GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking off) on Monthly cohort retention + revenue from a messy orders CSV
OGPT-6.1 Soldefault · thinking off · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.03
- Wall time
- 29s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Task: monthly cohort retention from a messy orders export `orders.csv` (in your working directory) is an e-commerce orders export for Jan–Jun 2026. Known data issues: some dates are DD/MM/YYYY instead of YYYY-MM-DD; some user_ids have stray whitespace/lowercase (U00012 == " u00012 "); some rows are exact duplicates; only status=="paid" counts. Produce `answer.json`: ```json {"cohorts": {"2026-01": {"size": <int>, "retention": [1.0, m1, m2, ...]}, ...}, "paid_revenue_by_month": {"2026-01": <float>, ... "2026-06": <float>}} ``` - Cohort = month of a user's first paid order. size = number of users in it. - retention[k] = share of cohort users with ≥1 paid order in cohort month + k (k=0..through 2026-06), rounded to 4 decimals. - Revenue = sum of amount_usd of paid orders (deduplicated) per calendar month, 2 decimals. Do not use the network. You may write and run code.
Three silent data traps (mixed date formats, whitespace/lowercase ids, exact duplicates) plus status filtering. Any missed trap shifts numbers beyond tolerance. 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: 1. answer.json must list every cohort month present in the key; each cohort size must match exactly. 2. Each cohort's retention list must have the exact expected length and every value within ±0.002 of the key. 3. Paid revenue for each month Jan–Jun must be within ±$0.05 of the key. 4. Pass only if every check above passes (all cohorts and all 6 months).
Setup notes: Use Case Scout batch3 case c03-cohort-retention (offline fixture). Prepared with batch3/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: `python score.py WORKDIR/answer.json KEYDIR/key.json`. 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. 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: 18/18. 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 4 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.03
- Wall time
- 29s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking off | 62,664 | 880 | $0.03 |
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: 1. answer.json must list every cohort month present in the key; each cohort size must match exactly. 2. Each cohort's retention list must have the exact expected length and every value within ±0.002 of the key. 3. Paid revenue for each month Jan–Jun must be within ±$0.05 of the key. 4. Pass only if every check above passes (all cohorts and all 6 months).
- 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.
Was this useful?
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": "01a16419-57a6-7e42-8be1-dd13ca79d185"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the CSV, normalize dates and user IDs, deduplicate orders, and calculate retention and revenue from paid orders.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'orders.csv' -g '*.py' -g 'answer.json'\"", "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 'AGENTS.md' -g 'orders.csv' -g '*.py' -g 'answer.json'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/139_c03/work\norders.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'head -n 12 orders.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'head -n 12 orders.csv'", "aggregated_output": "order_id,user_id,order_date,amount_usd,status\r\nO001806,U00805,10/04/2026,36.53,paid\r\nO002655,U01199,2026-03-13,111.58,paid\r\nO002399,U01075,2026-02-12,171.97,paid\r\nO001414,U00638,2026-05-01,133.68,paid\r\nO002623,U01182,2026-04-14,59.11,paid\r\nO002652,U01198,2026-04-20,172.72,paid\r\nO002179,U00972,2026-02-26,74.73,paid\r\nO000245,U00109,2026-04-25,171.59,paid\r\nO001522,U00682,2026-06-07,76.41,paid\r\nO000487, u00220 ,2026-05-21,142.2,paid\r\nO002065,U00916,2026-03-22,112.27,paid\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv, collections\nwith open('orders.csv', newline='') as f:\n rows=list(csv.DictReader(f))\nprint('rows',len(rows),'statuses',dict(collections.Counter(r['status'] for r in rows)))\nprint('exact duplicates',len(rows)-len({tuple(r.items()) for r in rows}))\nids=collections.defaultdict(set)\nfor r in rows: ids[r['order_id']].add(tuple(r.items()))\nprint('order IDs with differing rows',sum(len(v)>1 for v in ids.values()))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport csv, collections\nwith open('orders.csv', newline='') as f:\n rows=list(csv.DictReader(f))\nprint('rows',len(rows),'statuses',dict(collections.Counter(r['status'] for r in rows)))\nprint('exact duplicates',len(rows)-len({tuple(r.items()) for r in rows}))\nids=collections.defaultdict(set)\nfor r in rows: ids[r['order_id']].add(tuple(r.items()))\nprint('order IDs with differing rows',sum(len(v)>1 for v in ids.values()))\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv, json\nfrom collections import defaultdict, Counter\nfrom datetime import datetime\nfrom decimal import Decimal, ROUND_HALF_UP\nfrom pathlib import Path\nwith open('orders.csv', newline='') as f:\n rows=list(csv.DictReader(f))\nseen=set()\npaid=[]\nfor r in rows:\n key=tuple(r.items())\n if key in seen: continue\n seen.add(key)\n if r['status'] \"'!