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GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Monthly FIFO inventory report (COGS, write-offs, oversell exceptions) from a messy, shuffled stock-movement export

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
46s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Task: monthly FIFO inventory report `transactions.csv` is an export of a small shop's stock movements. Write `fifo.py` (Python stdlib only) that reads `transactions.csv` from the current directory and writes `report.csv` and `exceptions.json` there, following the rules below exactly. Run it. The grader compares your two output files with the correct ones, then re-runs `fifo.py` on a freshly generated file of the same kind (different values, same rules), so do not hard-code anything. No network. ## Input Columns: `seq,date,sku,type,qty,unit_amount`. 1. `seq` is a unique integer per transaction. The export contains some exact duplicate rows (same `seq`); a row whose `seq` was already seen must be ignored. 2. Rows are NOT in order. Process transactions sorted by `date` (YYYY-MM-DD), then by `seq` ascending. 3. `sku`: trim spaces and upper-case it (` ab-1` and `AB-1` are the same SKU). `type` is case-insensitive and is one of `BUY`, `SELL`, `WRITE_OFF`. `qty` is a positive integer, possibly with surrounding spaces. 4. `unit_amount` is a decimal number that may have a leading `$`, thousands separators (`,`) and up to 4 decimal places, e.g. `$1,234.5678`. For BUY it is the unit cost; for SELL the unit sale price; for WRITE_OFF it is empty. ## Costing 5. Each BUY creates a lot (qty, unit cost). SELL and WRITE_OFF consume units from the oldest remaining lots first (FIFO), across as many lots as needed. 6. COGS of a SELL = sum over consumed units of their lot's unit cost; revenue = units actually sold x unit price. A WRITE_OFF's consumed cost is write-off cost (no revenue, not COGS, not units sold). 7. If a SELL or WRITE_OFF asks for more units than are on hand, consume everything on hand and record the missing units as an exception; revenue only counts the units actually sold. 8. Use exact decimal arithmetic. Round to 2 decimal places (half-up) only when writing the report; every reported number must equal the exact value rounded that way. ## Output 9. `report.csv` with header `month,sku,units_sold,revenue,cogs,write_off_cost,gross_margin,ending_units,ending_value`: one row per (month, sku) for every SKU that has at least one transaction in that month (month = `YYYY-MM`), sorted by month then sku. `gross_margin = revenue - cogs`, computed from the exact unrounded revenue and COGS and then rounded (so it can differ by 0.01 from subtracting the two rounded columns). `ending_units` and `ending_value` are that SKU's units on hand and their total cost at the end of that month (after all its transactions up to and including that month). Money columns are written with exactly 2 decimals (e.g. `12.50`, `0.00`, `-3.10`), no `$` or separators; unit columns as integers. 10. `exceptions.json`: a JSON list with one object `{"seq": <int>, "sku": "<normalised sku>", "short": <int missing units>}` per SELL/WRITE_OFF that was short, in processing order (`[]` if none).

Needs dedupe by seq, date+seq ordering, SKU/type normalisation, $/thousands parsing, multi-lot FIFO consumption, shortfall handling, exact Decimal maths with rounding only at output (gross margin from unrounded values), and a fresh-seed rerun blocks hand-editing. 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_correct: report.csv header, rows, order and every value exact to the cent, money columns with 2 decimals. 2. exceptions_correct: exceptions.json equals the expected list in processing order. 3. fresh_data_ok: fifo.py re-run on fresh data (seed 4747) produces correct report.csv and exceptions.json.

