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GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking off) on Check and pay staff mileage claims across the April tax-year change

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 try
Estimated cost
$0.03
Wall time
25s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Check and pay staff mileage claims across the tax-year change Inputs: `claims.csv` (claim_id, employee_id, trip_date, from, to, miles, passengers, submitted_on), `employees.json`, `rates.json` and `finance-note.txt`. Amounts are in pounds. `rates.json` and these rules are the record; where the finance note disagrees, the rules win. Rules: - A claim submitted more than late_after_days days after its trip_date is rejected as late; it is listed only in rejected_late, even if it is also a duplicate. - Two remaining claims are duplicates if they have the same employee_id, trip_date, from and to (from and to compared ignoring letter case and extra spaces); only the one submitted first is paid (on a tie, the lower claim number), and the others are duplicates. - Business miles are paid at rate_first per mile until the employee's miles in that tax year reach threshold_miles, then at rate_after; a tax year runs from 6 April to 5 April. Count paid claims in trip_date order (ties by claim number), starting from miles_before_2025_26 for the 2025-26 tax year and from 0 for 2026-27; a claim that crosses the threshold is split. - Each passenger adds passenger_per_mile for every mile of the claim; passenger miles don't count toward the threshold. - Write `out/mileage.json`: `{"paid": {"<claim_id>": <pay>, ...}, "rejected_late": [<claim_ids>], "duplicates": [<claim_ids>], "employee_totals": {"<employee_id>": <sum of pay>, ...}}`. `paid` has every paid claim; lists are sorted by claim number; `employee_totals` lists employees with at least one paid claim. ## 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: late duplicates pay totals

Setup notes: batch29 case h02-mileage-expense-claims (offline fixture). Prepared with batch29/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 1774917927): 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: 4/4. 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 2 (0 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.03
Wall time
25s
Human step-ins
0
Turns
1
Model usage
ModelInput tokOutput tokCost
OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking off49,686771$0.03

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. 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.
  3. It counts as a success only if it meets every success criterion: late duplicates pay totals
  4. 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

