Invoice a bakery's wholesale cafes from revised orders, dated price changes, returns and a tiered discount
Exact prompt
# Invoice a bakery's wholesale cafe customers for November Inputs: `orders.csv` (every revision of every order: order_id, rev, cafe, delivery_date, item, qty), `prices.csv` (item prices with the date each takes effect), `returns.csv` (unsold goods sent back against an order), `cafes.json` (each cafe's tier) and `manager-summary.txt`. Every amount is in pounds. The files are the record; where a summary disagrees with them, the files win. Rules: - Only the highest revision of each order_id counts; rev is a whole number, so rev 10 comes after rev 9. A highest revision with qty 0 cancels the order. - Charge each order at the prices.csv row for its item with the latest effective_from on or before the delivery_date. - Credit each return at the unit price its order was charged; returns against cancelled orders get no credit. - Gold cafes get 7.5% off goods minus credits, rounded half-up to the penny; other cafes get no discount. - Amount due = goods - credits - discount. Write `out/invoices.json`: `{"orders": {"<order_id>": <final qty>, ...} (counted orders only, cancelled ones left out), "goods": {"<cafe>": <sum of qty x unit price>, ...}, "credits": {...}, "discount": {...}, "due": {...}, "grand_total": <sum of due>}` with every cafe in `cafes.json` in each cafe 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.
Build monthly invoices for cafes that buy from a bakery. Orders were revised several times (revision numbers run past 9, and a final revision can cancel), prices change mid-month with one rise announced for later, returns are credited at the price charged, and gold cafes get a discount on the net amount. A summary note in the inputs gets the price change and revision handling wrong. 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: latest_revisions prices_in_force return_credits gold_discount_and_totals
Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0054, 20s).
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.ⓘ
| Model | Cost | Time | Step-insⓘ | Autonomy | Outcome | Trustⓘ | Date | Compare |
|---|---|---|---|---|---|---|---|---|
| AClaude Opus 5.5default · thinking on | $0.15 | 22s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-22 | |
| AClaude Haiku 5.5default · thinking on | $0.0054 | 20s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-22 | |
| AClaude Sonnet 5.5default · thinking on | $0.06 | 20s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-22 |
Embed this result
A live badge for this use case. It links back here and updates as new runs are published.
Get new runs and ranking changes weekly
Which models got better or cheaper on real tasks like this one.