Setup notes: Use Case Scout batch7 case g04-fifo-inventory-cogs (offline fixture). Prepared with batch7/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: 3/3 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 3 (1 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.04
Wall time
46s
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 on67,7771,680$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_correct: report.csv header, rows, order and every value exact to the cent, money columns with 2 decimals. 2. exceptions_correct: exceptions.json equals the expected list in processing order. 3. fresh_data_ok: fifo.py re-run on fresh data (seed 4747) produces correct report.csv and exceptions.json.
  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": "01a16447-80f0-7000-997b-c944974be849"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the input, implement FIFO costing with exact arithmetic, and run `fifo.py` to generate both output files."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'transactions.csv' -g 'fifo.py' && head -n 6 transactions.csv\"", "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 'transactions.csv' -g 'fifo.py' && head -n 6 transactions.csv\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/183_g04/work\ntransactions.csv\nseq,date,sku,type,qty,unit_amount\r\n1460,2026-04-22,En-33,sell,11,167.76\r\n1619,2026-05-11,cx-10,BUY,9 ,5.2900\r\n1597,2026-05-08,Gh-12,SELL, 7,1337.725\r\n1785,2026-06-01,Dl-7,Sell, 5,989\r\n1675,2026-05-17,AB-2 ,Buy, 21,$565.1263\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/183_g04/work/fifo.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/183_g04/work/fifo.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python fifo.py && python - <<'PY'\nimport csv, json\nfrom fifo import amount, money\nassert amount('\"'$1,234.5678'\"') == 12345678\nassert money(10050) == '1.01'\nassert money(-10050) == '-1.01'\nwith open('report.csv', newline='') as f:\n    rows = list(csv.DictReader(f))\nassert [(r['month'], r['sku']) for r in rows] == sorted((r['month'], r['sku']) for r in rows)\nwith open('exceptions.json') as f:\n    exceptions = json.load(f)\nprint(f'Generated {len(rows)} report rows and {len(exceptions)} shortage exceptions.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python fifo.py && python - <<'PY'\nimport csv, json\nfrom fifo import amount, money\nassert amount('\"'$1,234.5678'\"') == 12345678\nassert money(10050) == '1.01'\nassert money(-10050) == '-1.01'\nwith open('report.csv', newline='') as f:\n    rows = list(csv.DictReader(f))\nassert [(r['month'], r['sku']) for r in rows] == sorted((r['month'], r['sku']) for r in rows)\nwith open('exceptions.json') as f:\n    exceptions = json.load(f)\nprint(f'Generated {len(rows)} report rows and {len(exceptions)} shortage exceptions.')\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 fifo.py && python3 - <<'PY'\nimport csv, json\nfrom fifo import amount, money\nassert amount('\"'$1,234.5678'\"') == 12345678\nassert money(10050) == '1.01'\nassert money(-10050) == '-1.01'\nwith open('report.csv', newline='') as f:\n    rows = list(csv.DictReader(f))\nassert [(r['month'], r['sku']) for r in rows] == sorted((r['month'], r['sku']) for r in rows)\nwith open('exceptions.json') as f:\n    exceptions = json.load(f)\nprint(f'Generated {len(rows)} report rows and {len(exceptions)} shortage exceptions.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 fifo.py && python3 - <<'PY'\nimport csv, json\nfrom fifo import amount, money\nassert amount('\"'$1,234.5678'\"') == 12345678\nassert money(10050) == '1.01'\nassert money(-10050) == '-1.01'\nwith open('report.csv', newline='') as f:\n    rows = list(csv.DictReader(f))\nassert [(r['month'], r['sku']) for r in rows] == sorted((r['month'], r['sku']) for r in rows)\nwith open('exceptions.json') as f:\n    exceptions = json.load(f)\nprint(f'Generated {len(rows)} report rows and {len(exceptions)} shortage exceptions.')\nPY\"", "aggregated_output": "Generated 32 report rows and 38 shortage exceptions.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "Created `fifo.py` using only Python\u2019s standard library and exact fixed-point arithmetic. Ran it successfully with `python3 fifo.py`.\n\nGenerated:\n- `report.csv`: 32 rows\n- `exceptions.json`: 38 shortage exceptions"}}
{"type": "turn.completed", "usage": {"input_tokens": 67777, "cached_input_tokens": 61696, "cache_write_input_tokens": 0, "output_tokens": 1680, "reasoning_output_tokens": 107}}

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