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GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Work out late-payment interest and compensation from part payments, credit notes and a base-rate change

OGPT-6.1 Soldefault · thinking on · 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
33s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Work out a design studio's late-payment interest and compensation Inputs: `invoices.csv` (inv_id, client, issued, due, amount), `credits.csv` (inv_id, amount), `payments.csv` (payment_id, client, paid_on, amount), `base-rates.csv` (from_date, rate), `terms.json` and `bookkeeper-note.txt`. Amounts are in pounds. `terms.json` and these rules are the record; where the bookkeeper's note disagrees, the rules win. Rules: - Clients match ignoring letter case and surrounding spaces; report them in upper case. A credit note reduces its invoice's amount from the start. - Payments are applied in paid_on order (ties by payment_id), each to the client's invoices with the earliest due date first (ties by the invoice number), filling one before the next; anything left over is unapplied. - Interest accrues for each day after the due date up to and including as_of on the amount still unpaid at the end of that day, at (statutory_pct + the base rate in force on that day) percent a year, divided by 365; each invoice's interest is rounded half-up to 0.01 once, at the end. - An invoice still unpaid at as_of, or paid in full only after its due date, earns one compensation fee from the first terms.json band whose `below` is more than its amount after credit notes (`below` null is the top band); an invoice paid in full on or before its due date earns none. - Write `out/late-payment.json`: `{"balances": {"<inv_id>": <unpaid at as_of>, ...}, "unapplied": {"<CLIENT>": <amount>, ...}, "interest": {"<inv_id>": <interest>, ...}, "compensation": {"<inv_id>": <fee>, ...}, "client_totals": {"<CLIENT>": <balances + interest + compensation>, ...}}`. `balances` and `client_totals` cover every invoice and client; the other maps list only amounts above 0. ## 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: allocation interest compensation client_totals

Setup notes: batch30 case o03-late-payment-interest (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 2068431190): 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
33s
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 on49,5261,142$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 on) via Codex CLI 0.163.0 (codex exec --json), cloud.
  3. It counts as a success only if it meets every success criterion: allocation interest compensation client_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

