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Bill a driving instructor's pupils for a month with block-booking discounts, late-cancellation charges and test fees

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# Bill a driving instructor's pupils for November Inputs: `lessons.csv` (each lesson: pupil, date, minutes, status), `pupils.json` (each pupil's `block_booked` flag) and `prices.json` (the hourly rate and the test fee). Every amount is in pounds. Billing rules, per lesson: - Lesson charge = hourly rate x minutes / 60 for each taken lesson (status taken), less 10% for pupils marked block_booked 'yes' in pupils.json. - A late cancellation (status late_cancel) is charged at 50% of the full lesson charge, with no block discount; an early cancellation (status early_cancel) is free. - A test (status test) is charged the flat test_fee, with no discount. - Round each lesson's charge to the cent half-up before adding anything up. Write `out/bills.json`: `{"lessons": {"<pupil>": <sum of taken-lesson charges>, ...}, "late_cancel": {...}, "tests": {...}, "total": {...}, "grand_total": <sum of the totals>}` with every pupil in `pupils.json` in each map (0 where nothing applies). ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.

Bill a month of driving lessons from a lesson log. Block-booked pupils get 10% off taken lessons, late cancellations cost half the full lesson price with no discount, early cancellations are free and tests cost a flat fee. Discounting late cancellations, charging early cancellations or discounting every pupil each change the bills. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: block_discount late_cancellations tests_and_totals

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0035, 11s).

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All runs (3)

100% pass rate across 3 eligible runs: 3 pass, 0 partial, 0 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
AClaude Opus 5.5default · thinking on$0.1017s0Fully autonomouspassTrust 80%2026-10-22
AClaude Haiku 5.5default · thinking on$0.003511s0Fully autonomouspassTrust 80%2026-10-22
AClaude Sonnet 5.5default · thinking on$0.0516s0Fully autonomouspassTrust 80%2026-10-22

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