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

40s

Input Tokens

958

Output Tokens

257

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 dealership, COUNT(*) as num_volvo_sales FROM sales INNER JOIN vehicles ON sales.vehicle_id = vehicles.vehicle_id WHERE make = 'Volvo' AND sale_date >= '2025-05-01' AND sale_date < '2026-01-01' AND EXTRACT(DOW FROM sale_date) = 1 GROUP BY dealership;