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GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking off) on Answer 8 business questions against a SQLite shop database

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

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.03
Wall time
39s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Task: answer business questions from a SQLite database `shop.db` is a SQLite database (use python's sqlite3; inspect the schema yourself). Only orders with status 'shipped' count as sales. Revenue = qty * unit_price from order_items (NOT list_price). Write `answer.json` with keys q1..q8: - q1: total shipped revenue (round 2) - q2: customer country with the highest shipped revenue (2-letter code) - q3: product category with highest shipped revenue NET of returns (subtract returned qty * that line's unit_price) - q4: number of customers with zero shipped orders - q5: returned units / shipped units, over shipped orders only (round 4) - q6: month (YYYY-MM) with highest shipped revenue - q7: number of distinct discontinued products that appear in shipped orders dated on/after 2026-07-01 - q8: average shipped order value (round 2) Also write `queries.sql` with the SQL you used. No network.

Traps: unit_price vs list_price, shipped-only filter, net-of-returns join per line, anti-join for zero-order customers, discontinued + date filter. 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. All 8 answers (q1–q8) must be correct: numeric answers within 0.01 (q5 within 0.0005); text answers exact match, case-insensitive. 2. Pass requires 8/8.

Setup notes: Use Case Scout batch3 case c12-sqlite-business-questions (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: 8/8. 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 5 (1 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.03
Wall time
39s
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 off81,3641,407$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: 1. All 8 answers (q1–q8) must be correct: numeric answers within 0.01 (q5 within 0.0005); text answers exact match, case-insensitive. 2. Pass requires 8/8.
  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": "01a1641e-f855-7fa1-a8e0-5bd6ba34040f"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the schema, calculate the results using shipped orders and line-item prices, and write `answer.json` and `queries.sql`.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'ls -la'", "aggregated_output": "total 308\ndrwxr-xr-x 6 bob bob   4096 Oct 22 10:58 .\ndrwxrwxr-x 3 bob bob   4096 Oct 22 10:58 ..\ndr-xr-xr-x 2 bob bob     40 Oct 22 10:58 .agents\ndr-xr-xr-x 2 bob bob     40 Oct 22 10:58 .aws\ndr-xr-xr-x 2 bob bob     40 Oct 22 10:58 .codex\ndr-xr-xr-x 2 bob bob     40 Oct 22 10:58 .git\n-rw-r--r-- 1 bob bob    899 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob 303104 Oct 22 10:08 shop.db\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"python - <<'PY'\nimport sqlite3\nc=sqlite3.connect('shop.db')\nfor row in c.execute(\\\"SELECT name, sql FROM sqlite_master WHERE type='table' ORDER BY name\\\"):\n print(row[0],row[1])\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 sqlite3\nc=sqlite3.connect('shop.db')\nfor row in c.execute(\\\"SELECT name, sql FROM sqlite_master WHERE type='table' ORDER BY name\\\"):\n print(row[0],row[1])\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 sqlite3\nc=sqlite3.connect('file:shop.db?mode=ro',uri=True)\nfor name,sql in c.execute(\\\"SELECT name, sql FROM sqlite_master WHERE type='table' ORDER BY name\\\"):\n print(name,sql)\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 sqlite3\nc=sqlite3.connect('file:shop.db?mode=ro',uri=True)\nfor name,sql in c.execute(\\\"SELECT name, sql FROM sqlite_master WHERE type='table' ORDER BY name\\\"):\n print(name,sql)\nPY\"", "aggregated_output": "customers CREATE TABLE customers(id integer primary key, name text, country text, signup_date text)\norder_items CREATE TABLE order_items(order_id int, product_id int, qty int, unit_price real)\norders CREATE TABLE orders(id integer primary key, customer_id int, order_date text, status text)\nproducts CREATE TABLE products(id integer primary key, sku text, category