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 – anthropic/claude-sonnet-4 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

Which students joined this year and already finished a lesson?

Expected output

SELECT DISTINCT u.name FROM users u JOIN completions c ON u.user_id = c.user_id WHERE u.role = 'student' AND u.created_at >= DATE_TRUNC('year', CURRENT_DATE);

Model output

SELECT DISTINCT u.user_id, u.name, u.email FROM users u JOIN completions c ON u.user_id = c.user_id WHERE u.role = 'student' AND EXTRACT(YEAR FROM u.created_at) = EXTRACT(YEAR FROM CURRENT_DATE);