Depth: 66.7 (tied No.2) – applies common rules such as subquery flattening and join reordering, though deeper semantic optimizations are lacking.

Syntax Error Detection: 100 (tied No.1) – guarantees that generated optimized SQL is syntactically correct.

Limitations include failure to rewrite LIKE‑prefix queries into range scans, inability to eliminate redundant LIMIT subqueries, and missing detection of implicit type‑conversion issues.

Dialect Conversion

Total Score: 77.1 (No.6)

Domestic database conversion (e.g., Oracle → OceanBase): 89.5 (tied No.16)

Logical equivalence: 80.6 (tied No.2)

Large‑SQL conversion: 61.3 (tied No.2)

Syntax error detection: 78.6 (tied No.8)

The model excels at preserving business logic across dialects but struggles with complex, long‑script transformations and occasionally misinterprets proprietary syntax such as CAST(... AS type_name) versus MULTISET in OceanBase.

Overall Summary and Outlook

Gemini 3 Pro demonstrates a distinctive profile of “deep understanding, high‑quality optimization, and balanced conversion.” Its execution accuracy makes it a trusted assistant for SQL logic verification, while its robust syntax‑error detection ensures production‑grade optimized code. Future iterations are expected to improve execution‑plan detection and large‑SQL handling, especially for enterprise migration projects.

Application Recommendations

Data Analysis & Engineering : use Gemini 3 Pro for complex query validation and result prediction.

Database Management & Development : rely on its optimization capabilities for code refactoring and performance tuning.

Enterprise Technical Decision‑Making : adopt the model as an auxiliary tool for database migration and domestic‑database replacement projects.

Future Directions

The SCALE framework will continue tracking Gemini 3 Pro and other frontier models, aiming for breakthroughs in execution‑plan detection and large‑SQL conversion to better serve real‑world production environments.