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GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Monthly regional report from messy orders CSV + shipments JSONL + weekday-only FX table

OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.04
Wall time
47s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Task: monthly regional order report Inputs: `orders.csv`, `shipments.jsonl`, `fx.csv`. Write `report.py` (Python stdlib only) that reads those three files from the current directory and writes `report.json`, then run it. `report.json` shape: `{"<Region>": {"YYYY-MM": {"orders": int, "revenue_eur": float, "unshipped": int, "late": int}}}` Rules: - Regions are North/South/East/West; the raw data uses many spellings/abbreviations/cases/whitespace (n, NORTH, " North ", E, ...). Normalise to those four names. - Order IDs are case-insensitive (`o-00012` = `O-00012`). Rows that are exact duplicates of an earlier row are dropped. Cancelled orders (any case) are excluded entirely. - `order_date` is ISO `YYYY-MM-DD`, `DD/MM/YYYY`, or `Mon DD YYYY`. The month bucket is the order date's month. - Amounts: USD amounts may have thousands separators (`1,234.50`); EUR amounts may use a decimal comma (`12,50`); GBP is plain. Convert to EUR with `fx.csv` (EUR per 1 unit; EUR itself is 1.0) using the rate on the order date, or if there is none that day (weekends), the most recent earlier date. Round `revenue_eur` to 2 decimals only at the end (sum unrounded values). - `unshipped` = kept orders with no shipment record. `late` = kept orders whose **first** shipment date (UTC date of `shipped_at`) is more than 5 days after the order date. Shipments for unknown orders and blank lines are ignored. `report.py` will also be re-run by the grader on freshly generated data with the same format, so don't hard-code anything. No network.

Many small traps: region aliases, case-insensitive IDs, exact-duplicate rows, three date formats, locale-specific amounts, weekend FX fallback, first-shipment lateness across Z/+00:00, orphan shipments; re-run on fresh data blocks hard-coding. 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. report_exact: every region/month cell in report.json matches the key (counts exact, revenue_eur within 0.01) and no extra cells. 2. fresh_data_exact: the agent's report.py, re-run on a freshly generated dataset (seed 9191), produces an exactly matching report.

Setup notes: Use Case Scout batch5 case e07-shipments-reconcile (offline fixture). Prepared with batch5/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 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. 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: 2/2 checks. 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
$0.04
Wall time
47s
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 on84,6581,858$0.04