text, list_price real, discontinued int)\nreturns CREATE TABLE returns(order_id int, product_id int, qty int, return_date text)\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport sqlite3\nc=sqlite3.connect('file:shop.db?mode=ro',uri=True)\nfor label,sql in [\n('duplicate item keys','SELECT order_id, product_id, COUNT(*) FROM order_items GROUP BY 1,2 HAVING COUNT(*)>1 LIMIT 5'),\n('return rows','SELECT * FROM returns LIMIT 5'),\n('statuses','SELECT status, COUNT(*) FROM orders GROUP BY status'),\n('unmatched returns','SELECT COUNT(*) FROM returns r WHERE NOT EXISTS (SELECT 1 FROM order_items i WHERE i.order_id=r.order_id AND i.product_id=r.product_id)')]:\n print(label,c.execute(sql).fetchall())\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 sqlite3\nc=sqlite3.connect('file:shop.db?mode=ro',uri=True)\nfor label,sql in [\n('duplicate item keys','SELECT order_id, product_id, COUNT(*) FROM order_items GROUP BY 1,2 HAVING COUNT(*)>1 LIMIT 5'),\n('return rows','SELECT * FROM returns LIMIT 5'),\n('statuses','SELECT status, COUNT(*) FROM orders GROUP BY status'),\n('unmatched returns','SELECT COUNT(*) FROM returns r WHERE NOT EXISTS (SELECT 1 FROM order_items i WHERE i.order_id=r.order_id AND i.product_id=r.product_id)')]:\n print(label,c.execute(sql).fetchall())\nPY\"", "aggregated_output": "duplicate item keys []\nreturn rows [(6, 49, 1, '2026-10-01'), (7, 40, 2, '2026-10-01'), (17, 3, 1, '2026-10-01'), (17, 8, 2, '2026-10-01'), (32, 13, 1, '2026-10-01')]\nstatuses [('cancelled', 313), ('pending', 152), ('shipped', 2535)]\nunmatched returns [(0,)]\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"cat > /tmp/shop_answers.py <<'PY'\nimport json\nimport sqlite3\nfrom pathlib import Path\n\nqueries = {\n'q1': \\\"\\\"\\\"SELECT ROUND(SUM(i.qty * i.unit_price), 2)\nFROM orders o JOIN order_items i ON i.order_id = o.id\nWHERE o.status = 'shipped';\\\"\\\"\\\",\n'q2': \\\"\\\"\\\"SELECT c.country\nFROM customers c\nJOIN orders o ON o.customer_id = c.id\nJOIN order_items i ON i.order_id = o.id\nWHERE o.status = 'shipped'\nGROUP BY c.country\nORDER BY SUM(i.qty * i.unit_price) DESC, c.country\nLIMIT 1;\\\"\\\"\\\",\n'q3': \\\"\\\"\\\"WITH returned AS (\n    SELECT order_id, product_id, SUM(qty) AS qty\n    FROM returns\n    GROUP BY order_id, product_id\n)\nSELECT p.category\nFROM orders o\nJOIN order_items i ON i.order_id = o.id\nJOIN products p ON p.id = i.product_id\nLEFT JOIN returned r ON r.order_id = i.order_id AND r.product_id = i.product_id\nWHERE o.status = 'shipped'\nGROUP BY p.category\nORDER BY SUM((i.qty - COALESCE(r.qty, 0)) * i.unit_price) DESC, p.category\nLIMIT 1;\\\"\\\"\\\",\n'q4': \\\"\\\"\\\"SELECT COUNT(*)\nFROM customers c\nWHERE NOT EXISTS (\n    SELECT 1 FROM orders o WHERE o.customer_id = c.id AND o.status = 'shipped'\n);\\\"\\\"\\\",\n'q5': \\\"\\\"\\\"SELECT ROUND(\n    1.0 * (SELECT COALESCE(SUM(r.qty), 0)\n           FROM returns r JOIN orders o ON o.id = r.order_id\n           WHERE o.status = 'shipped')\n    / (SELECT SUM(i.qty)\n       FROM order_items i JOIN orders o ON o.id = i.order_id\n       WHERE o.status = 'shipped'), 4\n);\\\"\\\"\\\",\n'q6': \\\"\\\"\\\"SELECT strftime('%Y-%m', o.order_date) AS month\nFROM orders o JOIN order_items i ON i.order_id = o.id\nWHERE o.status = 'shipped'\nGROUP BY month\nORDER BY SUM(i.qty * i.unit_price) DESC, month\nLIMIT 1;\\\"\\\"\\\",\n'q7': \\\"\\\"\\\"SELECT COUNT(DISTINCT p.id)\nFROM products p\nJOIN order_items i ON i.product_id = p.id\nJOIN orders o ON o.id = i.order_id\nWHERE p.discontinued = 1\n  AND o.status = 'shipped'\n  AND o.order_date >= '2026-07-01';\\\"\\\"\\\",\n'q8': \\\"\\\"\\\"SELECT ROUND(\n    (SELECT SUM(i.qty * i.unit_price)\n     FROM orders o JOIN order_items i ON i.order_id = o.id\n     WHERE o.status = 'shipped')\n    / (SELECT COUNT(*) FROM orders WHERE status = 'shipped'), 2\n);\\\"\\\"\\\",\n}\nwith sqlite3.connect('file:shop.db?mode=ro', uri=True) as con:\n    answers = {key: con.execute(sql).fetchone()[0] for key, sql in queries.items()}\n    # Independently check totals and order averages using per-order aggregation.