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

Evidence log
{"type": "thread.started", "thread_id": "01a16835-5798-7d63-ac61-46d8a43d8d7a"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll read the inputs, apply the allocation and accrual rules, and write the result to `out/late-payment.json`.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; cat terms.json bookkeeper-note.txt; cat invoices.csv credits.csv payments.csv base-rates.csv'", "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 terms.json bookkeeper-note.txt; cat invoices.csv credits.csv payments.csv base-rates.csv'", "aggregated_output": "total 44\ndrwxr-xr-x 8 bob bob 4096 Oct 23 06:01 .\ndrwxrwxr-x 3 bob bob 4096 Oct 23 06:01 ..\ndr-xr-xr-x 2 bob bob   40 Oct 23 06:01 .agents\ndr-xr-xr-x 2 bob bob   40 Oct 23 06:01 .aws\ndr-xr-xr-x 2 bob bob   40 Oct 23 06:01 .codex\ndr-xr-xr-x 2 bob bob   40 Oct 23 06:01 .git\n-rw-r--r-- 1 bob bob 2168 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   47 Oct 23 05:55 base-rates.csv\n-rw-r--r-- 1 bob bob  204 Oct 23 05:55 bookkeeper-note.txt\n-rw-r--r-- 1 bob bob   75 Oct 23 05:55 credits.csv\n-rw-r--r-- 1 bob bob  566 Oct 23 05:55 invoices.csv\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 out\n-rw-r--r-- 1 bob bob  374 Oct 23 05:55 payments.csv\n-rw-r--r-- 1 bob bob  218 Oct 23 05:55 terms.json\n{\n \"as_of\": \"2026-11-30\",\n \"statutory_pct\": \"8\",\n \"compensation\": [\n  {\n   \"below\": \"1000.00\",\n   \"fee\": \"40.00\"\n  },\n  {\n   \"below\": \"10000.00\",\n   \"fee\": \"70.00\"\n  },\n  {\n   \"below\": null,\n   \"fee\": \"100.00\"\n  }\n ]\n}Bookkeeper note: use the base rate from the due date for the whole period, it's what we always do.\nCompensation goes on the invoice total before any credit note. Payments clear the newest invoices first.\ninv_id,client,issued,due,amount\r\nINV-110,Northgate Dental,2026-08-16,2026-09-15,5536.79\r\nINV-107,pellow & sons ,2026-07-30,2026-08-29,903.29\r\nINV-96,Kestrel Books,2026-09-02,2026-10-02,908.68\r\nINV-103,pellow & sons ,2026-08-12,2026-09-11,402.03\r\nINV-95,Kestrel Books,2026-07-25,2026-08-24,1011.51\r\nINV-102,Pellow & Sons,2026-09-11,2026-10-11,840.20\r\nINV-108,NORTHGATE DENTAL,2026-08-08,2026-09-07,1107.80\r\nINV-112,NORTHGATE DENTAL,2026-07-22,2026-08-21,851.74\r\nINV-99,kestrel books ,2026-09-04,2026-10-04,642.48\r\nINV-104,PELLOW & SONS,2026-08-28,2026-09-27,3657.86\r\ninv_id,amount\r\nINV-96,82.23\r\nINV-103,257.12\r\nINV-108,133.33\r\nINV-95,58.26\r\npayment_id,client,paid_on,amount\r\nPAY-006,Northgate Dental,2026-08-21,851.74\r\nPAY-001,Kestrel Books,2026-08-24,5573.30\r\nPAY-005,pellow & sons,2026-09-24,669.33\r\nPAY-004,Pellow & Sons,2026-09-17,391.31\r\nPAY-008,northgate dental,2026-09-30,766.64\r\nPAY-003,Pellow & Sons,2026-08-29,903.29\r\nPAY-002,kestrel books,2026-10-11,1696.05\r\nPAY-007,Northgate Dental,2026-09-26,1374.11\r\nfrom_date,rate\r\n2026-05-01,4.25\r\n2026-09-29,4\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\ncat > out/calculate.py <<'PY'\nimport csv,json\nfrom decimal import Decimal as D, ROUND_HALF_UP\nfrom datetime import date,timedelta\nfrom pathlib import Path\n\ndef rows(name):\n    with open(name,newline='') as f: return list(csv.DictReader(f))\ndef dt(s): return date.fromisoformat(s)\ndef money(v): return float(v.quantize(D('.01'),rounding=ROUND_HALF_UP))\nterms=json.loads(Path('terms.json').read_text())\nasof=dt(terms['as_of'])\ncredits={}\nfor r in rows('credits.csv'): credits[r['inv_id']]=credits.get(r['inv_id'],D(0))+D(r['amount'])\ninvoices={}\nfor r in rows('invoices.csv'):\n    k=r['inv_id']; amount=D(r['amount'])-credits.get(k,D(0))\n    invoices[k]={'client':r['client'].strip().upper(),'due':dt(r['due']),'amount':amount,'balance':amount,'allocations':[],'paid_full':None}\nunapplied={}\nfor p in sorted(rows('payments.csv'),key=lambda p:(dt(p['paid_on']),p['payment_id'])):\n    day=dt(p['paid_on'])\n    if day>asof: continue\n    client=p['client'].strip().upper(); remaining=D(p['amount'])\n    eligible=sorted(((k,v) for k,v in invoices.items() if v['client']==client),key=lambda kv:(kv[1]['due'],int(kv[0].split('-')[-1])))\n    for k,v in eligible:\n        applied=min(remaining,v['balance'])\n        if applied>0:\n            v['balance']-=applied; remaining-=applied\n            v['allocations'].append((day,applied))\n            if v['balance']==0: v['paid_full']=day\n    if remaining>0: unapplied[client]=unapplied.get(client,D(0))+remaining\nrates=sorted((dt(r['from_date']),D(r['rate'])) for r in rows('base-rates.csv'))\nresult={'balances':{},'unapplied':{k:money(v) for k,v in sorted(unapplied.items()) if v>0},'interest':{},'compensation':{},'client_totals':{}}\nfor k,v in invoices.items():\n    interest=D(0); day=v['due']+timedelta(days=1)\n    while day<=asof:\n        balance=v['amount']-sum((amount for paid,amount in v['allocations'] if paid<=day),D(0))\n        rate=next(rate for start,rate in reversed(rates) if start<=day)\n        interest+=balance*(D(terms['statutory_pct'])+rate)/D(100)/D(365)\n        day+=timedelta(days=1)\n    interest=interest.quantize(D('.01'),rounding=ROUND_HALF_UP)\n    fee=D(0)\n    if v['balance']>0 or (v['paid_full'] is not None and