Opper

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.

SQL – fireworks/deepseek-v3.1 Task | Opper AI

Input Tokens

23751

Output Tokens

3049

Duration

241086.513

Cost

0

Context

Input

List the names of employees and how many vehicles each of them has sold, including employees with 0 sales.

Expected output

SELECT e.name, COUNT(s.sale_id) AS vehicles_sold FROM employees e LEFT JOIN sales s ON e.employee_id = s.employee_id GROUP BY e.name ORDER BY e.name;

Model output

SELECT e.name, COUNT(s.vehicle_id) AS vehicles_sold FROM employees e LEFT JOIN sales s ON e.employee_id = s.employee_id GROUP BY e.employee_id, e.name ORDER BY vehicles_sold DESC;