SQL

Natural language to SQL query generation evaluates text-to-query fidelity and schema reasoning. This task is particularly relevant for analytics chat assistants and simplified database interfaces where users need to query data using natural language. Models must understand both the intent behind the question and the structure of the underlying database schema.

Duration

Input Tokens

999

Output Tokens

1269

Cost

$0.00

Context

Input

Between May and September 2025, how many Volvo vehicles were sold during the first week of each month, grouped by dealership?

Expected output

WITH filtered_sales AS ( SELECT s.*, v.make, d.name AS dealership_name FROM sales s JOIN vehicles v ON s.vehicle_id = v.vehicle_id JOIN dealerships d ON s.branch_id = d.branch_id WHERE v.make = 'Volvo' AND s.sale_date >= DATE '2025-05-01' AND s.sale_date < DATE '2025-10-01' AND EXTRACT(DAY FROM s.sale_date) <= 7 ) SELECT dealership_name, DATE_TRUNC('month', sale_date) AS month, COUNT(*) AS volvo_sales FROM filtered_sales GROUP BY dealership_name, DATE_TRUNC('month', sale_date) ORDER BY month, dealership_name;

Model output

SELECT T3.name, COUNT(T1.sale_id) FROM sales AS T1 INNER JOIN vehicles AS T2 ON T1.vehicle_id = T2.vehicle_id INNER JOIN dealerships AS T3 ON T1.branch_id = T3.branch_id WHERE T2.make = 'Volvo' AND T1.sale_date BETWEEN '2025-05-01' AND '2025-09-30' AND EXTRACT(DAY FROM T1.sale_date) <= 7 GROUP BY T3.name