\n    revenues = [r[0] for r in con.execute(\\\"SELECT COALESCE(SUM(i.qty*i.unit_price),0) FROM orders o LEFT JOIN order_items i ON i.order_id=o.id WHERE o.status='shipped' GROUP BY o.id\\\")]\n    assert round(sum(revenues), 2) == answers['q1']\n    assert round(sum(revenues)/len(revenues), 2) == answers['q8']\nPath('answer.json').write_text(json.dumps(answers, indent=2) + '\\\\n')\nPath('queries.sql').write_text('\\\\n\\\\n'.join('-- ' + key + '\\\\n' + sql for key, sql in queries.items()) + '\\\\n')\nprint(json.dumps(answers, indent=2))\nPY\npython3 /tmp/shop_answers.py\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"cat > /tmp/shop_answers.py <<'PY'\nimport json\nimport sqlite3\nfrom pathlib import Path\n\nqueries = {\n'q1': \\\"\\\"\\\"SELECT ROUND(SUM(i.qty * i.unit_price), 2)\nFROM orders o JOIN order_items i ON i.order_id = o.id\nWHERE o.status = 'shipped';\\\"\\\"\\\",\n'q2': \\\"\\\"\\\"SELECT c.country\nFROM customers c\nJOIN orders o ON o.customer_id = c.id\nJOIN order_items i ON i.order_id = o.id\nWHERE o.status = 'shipped'\nGROUP BY c.country\nORDER BY SUM(i.qty * i.unit_price) DESC, c.country\nLIMIT 1;\\\"\\\"\\\",\n'q3': \\\"\\\"\\\"WITH returned AS (\n    SELECT order_id, product_id, SUM(qty) AS qty\n    FROM returns\n    GROUP BY order_id, product_id\n)\nSELECT p.category\nFROM orders o\nJOIN order_items i ON i.order_id = o.id\nJOIN products p ON p.id = i.product_id\nLEFT JOIN returned r ON r.order_id = i.order_id AND r.product_id = i.product_id\nWHERE o.status = 'shipped'\nGROUP BY p.category\nORDER BY SUM((i.qty - COALESCE(r.qty, 0)) * i.unit_price) DESC, p.category\nLIMIT 1;\\\"\\\"\\\",\n'q4': \\\"\\\"\\\"SELECT COUNT(*)\nFROM customers c\nWHERE NOT EXISTS (\n    SELECT 1 FROM orders o WHERE o.customer_id = c.id AND o.status = 'shipped'\n);\\\"\\\"\\\",\n'q5': \\\"\\\"\\\"SELECT ROUND(\n    1.0 * (SELECT COALESCE(SUM(r.qty), 0)\n           FROM returns r JOIN orders o ON o.id = r.order_id\n           WHERE o.status = 'shipped')\n    / (SELECT SUM(i.qty)\n       FROM order_items i JOIN orders o ON o.id = i.order_id\n       WHERE o.status = 'shipped'), 4\n);\\\"\\\"\\\",\n'q6': \\\"\\\"\\\"SELECT strftime('%Y-%m', o.order_date) AS month\nFROM orders o JOIN order_items i ON i.order_id = o.id\nWHERE o.status = 'shipped'\nGROUP BY month\nORDER BY SUM(i.qty * i.unit_price) DESC, month\nLIMIT 1;\\\"\\\"\\\",\n'q7': \\\"\\\"\\\"SELECT COUNT(DISTINCT p.id)\nFROM products p\nJOIN order_items i ON i.product_id = p.id\nJOIN orders o ON o.id = i.order_id\nWHERE p.discontinued = 1\n  AND o.status = 'shipped'\n  AND o.order_date >= '2026-07-01';\\\"\\\"\\\",\n'q8': \\\"\\\"\\\"SELECT ROUND(\n    (SELECT SUM(i.qty * i.unit_price)\n     FROM orders o JOIN order_items i ON i.order_id = o.id\n     WHERE o.status = 'shipped')\n    / (SELECT COUNT(*) FROM orders WHERE status = 'shipped'), 2\n);\\\"\\\"\\\",\n}\nwith sqlite3.connect('file:shop.db?mode=ro', uri=True) as con:\n    answers = {key: con.execute(sql).fetchone()[0] for key, sql in queries.items()}\n    # Independently check totals and order averages using per-order aggregation.\n    revenues = [r[0] for r in con.execute(\\\"SELECT COALESCE(SUM(i.qty*i.unit_price),0) FROM orders o LEFT JOIN order_items i ON i.order_id=o.id WHERE o.status='shipped' GROUP BY o.id\\\")]\n    assert round(sum(revenues), 2) == answers['q1']\n    assert round(sum(revenues)/len(revenues), 2) == answers['q8']\nPath('answer.json').write_text(json.dumps(answers, indent=2) + '\\\\n')\nPath('queries.sql').write_text('\\\\n\\\\n'.join('-- ' + key + '\\\\n' + sql for key, sql in queries.items()) + '\\\\n')\nprint(json.dumps(answers, indent=2))\nPY\npython3 /tmp/shop_answers.py\"", "aggregated_output": "{\n  \"q1\": 2520896.86,\n  \"q2\": \"DE\",\n  \"q3\": \"input\",\n  \"q4\": 2,\n  \"q5\": 0.0443,\n  \"q6\": \"2026-09\",\n  \"q7\": 3,\n  \"q8\": 994.44\n}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "Created [answer.json](answer.json) and [queries.sql](queries.sql).\n\n```json\n{\n  \"q1\": 2520896.86,\n  \"q2\": \"DE\",\n  \"q3\": \"input\",\n  \"q4\": 2,\n  \"q5\": 0.0443,\n  \"q6\": \"2026-09\",\n  \"q7\": 3,\n  \"q8\": 994.44\n}\n```"}}
{"type": "turn.completed", "usage": {"input_tokens": 81364, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 1407, "reasoning_output_tokens": 0}}

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