GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking off) on Bill a hotel's group bookings across a mid-month rate rise, weekend nights, cancellations with time-zone offsets and a city-tax cap

OGPT-6.1 Soldefault · thinking off · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-23 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.04
Wall time: 31s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Bill a hotel's November group bookings Inputs: `bookings.csv` (booking_id, guest, company, room_type, arrive, depart, guests, status, cancelled_at, checkout_time), `rates.csv` (room_type, from_date, weekday, weekend), `hotel.json` and `front-desk-note.txt`. Amounts are in pounds. `hotel.json`, `rates.csv` and these rules are the record; where the front-desk note disagrees, the rules win. Rules: - Each night of a stay (arrive up to the night before depart) is charged at the rate in force on that night's date (the row with the latest from_date on or before it); nights falling on a weekday listed in weekend_nights use the weekend rate. - A booking whose company is in corporates (ignoring letter case) gets corporate_discount_pct off its room charge or cancellation charge only, the discount rounded half-up to 0.01 per booking. - A stayed booking whose checkout_time is later than late_checkout_after pays late_fee; checking out exactly at late_checkout_after is free. - A cancelled booking is free if cancelled_at (which carries its own UTC offset) is at least free_cancel_hours before checkin_time on the arrival date in the hotel's timezone; otherwise, and for every no_show, the first night's room rate is charged. - City tax for a stayed booking = guests x nights x city_tax_per_person_night, counting at most city_tax_max_nights nights. - Write `out/billing.json`: `{"room": {"<booking_id>": <room charge after discount>, ...}, "late_fees": {...}, "cancellation_charges": {...}, "city_tax": {...}, "total": <sum of everything>}`. `room` and `city_tax` list every stayed booking; `late_fees` and `cancellation_charges` list only bookings charged one. ## 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.

Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).

Success criteria: nightly_rates corporate_discount cancellations late_checkout city_tax_and_total

Setup notes: batch30 case o01-hotel-group-billing (offline fixture). Prepared with batch30/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python3 score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Autonomous case (seed 28261577): no stand-in user; any ask/ file counts as an intervention and blocks a pass. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 5/5. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 3 (0 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.04
Wall time: 31s
Human step-ins: 0
Turns: 1

Model usage
Model	Input tok	Output tok	Cost
OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking off	67,268	907	$0.04

Reproduce this run

Use the use case prompt on the use case page.
Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking off) via Codex CLI 0.163.0 (codex exec --json), cloud.
It counts as a success only if it meets every success criterion: nightly_rates corporate_discount cancellations late_checkout city_tax_and_total
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

