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/glm-4.5-air 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

Show the names of customers who paid more than the average sale price for the vehicles they bought.

Expected output

SELECT c.name FROM customers c JOIN sales s ON c.customer_id = s.customer_id WHERE s.sale_price > (SELECT AVG(s2.sale_price) FROM sales s2 WHERE s2.vehicle_id = s.vehicle_id);

Model output

WITH vehicle_avg_prices AS (SELECT vehicle_id, AVG(sale_price) AS avg_price FROM sales GROUP BY vehicle_id) SELECT DISTINCT c.name FROM customers c JOIN sales s ON c.customer_id = s.customer_id JOIN vehicle_avg_prices v ON s.vehicle_id = v.vehicle_id WHERE s.sale_price > v.avg_price ORDER BY c.name;