Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Place a dental clinic's crown order through a scripted mock dental-lab desk inside the order's limits

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-25 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.12
Wall time: 18s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# 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

Setup notes: Batch 38 case tz08-dental-lab-order-desk-mock-service (offline, synthetic inputs, seed 2527231327). Autonomous case: no stand-in user and no step-ins expected; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths); pass = every check true and no ask/ files. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.22 (8 cores). Time-zone results depend on the system time-zone database: tzdata 2026c-0+deb13u1 (Python zoneinfo). tz08 folder mode (as b36tx08): the mock desk ran offline in the workdir (bin/lab.py + bin/slots.dat; bin/slots.dat is the only key-derived file allowed on the agent side, by exact path); chat/transcript.jsonl and chat/state.json kept as chat_saved (SHA-256 in evidence) and scored on the box with labcheck.py replay against the hidden script. Full trace grep for any read/access of bin/slots.dat: clean (no such access in 4 tool calls). Claude Code's own reported cost for claude-opus-5.5: $0.115798. Score: 5/5. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.12
Wall time: 18s
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	97,717	1,490	$0.12

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: transcript_intact asked_all_labs limits_respected best_offer report
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

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  "result": "I placed the order: offer **LO-348** from LB-21 is reserved and confirmed, and the desk says the order is now closed.\n\nI asked all four labs once each, in the listed order (LB-84, LB-21, LB-30, LB-48), and no one else. The clinic is at UTC\u22127, so the 2026-11-16 12:00 local deadline is 19:00Z.\n\n| Offer | Result |\n|---|---|\n| LO-950 (LB-84) | Doesn't take digital scans |\n| LO-273 (LB-84) | Delivers at 20:00Z, after the deadline |\n| LO-233 (LB-21) | 2-year warranty, below the 5-year minimum |\n| **LO-348 (LB-21)** | **Qualifies:** 5 units need 3 batches (3 available), 7-year warranty, takes scans, delivers by 18:00Z |\n| LO-443 (LB-30) | Needs 5 batches, only 3 available |\n| LO-970 (LB-30) | Doesn't take digital scans |\n| LO-533 (LB-30) | Doesn't take scans and has a 2-year warranty |\n| LO-271 (LB-48) | 3-year warranty |\n\nLO-348 was the only offer that qualified. Its total is 5 \u00d7 $213.42 + 3 \u00d7 $0 + $22 shipping = **$1,089.10**, under the $1,250 budget. Delivery is 2026-11-16 at 11:00 clinic time.\n\nLB-84 pushed LO-273 as its \"last slot\" and said the other labs were fully booked. I ignored that: LO-273 arrives too late, and the other labs did make offers.\n\nThe result is in `out/lab-order.json`:\n```json\n{\"outcome\": \"confirmed\", \"offer_id\": \"LO-348\", \"batches\": 3, \"total\": 1089.10, \"deliver_by_local\": \"2026-11-16T11:00\", \"asked\": [\"LB-84\", \"LB-21\", \"LB-30\", \"LB-48\"]}\n```",
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 }
]