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Evidence log
{"type": "thread.started", "thread_id": "01a16830-6dd2-7bd3-9099-cd48c59c1ce7"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll read the inputs, apply the lateness, duplicate and tax-year rules, then write and verify `out/mileage.json`."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; cat claims.csv employees.json rates.json finance-note.txt'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; cat claims.csv employees.json rates.json finance-note.txt'", "aggregated_output": "total 36\ndrwxr-xr-x 8 bob bob 4096 Oct 23 05:56 .\ndrwxrwxr-x 3 bob bob 4096 Oct 23 05:56 ..\ndr-xr-xr-x 2 bob bob   40 Oct 23 05:56 .agents\ndr-xr-xr-x 2 bob bob   40 Oct 23 05:56 .aws\ndr-xr-xr-x 2 bob bob   40 Oct 23 05:56 .codex\ndr-xr-xr-x 2 bob bob   40 Oct 23 05:56 .git\n-rw-r--r-- 1 bob bob 1984 Oct 23 05:55 TASK.md\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 ask\n-rw-r--r-- 1 bob bob 1780 Oct 23 05:55 claims.csv\n-rw-r--r-- 1 bob bob  276 Oct 23 05:55 employees.json\n-rw-r--r-- 1 bob bob  202 Oct 23 05:55 finance-note.txt\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 out\n-rw-r--r-- 1 bob bob  130 Oct 23 05:55 rates.json\nclaim_id,employee_id,trip_date,from,to,miles,passengers,submitted_on\r\nMC-103,EM-36,2026-04-07,Leeds Office,Harrogate Clinic,146,1,2026-04-09\r\nMC-113,EM-41,2026-03-28,York Depot,Hull Warehouse,139,2,2026-06-06\r\nMC-97,EM-32,2026-04-02,Wakefield Site,York Depot,47,2,2026-06-01\r\nMC-118,EM-41,2026-04-11,Bradford Store,Wakefield Site,166,2,2026-05-01\r\nMC-101,EM-32,2026-04-20,Harrogate Clinic,Wakefield Site,105,0,2026-04-29\r\nMC-112,EM-36,2026-03-24,Bradford Store,Leeds Office,315,0,2026-05-23\r\nMC-131,EM-36,2026-04-05,Harrogate Clinic,Hull Warehouse,100,0,2026-04-08\r\nMC-100,EM-32,2026-04-05,Harrogate Clinic,Leeds Office,148,2,2026-04-10\r\nMC-105,EM-36,2026-04-07,Harrogate Clinic,York Depot,143,2,2026-06-06\r\nMC-121,EM-42,2026-04-04,Bradford Store,York Depot,148,2,2026-04-24\r\nMC-132,EM-32,2026-04-06,Bradford Store,Leeds Office,198,0,2026-04-09\r\nMC-114,EM-41,2026-03-26,Wakefield Site,York Depot,171,1,2026-05-25\r\nMC-120,EM-42,2026-04-22,Bradford Store,Wakefield Site,169,2,2026-04-27\r\nMC-94,EM-32,2026-04-01,Bradford Store,Hull Warehouse,195,0,2026-05-29\r\nMC-117,EM-41,2026-03-23,Bradford Store,Leeds Office,135,0,2026-04-01\r\nMC-110,EM-36,2026-04-10,Harrogate Clinic,Bradford Store,154,0,2026-06-07\r\nMC-96,EM-32,2026-04-06,Harrogate Clinic,Bradford Store,284,1,2026-05-09\r\nMC-107,EM-36,2026-04-04,Hull Warehouse,Harrogate Clinic,108,0,2026-06-01\r\nMC-128,EM-42,2026-03-30,Wakefield Site,Hull Warehouse,96,2,2026-06-08\r\nMC-106,EM-36,2026-04-03,Wakefield Site,Harrogate Clinic,239,2,2026-04-08\r\nMC-135,EM-32,2026-04-05,harrogate clinic,Leeds Office,148,2,2026-04-11\r\nMC-127,EM-42,2026-04-08,Harrogate Clinic,Hull Warehouse,92,0,2026-04-10\r\nMC-124,EM-42,2026-03-31,Leeds Office,Hull Warehouse,42,2,2026-05-28\r\nMC-134,EM-32,2026-04-01,Bradford  Store,Hull Warehouse,195,0,2026-05-29\r\n{\n \"EM-32\": {\n  \"name\": \"B. Ruiz\",\n  \"miles_before_2025_26\": 9658\n },\n \"EM-36\": {\n  \"name\": \"C. Okoro\",\n  \"miles_before_2025_26\": 4362\n },\n \"EM-41\": {\n  \"name\": \"A. Hale\",\n  \"miles_before_2025_26\": 9733\n },\n \"EM-42\": {\n  \"name\": \"G. Byrne\",\n  \"miles_before_2025_26\": 9653\n }\n}{\n \"rate_first\": \"0.45\",\n \"rate_after\": \"0.25\",\n \"passenger_per_mile\": \"0.05\",\n \"threshold_miles\": 10000,\n \"late_after_days\": 60\n}Finance note: the 10,000-mile allowance runs per calendar year, so nothing resets in April.\nLate claims are anything over two months old; resubmitted trips are fine to pay twice if the wording differs.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv,json,re\nfrom datetime import date\nfrom decimal import Decimal\nfrom pathlib import Path\nrows=list(csv.DictReader(open('claims.csv')))\nemployees=json.load(open('employees.json'))\nrates=json.load(open('rates.json'))\nnum=lambda r:int(re.search(r'\\\\d+\"'$'\"',r['claim_id']).group())\nlate=[]; dup=[]; accepted=[]; seen=set()\nfor r in sorted(rows,key=lambda r:(r['submitted_on'],num(r))):\n    if (date.fromisoformat(r['submitted_on'])-date.fromisoformat(r['trip_date'])).days>rates['late_after_days']:\n        late.append(r);continue\n    key=(r['employee_id'],r['trip_date'],*(' '.join(r[k].casefold().split()) for k in ('from','to')))\n    if key in seen:dup.append(r);continue\n    seen.add(key);accepted.append(r)\nused={};paid={};totals={}\nfor r in sorted(accepted,key=lambda