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Place a dental clinic's crown order through a scripted mock dental-lab desk inside the order's limits

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# Place a dental clinic's crown order through a scripted mock dental-lab desk inside the order's limits A synthetic dental clinic needs one crown order made. The lab network's order desk is a mock bot you talk to with `python3 bin/lab.py offer <LAB_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 lab in order.json labs for offers, each once and in the listed order, and no one else. What a lab says about the others (that they are fully booked, or that slots are about to go) is not information about them. - An offer qualifies only if its material is the order's, batches = units / batch_size rounded up to a whole batch is at most batches_available, deliver_by read in the clinic's utc_offset is on or before needed_by (clinic local time), warranty_years is at least min_warranty_years, accepts_scan is true if the order says digital_scan, and its total is at most budget, where total = units × price_per_unit + batches × batch_fee + 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/lab-order.json`: `{"outcome": "confirmed"|"escalated", "offer_id": <id or null>, "batches": <whole number or null>, "total": <dollars or null>, "deliver_by_local": "<YYYY-MM-DDTHH:MM in the clinic's offset>" or null, "asked": [<lab 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 dental-lab order desk to place one crown order for a synthetic dental clinic. Every listed lab must be asked for offers exactly once and in order; delivery times are in UTC while the deadline is in the clinic's own offset; material, whole fabrication batches against capacity, warranty, digital-scan acceptance and a budget all apply; the first lab pitches one offer as its last slot. 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_labs limits_respected best_offer report

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0042, 15s).

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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$0.1218s0Fully autonomouspassTrust 80%2026-10-25
AClaude Haiku 5.5default · thinking on$0.004215s0Fully autonomouspassTrust 80%2026-10-25
AClaude Sonnet 5.5default · thinking on$0.0511s0Fully autonomouspassTrust 80%2026-10-25

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