{"type": "thread.started", "thread_id": "01a16834-6d19-7f11-b941-6b41abcd6461"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll read the inputs, calculate each booking\u2019s charges using the stated rules, and write `out/billing.json`.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'bookings.csv' -g 'rates.csv' -g 'hotel.json' -g 'front-desk-note.txt' -g 'AGENTS.md'\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'bookings.csv' -g 'rates.csv' -g 'hotel.json' -g 'front-desk-note.txt' -g 'AGENTS.md'\"", "aggregated_output": "/home/bob/usecase-runs/b2930/out/codex/gpt-6.1-sol/b30o01/work\nfront-desk-note.txt\nhotel.json\nrates.csv\nbookings.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat hotel.json rates.csv bookings.csv front-desk-note.txt'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat hotel.json rates.csv bookings.csv front-desk-note.txt'", "aggregated_output": "{\n \"timezone\": \"Europe/London\",\n \"weekend_nights\": [\n  \"Fri\",\n  \"Sat\"\n ],\n \"checkin_time\": \"15:00\",\n \"free_cancel_hours\": 48,\n \"late_checkout_after\": \"11:00\",\n \"late_fee\": \"25.00\",\n \"city_tax_per_person_night\": \"2.50\",\n \"city_tax_max_nights\": 5,\n \"corporate_discount_pct\": 10,\n \"corporates\": [\n  \"Tallis Print\",\n  \"Moorgate Health\",\n  \"Osprey Events\"\n ]\n}room_type,from_date,weekday,weekend\r\nstandard,2026-10-01,89.00,119.00\r\nstandard,2026-11-17,97.00,132.00\r\ndouble,2026-10-01,109.00,139.00\r\ndouble,2026-11-17,117.00,152.00\r\nsuite,2026-10-01,189.00,229.00\r\nsuite,2026-11-17,199.00,244.00\r\nbooking_id,guest,company,room_type,arrive,depart,guests,status,cancelled_at,checkout_time\r\nHB-127,Guest 15,,double,2026-11-08,2026-11-15,1,cancelled,2026-11-06T17:00+02:00,\r\nHB-102,Guest 3,HARBOUR LOGISTICS,standard,2026-11-19,2026-11-22,1,stayed,,12:30\r\nHB-129,Guest 16,TALLIS PRINT,suite,2026-11-14,2026-11-16,1,stayed,,10:15\r\nHB-134,Guest 18,,standard,2026-11-06,2026-11-13,2,stayed,,11:20\r\nHB-106,Guest 5,Moorgate Health,standard,2026-11-17,2026-11-21,3,stayed,,11:00\r\nHB-110,Guest 7,,standard,2026-11-06,2026-11-10,3,stayed,,11:00\r\nHB-115,Guest 9,Tallis Print,standard,2026-11-11,2026-11-12,1,no_show,,\r\nHB-107,Guest 6,Ferncliff Legal,double,2026-11-13,2026-11-16,1,cancelled,2026-11-11T17:00+02:00,\r\nHB-98,Guest 1,ferncliff legal,double,2026-11-18,2026-11-20,2,stayed,,09:40\r\nHB-122,Guest 12,Ferncliff Legal,suite,2026-11-21,2026-11-24,1,no_show,,\r\nHB-123,Guest 13,Ferncliff Legal,double,2026-11-06,2026-11-12,1,stayed,,10:15\r\nHB-101,Guest 2,,standard,2026-11-09,2026-11-12,1,cancelled,2026-11-08T14:00-05:00,\r\nHB-103,Guest 4,HARBOUR LOGISTICS,standard,2026-11-21,2026-11-22,1,cancelled,2026-11-19T16:00+02:00,\r\nHB-132,Guest 17,moorgate health,suite,2026-11-10,2026-11-17,1,cancelled,2026-11-08T14:00+00:00,\r\nHB-137,Guest 19,,double,2026-11-11,2026-11-13,2,stayed,,11:20\r\nHB-117,Guest 10,,suite,2026-11-16,2026-11-22,1,stayed,,12:30\r\nHB-112,Guest 8,ferncliff legal,suite,2026-11-08,2026-11-14,1,stayed,,09:40\r\nHB-124,Guest 14,harbour logistics,double,2026-11-18,2026-11-19,1,stayed,,11:00\r\nHB-138,Guest 20,osprey events,standard,2026-11-04,2026-11-05,1,stayed,,09:40\r\nHB-119,Guest 11,HARBOUR LOGISTICS,double,2026-11-19,2026-11-26,2,cancelled,2026-11-17T17:00+01:00,\r\nFront desk note: price the whole stay at the rate for the night they arrived, it's simpler.\nCancellation times are all UK time. Checkout at 11:00 on the dot counts as late.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\npython - <<'PY'\nimport csv,json\nfrom datetime import datetime,date,time,timedelta,timezone\nfrom decimal import Decimal,ROUND_HALF_UP\nfrom zoneinfo import ZoneInfo\nfrom pathlib import Path\nh=json.load(open('hotel.json'))\nrates=list(csv.DictReader(open('rates.csv')))\nbookings=list(csv.DictReader(open('bookings.csv')))\nD=Decimal\npenny=D('.01')\ncorporates={c.casefold() for c in h['corporates']}\ndef rate(b,day):\n    r=max((r for r in rates if r['room_type']==b['room_type'] and date.fromisoformat(r['from_date'])<=day),key=lambda r:r['from_date'])\n    return D(r['weekend'] if day.strftime('%a') in h['weekend_nights'] else r['weekday'])\ndef discounted(b,charge):\n    discount=(charge*D(str(h['corporate_discount_pct']))/100).quantize(penny,rounding=ROUND_HALF_UP) if b['company'].casefold() in corporates else D(0)\n    return charge-discount\nout={k:{} for k in ['room','late_fees','cancellation_charges','city_tax']}\nfor b in bookings:\n    bid=b['booking_id']; arrival=date.fromisoformat(b['arrive']); departure=date.fromisoformat(b['depart'])\n    nights=(departure-arrival).days\n    if b['status']=='stayed':\n        out['room'][bid]=discounted(b,sum((rate(b,arrival+timedelta(days=i)) for i in range(nights)),D(0)))\n        out['city_tax'][bid]=D(b['guests'])*min(nights,h['city_tax_max_nights'])*D(h['city_tax_per_person_night'])\n        