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 – mistral/mistral-small-eu Task | Opper AI

Input Tokens

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Output Tokens

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Duration

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Cost

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Context

Input

For each dealership, rank employees by total sales amount in descending order. In the results I want to see dealership_name, employee_name , total_sales and sales_rank

Expected output

SELECT d.name AS dealership_name, e.name AS employee_name, SUM(s.sale_price) AS total_sales, RANK() OVER (PARTITION BY d.branch_id ORDER BY SUM(s.sale_price) DESC) AS sales_rank FROM sales s JOIN employees e ON s.employee_id = e.employee_id JOIN dealerships d ON s.branch_id = d.branch_id GROUP BY d.name, e.name, d.branch_id;

Model output

SELECT  dealerships.name AS dealership_name,  employees.name AS employee_name,  SUM(sales.sale_price) AS total_sales,  RANK() OVER (PARTITION BY dealerships.name ORDER BY SUM(sales.sale_price) DESC) AS sales_rank  FROM  employees  JOIN  sales  ON  employees.employee_id = sales.employee_id  JOIN  dealerships  ON  employees.branch_id = dealerships.branch_id  GROUP BY  dealerships.name, employees.name  ORDER BY  dealerships.name, sales_rank;