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 – cerebras/qwen-3-235b-a22b-instruct-2507 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

Create a new table where I can store how much we pay instructors each month.

Expected output

CREATE TABLE instructor_payouts ( instructor_id INT REFERENCES users(user_id), payout_month DATE, amount DECIMAL(10, 2), PRIMARY KEY (instructor_id, payout_month) );

Model output

CREATE TABLE instructor_payments (
    payment_id SERIAL PRIMARY KEY,
    instructor_id INT REFERENCES users(user_id) ON DELETE CASCADE,
    payment_month DATE NOT NULL,
    amount DECIMAL(10, 2) NOT NULL,
    created_at TIMESTAMP DEFAULT NOW(),
    UNIQUE (instructor_id, payment_month)
);