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: 1. report_exact: every region/month cell in report.json matches the key (counts exact, revenue_eur within 0.01) and no extra cells. 2. fresh_data_exact: the agent's report.py, re-run on a freshly generated dataset (seed 9191), produces an exactly matching report.
  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": "01a16431-6743-7ed2-987e-9536e82e02c2"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the input formats, write `report.py` using only the standard library, then run it and check the output.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la; head -n 8 orders.csv; head -n 6 shipments.jsonl; head -n 8 fx.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; head -n 8 orders.csv; head -n 6 shipments.jsonl; head -n 8 fx.csv'", "aggregated_output": "total 160\ndrwxr-xr-x 6 bob bob   4096 Oct 22 11:18 .\ndrwxrwxr-x 3 bob bob   4096 Oct 22 11:18 ..\ndr-xr-xr-x 2 bob bob     40 Oct 22 11:18 .agents\ndr-xr-xr-x 2 bob bob     40 Oct 22 11:18 .aws\ndr-xr-xr-x 2 bob bob     40 Oct 22 11:18 .codex\ndr-xr-xr-x 2 bob bob     40 Oct 22 11:18 .git\n-rw-r--r-- 1 bob bob   1534 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob   3791 Oct 22 10:08 fx.csv\n-rw-r--r-- 1 bob bob  33876 Oct 22 10:08 orders.csv\n-rw-r--r-- 1 bob bob 108052 Oct 22 10:08 shipments.jsonl\norder_id,region,order_date,currency,amount,status\r\nO-00001,NORTH,Mar 04 2026,EUR,454.02,ok\r\nO-00002,W,Apr 26 2026,EUR,34.76,ok\r\nO-00003, North ,Feb 05 2026,GBP,672.05,ok\r\no-00004,NORTH,29/03/2026,EUR,410.80,OK\r\nO-00005,WEST,01/03/2026,GBP,121.86,ok\r\nO-00006,s,2026-04-12,EUR,155.06,ok\r\nO-00007,east ,2026-03-28,GBP,578.79,ok\r\n{\"order\": \"O-00368\", \"shipped_at\": \"2026-01-27T19:13:00+00:00\", \"carrier\": \"dhl\", \"events\": [{\"type\": \"shipped\"}]}\n{\"order\": \"O-00617\", \"shipped_at\": \"2026-03-19T05:47:00+00:00\", \"carrier\": \"DHL\", \"events\": [{\"type\": \"shipped\"}]}\n{\"order\": \"O-00385\", \"shipped_at\": \"2026-04-18T02:26:00Z\", \"carrier\": \"dhl\", \"events\": [{\"type\": \"shipped\"}]}\n{\"order\": \"O-00657\", \"shipped_at\": \"2026-01-26T03:29:00Z\", \"carrier\": \"dhl\", \"events\": [{\"type\": \"shipped\"}]}\n{\"order\": \"O-00146\", \"shipped_at\": \"2026-02-17T01:23:00Z\", \"carrier\": \"dhl\", \"events\": [{\"type\": \"shipped\"}]}\n{\"order\": \"O-00546\", \"shipped_at\": \"2026-04-23T10:36:00+00:00\", \"carrier\": \"dhl\", \"events\": [{\"type\": \"shipped\"}]}\ndate,currency,eur_per_unit\n2026-01-01,USD,0.8518\n2026-01-01,GBP,1.1706\n2026-01-02,USD,0.8835\n2026-01-02,GBP,1.1799\n2026-01-05,USD,0.8964\n2026-01-05,GBP,1.1202\n2026-01-06,USD,0.9385\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "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))\nfor key in ['region','status','currency']:\n print(key,sorted({r[key] for r in rows}))\nprint('rows',len(rows),'exact duplicates',len(rows)-len({tuple(r.items()) for r in rows}))\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,collections\nwith open('orders.csv',newline='') as f:\n rows=list(csv.DictReader(f))\nfor key in ['region','status','currency']:\n print(key,sorted({r[key] for r in rows}))\nprint('rows',len(rows),'exact duplicates',len(rows)-len({tuple(r.items()) for r in rows}))\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv\nwith open('orders.csv',newline='') as f:\n rows=list(csv.DictReader(f))\nfor key in ['region','status','currency']:\n print(key,sorted({r[key] for r in rows}))\nprint('rows',len(rows),'exact duplicates',len(rows)-len({tuple(r.items()) for r in rows}))\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv\nwith open('orders.csv',newline='') as f:\n rows=list(csv.DictReader(f))\nfor key in ['region','status','currency']:\n print(key,sorted({r[key] for r in rows}))\nprint('rows',len(rows),'exact duplicates',len(rows)-len({tuple(r.items()) for r in rows}))\nPY\"", "aggregated_output": "region [' North ', 'E', 'NORTH', 'South', 'W', 'WEST', 'east ', 'n', 's', 'south', 'west']\nstatus ['CANCELLED', 'OK', 'cancelled', 'ok']\ncurrency ['EUR', 'GBP', 'USD']\nrows 847 exact duplicates 47\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/165_e07/work/report.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/165_e07/work/report.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 report.py\npython3 - <<'PY'\nimport json\nwith open('report.json') as f:\n report=json.load(f)\nprint('Regions:', ', '.join(report))\nprint('Month buckets:', sum(map(len,report.values())))\nprint('Totals:', {k: sum(b[k] for m in report.values() for b in m.values()) for k in ('orders','unshipped','late')})\nassert set(report) == {'North','South','East','West'}\nassert all(0 <= b['late'] <= b['orders'] - b['unshipped'] for m in report.values() for b in m.values())\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 report.py\npython3 - <<'PY'\nimport json\nwith open('report.json') as f:\n report=json.load(f)\nprint('Regions:', ', '.join(report))\nprint('Month buckets:', sum(map(len,report.values())))\nprint('Totals:', {k: sum(b[k] for m in report.values() for b in m.values()) for k in ('orders','unshipped','late')})\nassert set(report) == {'North','South','East','West'}\nassert all(0 <= b['late'] <= b['orders'] - b['unshipped'] for m in report.values() for b in m.values())\nPY\"", "aggregated_output": "Regions: East, North, South, West\nMonth buckets: 16\nTotals: {'orders': 709, 'unshipped': 50, 'late': 212}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "Created [report.py](report.py) and ran it successfully, generating [report.json](report.json).\n\nVerified 4 regions and 16 monthly buckets: 709 kept orders, 50 unshipped, and 212 late."}}
{"type": "turn.completed", "usage": {"input_tokens": 84658, "cached_input_tokens": 79360, "cache_write_input_tokens": 0, "output_tokens": 1858, "reasoning_output_tokens": 8}}

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