= '\"'paid': continue\n date=datetime.strptime(r['order_date'], '%d/%m/%Y' if '/' in r['order_date'] else '%Y-%m-%d').date()\n assert date.year == 2026 and 1 <= date.month <= 6\n paid.append((r['user_id'].strip().upper(), date.month, Decimal(r['amount_usd'])))\nactivity=defaultdict(set)\nrevenue={m:Decimal('0') for m in range(1,7)}\nfor user, month, amount in paid:\n activity[user].add(month)\n revenue[month]+=amount\ncohorts=defaultdict(set)\nfor user, months in activity.items(): cohorts[min(months)].add(user)\nanswer={'cohorts':{}, 'paid_revenue_by_month':{}}\nfor month, users in sorted(cohorts.items()):\n answer['cohorts'][f'2026-{month:02}']={'size':len(users),'retention':[round(sum(m in activity[u] for u in users)/len(users),4) for m in range(month,7)]}\nfor month, amount in revenue.items():\n answer['paid_revenue_by_month'][f'2026-{month:02}']=float(amount.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP))\nPath('answer.json').write_text(json.dumps(answer,indent=2)+'\\\\n')\nassert sum(c['size'] for c in answer['cohorts'].values()) == len(activity)\nassert sum(revenue.values()) == sum((p[2] for p in paid), Decimal('0'))\nassert all(c['retention'][0] == 1.0 for c in answer['cohorts'].values())\nprint('Input rows:',len(rows),'Exact duplicates removed:',len(rows)-len(seen),'Paid orders:',len(paid),'Paid users:',len(activity))\nprint(json.dumps(answer,indent=2))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv, json\nfrom collections import defaultdict, Counter\nfrom datetime import datetime\nfrom decimal import Decimal, ROUND_HALF_UP\nfrom pathlib import Path\nwith open('orders.csv', newline='') as f:\n rows=list(csv.DictReader(f))\nseen=set()\npaid=[]\nfor r in rows:\n key=tuple(r.items())\n if key in seen: continue\n seen.add(key)\n if r['status'] \"'!= '\"'paid': continue\n date=datetime.strptime(r['order_date'], '%d/%m/%Y' if '/' in r['order_date'] else '%Y-%m-%d').date()\n assert date.year == 2026 and 1 <= date.month <= 6\n paid.append((r['user_id'].strip().upper(), date.month, Decimal(r['amount_usd'])))\nactivity=defaultdict(set)\nrevenue={m:Decimal('0') for m in range(1,7)}\nfor user, month, amount in paid:\n activity[user].add(month)\n revenue[month]+=amount\ncohorts=defaultdict(set)\nfor user, months in activity.items(): cohorts[min(months)].add(user)\nanswer={'cohorts':{}, 'paid_revenue_by_month':{}}\nfor month, users in sorted(cohorts.items()):\n answer['cohorts'][f'2026-{month:02}']={'size':len(users),'retention':[round(sum(m in activity[u] for u in users)/len(users),4) for m in range(month,7)]}\nfor month, amount in revenue.items():\n answer['paid_revenue_by_month'][f'2026-{month:02}']=float(amount.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP))\nPath('answer.json').write_text(json.dumps(answer,indent=2)+'\\\\n')\nassert sum(c['size'] for c in answer['cohorts'].values()) == len(activity)\nassert sum(revenue.values()) == sum((p[2] for p in paid), Decimal('0'))\nassert all(c['retention'][0] == 1.0 for c in answer['cohorts'].values())\nprint('Input rows:',len(rows),'Exact duplicates removed:',len(rows)-len(seen),'Paid orders:',len(paid),'Paid users:',len(activity))\nprint(json.dumps(answer,indent=2))\nPY\"", "aggregated_output": "Input rows: 2698 Exact duplicates removed: 42 Paid orders: 2450 Paid users: 1171\n{\n \"cohorts\": {\n \"2026-01\": {\n \"size\": 148,\n \"retention\": [\n 1.0,\n 0.3311,\n 0.2703,\n 0.1486,\n 0.1284,\n 0.0405\n ]\n },\n \"2026-02\": {\n \"size\": 216,\n \"retention\": [\n 1.0,\n 0.3889,\n 0.2731,\n 0.1157,\n 0.0648\n ]\n },\n \"2026-03\": {\n \"size\": 228,\n \"retention\": [\n 1.0,\n 0.4123,\n 0.2544,\n 0.1228\n ]\n },\n \"2026-04\": {\n \"size\": 217,\n \"retention\": [\n 1.0,\n 0.3733,\n 0.2212\n ]\n },\n \"2026-05\": {\n \"size\": 246,\n \"retention\": [\n 1.0,\n 0.3415\n ]\n },\n \"2026-06\": {\n \"size\": 116,\n \"retention\": [\n 1.0\n ]\n }\n },\n \"paid_revenue_by_month\": {\n \"2026-01\": 16862.2,\n \"2026-02\": 29862.41,\n \"2026-03\": 43820.16,\n \"2026-04\": 47451.77,\n \"2026-05\": 51556.03,\n \"2026-06\": 36742.33\n }\n}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "Created [answer.json](answer.json) with cohort retention and monthly paid revenue.\n\nNormalized dates and user IDs, removed 42 exact duplicates, and counted 2,450 paid orders across 1,171 users. Validation checks passed."}}
{"type": "turn.completed", "usage": {"input_tokens": 62664, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 880, "reasoning_output_tokens": 0}}