v['paid_full']>v['due']):\n        fee=next(D(b['fee']) for b in terms['compensation'] if b['below'] is None or v['amount']<D(b['below']))\n    result['balances'][k]=money(v['balance'])\n    if interest>0: result['interest'][k]=money(interest)\n    if fee>0: result['compensation'][k]=money(fee)\n    c=v['client']; result['client_totals'][c]=result['client_totals'].get(c,D(0))+v['balance']+interest+fee\nresult['client_totals']={k:money(v) for k,v in sorted(result['client_totals'].items())}\nassert sum((v['balance'] for v in invoices.values()),D(0)) == sum((v['amount'] for v in invoices.values()),D(0))-sum((D(p['amount']) for p in rows('payments.csv') if dt(p['paid_on'])<=asof),D(0))+sum(unapplied.values(),D(0))\nPath('out/late-payment.json').write_text(json.dumps(result,indent=2)+'\\\\n')\nprint(json.dumps(result,indent=2))\nPY\npython out/calculate.py\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\ncat > out/calculate.py <<'PY'\nimport csv,json\nfrom decimal import Decimal as D, ROUND_HALF_UP\nfrom datetime import date,timedelta\nfrom pathlib import Path\n\ndef rows(name):\n    with open(name,newline='') as f: return list(csv.DictReader(f))\ndef dt(s): return date.fromisoformat(s)\ndef money(v): return float(v.quantize(D('.01'),rounding=ROUND_HALF_UP))\nterms=json.loads(Path('terms.json').read_text())\nasof=dt(terms['as_of'])\ncredits={}\nfor r in rows('credits.csv'): credits[r['inv_id']]=credits.get(r['inv_id'],D(0))+D(r['amount'])\ninvoices={}\nfor r in rows('invoices.csv'):\n    k=r['inv_id']; amount=D(r['amount'])-credits.get(k,D(0))\n    invoices[k]={'client':r['client'].strip().upper(),'due':dt(r['due']),'amount':amount,'balance':amount,'allocations':[],'paid_full':None}\nunapplied={}\nfor p in sorted(rows('payments.csv'),key=lambda p:(dt(p['paid_on']),p['payment_id'])):\n    day=dt(p['paid_on'])\n    if day>asof: continue\n    client=p['client'].strip().upper(); remaining=D(p['amount'])\n    eligible=sorted(((k,v) for k,v in invoices.items() if v['client']==client),key=lambda kv:(kv[1]['due'],int(kv[0].split('-')[-1])))\n    for k,v in eligible:\n        applied=min(remaining,v['balance'])\n        if applied>0:\n            v['balance']-=applied; remaining-=applied\n            v['allocations'].append((day,applied))\n            if v['balance']==0: v['paid_full']=day\n    if remaining>0: unapplied[client]=unapplied.get(client,D(0))+remaining\nrates=sorted((dt(r['from_date']),D(r['rate'])) for r in rows('base-rates.csv'))\nresult={'balances':{},'unapplied':{k:money(v) for k,v in sorted(unapplied.items()) if v>0},'interest':{},'compensation':{},'client_totals':{}}\nfor k,v in invoices.items():\n    interest=D(0); day=v['due']+timedelta(days=1)\n    while day<=asof:\n        balance=v['amount']-sum((amount for paid,amount in v['allocations'] if paid<=day),D(0))\n        rate=next(rate for start,rate in reversed(rates) if start<=day)\n        interest+=balance*(D(terms['statutory_pct'])+rate)/D(100)/D(365)\n        day+=timedelta(days=1)\n    interest=interest.quantize(D('.01'),rounding=ROUND_HALF_UP)\n    fee=D(0)\n    if v['balance']>0 or (v['paid_full'] is not None and v['paid_full']>v['due']):\n        fee=next(D(b['fee']) for b in terms['compensation'] if b['below'] is None or v['amount']<D(b['below']))\n    result['balances'][k]=money(v['balance'])\n    if interest>0: result['interest'][k]=money(interest)\n    if fee>0: result['compensation'][k]=money(fee)\n    c=v['client']; result['client_totals'][c]=result['client_totals'].get(c,D(0))+v['balance']+interest+fee\nresult['client_totals']={k:money(v) for k,v in sorted(result['client_totals'].items())}\nassert sum((v['balance'] for v in invoices.values()),D(0)) == sum((v['amount'] for v in invoices.values()),D(0))-sum((D(p['amount']) for p in rows('payments.csv') if dt(p['paid_on'])<=asof),D(0))+sum(unapplied.values(),D(0))\nPath('out/late-payment.json').write_text(json.dumps(result,indent=2)+'\\\\n')\nprint(json.dumps(result,indent=2))\nPY\npython out/calculate.py\"", "aggregated_output": "{\n  \"balances\": {\n    \"INV-110\": 4370.51,\n    \"INV-107\": 0.0,\n    \"INV-96\": 0.0,\n    \"INV-103\": 0.0,\n    \"INV-95\": 0.0,\n    \"INV-102\": 840.2,\n    \"INV-108\": 0.0,\n    \"INV-112\": 0.0,\n    \"INV-99\": 0.0,\n    \"INV-104\": 2742.13\n  },\n  \"unapplied\": {\n    \"KESTREL BOOKS\": 4847.17\n  },\n  \"interest\": {\n    \"INV-110\": 114.53,\n    \"INV-103\": 0.24,\n    \"INV-102\": 13.81,\n    \"INV-108\": 5.89,\n    \"INV-104\": 57.72\n  },\n  \"compensation\": {\n    \"INV-110\": 70.0,\n    \"INV-103\": 40.0,\n    \"INV-102\": 40.0,\n    \"INV-108\": 40.0,\n    \"INV-104\": 70.0\n  },\n  \"client_totals\": {\n    \"KESTREL BOOKS\": 0.0,\n    \"NORTHGATE DENTAL\": 4600.93,\n    \"PELLOW & SONS\": 3804.1\n  }\n}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "agent_message", "text": "Created [out/late-payment.json](out/late-payment.json) using the stated rules. Verified that invoice balances, payments, and unapplied funds reconcile."}}
{"type": "turn.completed", "usage": {"input_tokens": 49526, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 1142, "reasoning_output_tokens": 24}}

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