Pay a quarter's sales commission with marginal tiers, split deals and in-quarter refunds
Exact prompt
# Pay Q3 2026 sales commission Inputs: `deals.csv` (CRM export), `splits.csv` (deals shared between reps), `refunds.csv` and `comp-plan.json` (tiers). Work out commission for every rep for Q3 2026 (2026-07-01 to 2026-09-30). - Only deals with stage `closed_won` and a `close_date` inside the quarter count. - A deal listed in `splits.csv` credits each rep `share_pct` percent of its amount; any other deal credits its owner in full. - A refund dated inside the quarter reduces the refunded deal's amount before splitting; refunds dated after the quarter are left for next quarter. - Tiers are marginal: each tier's rate applies only to the part of the rep's quarterly credited bookings that falls inside that tier. - Work exactly; round only the reported figures, to 2 decimals half-up. Write `out/commission.json`: `{"bookings": {"<rep>": <credited bookings>, ...}, "commission": {"<rep>": <commission>, ...}, "total_commission": <sum of the rounded commission figures>}`. List every rep who has credited bookings. ## 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.
Work out each sales rep's commission for a quarter from a CRM export of won and lost deals, split credits and refunds. Tiers are marginal on each rep's quarterly credited bookings, split deals credit each rep only their share, lost or out-of-quarter deals don't count, and only refunds issued inside the quarter reduce bookings. Applying the top tier to everything, crediting splits in full or counting every refund each changes someone's pay. 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: bookings_credited marginal_tiers refund_timing deal_filter
Best model for this task so far: Claude Sonnet 5.5 (effort default (no --effort flag) · thinking off) (success, $0.06, 11s).
All runs (3)
67% pass rate across 3 eligible runs: 2 pass, 0 partial, 1 fail. Partial and failed runs are listed below with everything else.ⓘ
| Model | Cost | Time | Step-insⓘ | Autonomy | Outcome | Trustⓘ | Date | Compare |
|---|---|---|---|---|---|---|---|---|
| AClaude Opus 5.5default · thinking on | $0.13 | 16s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-21 | |
| AClaude Haiku 5.5default · thinking on | $0.0042 | 14s | 0 | Fully autonomous | fail | Trust 80% | 2026-10-21 | |
| AClaude Sonnet 5.5default · thinking off | $0.06 | 11s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-21 |
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