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 – openai/gpt-5-nano 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

WITH emp_sales AS (
  SELECT e.employee_id,
         e.name AS employee_name,
         e.branch_id,
         d.name AS dealership_name,
         COALESCE(SUM(s.sale_price), 0) AS total_sales
  FROM employees e
  JOIN dealerships d ON e.branch_id = d.branch_id
  LEFT JOIN sales s ON s.employee_id = e.employee_id
  GROUP BY e.employee_id, e.name, e.branch_id, d.name
)
SELECT dealership_name,
       employee_name,
       total_sales,
       DENSE_RANK() OVER (PARTITION BY branch_id ORDER BY total_sales DESC) AS sales_rank
FROM emp_sales
ORDER BY dealership_name, sales_rank, employee_name;