if time.fromisoformat(b['checkout_time'])>time.fromisoformat(h['late_checkout_after']):\n            out['late_fees'][bid]=D(h['late_fee'])\n    elif b['status'] in ('cancelled','no_show'):\n        charge=b['status']=='no_show'\n        if not charge:\n            checkin=datetime.combine(arrival,time.fromisoformat(h['checkin_time']),ZoneInfo(h['timezone']))\n            cancelled=datetime.fromisoformat(b['cancelled_at'])\n            charge=checkin.astimezone(timezone.utc)-cancelled.astimezone(timezone.utc)<timedelta(hours=h['free_cancel_hours'])\n        if charge:\n            out['cancellation_charges'][bid]=discounted(b,rate(b,arrival))\n    else:\n        raise ValueError(b['status'])\nout['total']=sum((sum(v.values(),D(0)) for v in out.values()),D(0))\nserial={k:({bid:float(v) for bid,v in values.items()} if isinstance(values,dict) else float(values)) for k,values in out.items()}\nPath('out/billing.json').write_text(json.dumps(serial,indent=2)+'\\\\n')\nassert len(out['room'])==len(out['city_tax'])==sum(b['status']=='stayed' for b in bookings)\nprint(json.dumps(serial,indent=2))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\npython - <<'PY'\nimport csv,json\nfrom datetime import datetime,date,time,timedelta,timezone\nfrom decimal import Decimal,ROUND_HALF_UP\nfrom zoneinfo import ZoneInfo\nfrom pathlib import Path\nh=json.load(open('hotel.json'))\nrates=list(csv.DictReader(open('rates.csv')))\nbookings=list(csv.DictReader(open('bookings.csv')))\nD=Decimal\npenny=D('.01')\ncorporates={c.casefold() for c in h['corporates']}\ndef rate(b,day):\n    r=max((r for r in rates if r['room_type']==b['room_type'] and date.fromisoformat(r['from_date'])<=day),key=lambda r:r['from_date'])\n    return D(r['weekend'] if day.strftime('%a') in h['weekend_nights'] else r['weekday'])\ndef discounted(b,charge):\n    discount=(charge*D(str(h['corporate_discount_pct']))/100).quantize(penny,rounding=ROUND_HALF_UP) if b['company'].casefold() in corporates else D(0)\n    return charge-discount\nout={k:{} for k in ['room','late_fees','cancellation_charges','city_tax']}\nfor b in bookings:\n    bid=b['booking_id']; arrival=date.fromisoformat(b['arrive']); departure=date.fromisoformat(b['depart'])\n    nights=(departure-arrival).days\n    if b['status']=='stayed':\n        out['room'][bid]=discounted(b,sum((rate(b,arrival+timedelta(days=i)) for i in range(nights)),D(0)))\n        out['city_tax'][bid]=D(b['guests'])*min(nights,h['city_tax_max_nights'])*D(h['city_tax_per_person_night'])\n        if time.fromisoformat(b['checkout_time'])>time.fromisoformat(h['late_checkout_after']):\n            out['late_fees'][bid]=D(h['late_fee'])\n    elif b['status'] in ('cancelled','no_show'):\n        charge=b['status']=='no_show'\n        if not charge:\n            checkin=datetime.combine(arrival,time.fromisoformat(h['checkin_time']),ZoneInfo(h['timezone']))\n            cancelled=datetime.fromisoformat(b['cancelled_at'])\n            charge=checkin.astimezone(timezone.utc)-cancelled.astimezone(timezone.utc)<timedelta(hours=h['free_cancel_hours'])\n        if charge:\n            out['cancellation_charges'][bid]=discounted(b,rate(b,arrival))\n    else:\n        raise ValueError(b['status'])\nout['total']=sum((sum(v.values(),D(0)) for v in out.values()),D(0))\nserial={k:({bid:float(v) for bid,v in values.items()} if isinstance(values,dict) else float(values)) for k,values in out.items()}\nPath('out/billing.json').write_text(json.dumps(serial,indent=2)+'\\\\n')\nassert len(out['room'])==len(out['city_tax'])==sum(b['status']=='stayed' for b in bookings)\nprint(json.dumps(serial,indent=2))\nPY\"", "aggregated_output": "{\n  \"room\": {\n    \"HB-102\": 361.0,\n    \"HB-129\": 376.2,\n    \"HB-134\": 683.0,\n    \"HB-106\": 380.7,\n    \"HB-110\": 416.0,\n    \"HB-98\": 234.0,\n    \"HB-123\": 714.0,\n    \"HB-137\": 218.0,\n    \"HB-117\": 1274.0,\n    \"HB-112\": 1174.0,\n    \"HB-124\": 117.0,\n    \"HB-138\": 80.1\n  },\n  \"late_fees\": {\n    \"HB-102\": 25.0,\n    \"HB-134\": 25.0,\n    \"HB-137\": 25.0,\n    \"HB-117\": 25.0\n  },\n  \"cancellation_charges\": {\n    \"HB-115\": 80.1,\n    \"HB-122\": 244.0,\n    \"HB-101\": 89.0,\n    \"HB-119\": 117.0\n  },\n  \"city_tax\": {\n    \"HB-102\": 7.5,\n    \"HB-129\": 5.0,\n    \"HB-134\": 25.0,\n    \"HB-106\": 30.0,\n    \"HB-110\": 30.0,\n    \"HB-98\": 10.0,\n    \"HB-123\": 12.5,\n    \"HB-137\": 10.0,\n    \"HB-117\": 12.5,\n    \"HB-112\": 12.5,\n    \"HB-124\": 2.5,\n    \"HB-138\": 2.5\n  },\n  \"total\": 6818.1\n}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "agent_message", "text": "Created [out/billing.json](out/billing.json) using the stated rules.\n\nTotal: **\u00a36,818.10**."}}
{"type": "turn.completed", "usage": {"input_tokens": 67268, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 907, "reasoning_output_tokens": 0}}