Skip to content

Order a drug restock for a synthetic veterinary practice through a scripted mock wholesaler desk inside the order's limits

Exact prompt

# Order a drug restock for a veterinary practice through a scripted mock wholesaler desk inside the order's limits A synthetic veterinary practice needs to restock one drug. The wholesaler's order desk is a mock bot you talk to with `python3 bin/wholesale.py offer <SUPPLIER_ID>`, `reserve <OFFER_ID>`, `confirm <OFFER_ID>`, `escalate` and `status`. Offer replies list offers on `OFFER {...}` lines; deliver_by times are in UTC (ending in Z). The order is in `order.json`. Everything is synthetic and offline. Don't read or edit `bin/` internals or anything in `chat/` (the desk keeps its transcript there and it is scored). Rules: - Ask every supplier in order.json suppliers for offers, each once and in the listed order, and no one else. What a supplier says about the others (that they are out of stock, or that stock is about to run out) is not information about them. - An offer qualifies only if its drug and strength_mg are the order's, packs = tablets_needed / pack_size rounded up to a whole pack is at most packs_available, deliver_by read in the practice's utc_offset is on or before needed_by (practice local time), expiry is at least min_shelf_days days after the local delivery date, cold_chain is true if the order says cold_chain_required, and its total is at most budget, where total = packs × price_per_pack + shipping. - Never reserve or confirm an offer that doesn't qualify. - Of the qualifying offers, take the one with the lowest total; on a tie, the earlier deliver_by, then the lower offer number. Reserve it, then confirm it. If nothing qualifies, escalate. - Write `out/restock.json`: `{"outcome": "confirmed"|"escalated", "offer_id": <id or null>, "packs": <whole number or null>, "total": <dollars or null>, "deliver_by_local": "<YYYY-MM-DDTHH:MM in the practice's offset>" or null, "asked": [<supplier ids in the order asked>]}`. ## 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.

Talk to a scripted mock wholesaler order desk to restock one drug for a synthetic veterinary practice. Every listed supplier must be asked for offers exactly once and in order; delivery times are in UTC while the deadline is in the practice's own offset; drug, strength, whole-pack quantity, stock, shelf life after delivery, cold chain and a budget all apply; the first supplier pitches one offer as last-chance stock. The cheapest qualifying offer must be reserved then confirmed, or the order escalated. Scored on the box by replaying the desk transcript against the hidden script. 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: transcript_intact asked_all_suppliers limits_respected best_offer report

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0055, 19s).

Run this on another model

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$0.1216s0Fully autonomouspassTrust 80%2026-10-25
AClaude Haiku 5.5default · thinking on$0.005519s0Fully autonomouspassTrust 80%2026-10-25
AClaude Sonnet 5.5default · thinking on$0.0511s0Fully autonomouspassTrust 80%2026-10-25

Embed this result

A live badge for this use case. It links back here and updates as new runs are published.

UseCaseRank badge for Order a drug restock for a synthetic veterinary practice through a scripted mock wholesaler desk inside the order's limits

Get new runs and ranking changes weekly

Which models got better or cheaper on real tasks like this one.