r:(r['trip_date'],num(r))):\n    d=date.fromisoformat(r['trip_date']);year=d.year if (d.month,d.day)>=(4,6) else d.year-1\n    emp=r['employee_id'];key=(emp,year)\n    before=used.get(key,employees[emp]['miles_before_2025_26'] if year==2025 else 0)\n    miles=int(r['miles']); first=min(miles,max(0,rates['threshold_miles']-before))\n    pay=first*Decimal(rates['rate_first'])+(miles-first)*Decimal(rates['rate_after'])+miles*int(r['passengers'])*Decimal(rates['passenger_per_mile'])\n    used[key]=before+miles;paid[r['claim_id']]=pay;totals[emp]=totals.get(emp,Decimal(0))+pay\nresult={'paid':{k:float(v) for k,v in sorted(paid.items(),key=lambda kv:int(kv[0].split('-')[-1]))},'rejected_late':[r['claim_id'] for r in sorted(late,key=num)],'duplicates':[r['claim_id'] for r in sorted(dup,key=num)],'employee_totals':{k:float(v) for k,v in sorted(totals.items())}}\nPath('out').mkdir(exist_ok=True)\nPath('out/mileage.json').write_text(json.dumps(result,indent=2)+'\\\\n')\nassert len(paid)+len(late)+len(dup)==len(rows)\nassert sum(paid.values())==sum(totals.values())\nprint(Path('out/mileage.json').read_text())\nprint('Verified all',len(rows),'claims classified exactly once; total paid:',sum(totals.values()))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv,json,re\nfrom datetime import date\nfrom decimal import Decimal\nfrom pathlib import Path\nrows=list(csv.DictReader(open('claims.csv')))\nemployees=json.load(open('employees.json'))\nrates=json.load(open('rates.json'))\nnum=lambda r:int(re.search(r'\\\\d+\"'$'\"',r['claim_id']).group())\nlate=[]; dup=[]; accepted=[]; seen=set()\nfor r in sorted(rows,key=lambda r:(r['submitted_on'],num(r))):\n    if (date.fromisoformat(r['submitted_on'])-date.fromisoformat(r['trip_date'])).days>rates['late_after_days']:\n        late.append(r);continue\n    key=(r['employee_id'],r['trip_date'],*(' '.join(r[k].casefold().split()) for k in ('from','to')))\n    if key in seen:dup.append(r);continue\n    seen.add(key);accepted.append(r)\nused={};paid={};totals={}\nfor r in sorted(accepted,key=lambda r:(r['trip_date'],num(r))):\n    d=date.fromisoformat(r['trip_date']);year=d.year if (d.month,d.day)>=(4,6) else d.year-1\n    emp=r['employee_id'];key=(emp,year)\n    before=used.get(key,employees[emp]['miles_before_2025_26'] if year==2025 else 0)\n    miles=int(r['miles']); first=min(miles,max(0,rates['threshold_miles']-before))\n    pay=first*Decimal(rates['rate_first'])+(miles-first)*Decimal(rates['rate_after'])+miles*int(r['passengers'])*Decimal(rates['passenger_per_mile'])\n    used[key]=before+miles;paid[r['claim_id']]=pay;totals[emp]=totals.get(emp,Decimal(0))+pay\nresult={'paid':{k:float(v) for k,v in sorted(paid.items(),key=lambda kv:int(kv[0].split('-')[-1]))},'rejected_late':[r['claim_id'] for r in sorted(late,key=num)],'duplicates':[r['claim_id'] for r in sorted(dup,key=num)],'employee_totals':{k:float(v) for k,v in sorted(totals.items())}}\nPath('out').mkdir(exist_ok=True)\nPath('out/mileage.json').write_text(json.dumps(result,indent=2)+'\\\\n')\nassert len(paid)+len(late)+len(dup)==len(rows)\nassert sum(paid.values())==sum(totals.values())\nprint(Path('out/mileage.json').read_text())\nprint('Verified all',len(rows),'claims classified exactly once; total paid:',sum(totals.values()))\nPY\"", "aggregated_output": "{\n  \"paid\": {\n    \"MC-94\": 87.75,\n    \"MC-96\": 142.0,\n    \"MC-97\": 25.85,\n    \"MC-100\": 71.8,\n    \"MC-101\": 47.25,\n    \"MC-103\": 73.0,\n    \"MC-105\": 78.65,\n    \"MC-106\": 131.45,\n    \"MC-107\": 48.6,\n    \"MC-110\": 69.3,\n    \"MC-112\": 141.75,\n    \"MC-114\": 77.7,\n    \"MC-117\": 60.75,\n    \"MC-118\": 91.3,\n    \"MC-120\": 92.95,\n    \"MC-121\": 81.4,\n    \"MC-124\": 23.1,\n    \"MC-127\": 41.4,\n    \"MC-131\": 45.0,\n    \"MC-132\": 89.1\n  },\n  \"rejected_late\": [\n    \"MC-113\",\n    \"MC-128\"\n  ],\n  \"duplicates\": [\n    \"MC-134\",\n    \"MC-135\"\n  ],\n  \"employee_totals\": {\n    \"EM-32\": 463.75,\n    \"EM-36\": 587.75,\n    \"EM-41\": 229.75,\n    \"EM-42\": 238.85\n  }\n}\n\nVerified all 24 claims classified exactly once; total paid: 1520.10\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "agent_message", "text": "Created [out/mileage.json](out/mileage.json).\n\nVerified all 24 claims: 20 paid, 2 rejected late, and 2 duplicates. Total payable: **\u00a31,520.10**."}}
{"type": "turn.completed", "usage": {"input_tokens": 49686, "cached_input_tokens": 41728, "cache_write_input_tokens": 0, "output_tokens": 771, "reasoning_output_tokens": 0}}

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