GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Berth occupancy point and range-maximum queries at scale
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.04
- Wall time
- 53s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Marina berth occupancy queries at scale `input.json` (synthetic) has `bookings` ([start, end) integer minute pairs), `points` (integer times) and `ranges` ([l, r) pairs). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. One hidden input has **150,000 bookings and about 300,000 queries and must finish within 20 seconds** (other runs: 30 s). Answers must be exact. ## Rules 1. A booking with start > end is invalid: count it in `invalid_bookings` and ignore it. A booking with start == end is ignored (not invalid). Duplicate bookings count separately. 2. Occupancy at integer time t = number of valid bookings with start <= t < end. 3. `points`: occupancy at each time, in input order. 4. `ranges`: for each [l, r): if l >= r the answer is null; otherwise the maximum occupancy over all integer t with l <= t < r. In input order. 5. `peak` = maximum occupancy over all times (0 if there are no valid bookings); `peak_first_time` = the earliest time it is reached (null if peak is 0). ## answer.json `{"invalid_bookings", "peak", "peak_first_time", "points": [...], "ranges": [...]}` ## Scoring Hidden checks (partial credit; all must pass; all exact): `invalid_and_peak`, `point_queries`, `range_max_queries` on the given input; `fresh_run_1`, `fresh_run_2`; `big_150k_bookings_300k_queries_20s`.
The rules are simple and a brute-force answer is correct on small inputs, so separation comes from choosing a sweep plus range-maximum structure and getting the half-open boundaries right. 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. invalid_and_peak 2. point_queries 3. range_max_queries 4. fresh_run_1 5. fresh_run_2 6. big_150k_bookings_300k_queries_20s
Setup notes: Use Case Scout batch12 case m08-berth-occupancy-queries (offline fixture). Prepared with batch12/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: 6/6. 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
- 53s
- 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 on | 82,489 | 1,620 | $0.04 |
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 on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. invalid_and_peak 2. point_queries 3. range_max_queries 4. fresh_run_1 5. fresh_run_2 6. big_150k_bookings_300k_queries_20s
- 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).
- 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": "01a1640a-cccc-7b11-826e-e10c45fff033"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll implement a sweep-line solution with indexed range maxima, then check it against brute force and a large synthetic input.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py'\"", "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 'input.json' -g 'solve.py'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/127_m08/work\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\n#\"'!/usr/bin/env python3\n\"\"\"Answer half-open occupancy queries using a sweep and a range-max tree.\"\"\"\nimport bisect\nimport json\nimport sys\n\n\ndef solve(data):\n events = {}\n invalid = 0\n for start, end in data['\"'bookings']:\n if start > end:\n invalid += 1\n elif start < end:\n events[start] = events.get(start, 0) + 1\n events[end] = events.get(end, 0) - 1\n\n times = sorted(events)\n n = len(times)\n # Leaves represent occupancy on [times[i], times[i + 1]). The final\n # leaf has occupancy zero and extends indefinitely to the right.\n tree = [0] * (2 * n)\n current = peak = 0\n first = None\n for i, time in enumerate(times):\n current += events[time]\n tree[n + i] = current\n if current > peak:\n peak = current\n first = time\n del events\n for i in range(n - 1, 0, -1):\n tree[i] = max(tree[2 * i], tree[2 * i + 1])\n\n right = bisect.bisect_right\n left = bisect.bisect_left\n points = []\n for time in data['points']:\n i = right(times, time) - 1\n points.append(tree[n + i] if i >= 0 else 0)\n\n ranges = []\n for start, end in data['ranges']:\n if start >= end:\n ranges.append(None)\n continue\n # Include the segment containing start, plus every change before end.\n lo = max(0, right(times, start) - 1) + n\n hi = left(times, end) + n\n best = 0\n while lo < hi:\n if lo & 1:\n best = max(best, tree[lo])\n lo += 1\n if hi & 1:\n hi -= 1\n best = max(best, tree[hi])\n lo >>= 1\n hi >>= 1\n ranges.append(best)\n\n return {'invalid_bookings': invalid, 'peak': peak,\n 'peak_first_time': first, 'points': points, 'ranges': ranges}\n\n\ndef main():\n if len(sys.argv) \"'!= 3:\n raise SystemExit('\"'Usage: python solve.py input.json answer.json')\n with open(sys.argv[1], encoding='utf-8') as source:\n data = json.load(source)\n answer = solve(data)\n with open(sys.argv[2], 'w', encoding='utf-8') as destination:\n json.dump(answer, destination, separators=(',', ':'))\n destination.write('\\\\n')\n\n\nif __name__ == '__main__':\n main()\nPY\npython solve.py input.json answer.json\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\n#\"'!/usr/bin/env python3\n\"\"\"Answer half-open occupancy queries using a sweep and a range-max tree.\"\"\"\nimport bisect\nimport json\nimport sys\n\n\ndef solve(data):\n events = {}\n invalid = 0\n for start, end in data['\"'bookings']:\n if start > end:\n invalid += 1\n elif start < end:\n events[start] = events.get(start, 0) + 1\n events[end] = events.get(end, 0) - 1\n\n times = sorted(events)\n n = len(times)\n # Leaves represent occupancy on [times[i], times[i + 1]). The final\n # leaf has occupancy zero and extends indefinitely to the right.\n tree = [0] * (2 * n)\n current = peak = 0\n first = None\n for i, time in enumerate(times):\n current += events[time]\n tree[n + i] = current\n if current > peak:\n peak = current\n first = time\n del events\n for i in range(n - 1, 0, -1):\n tree[i] = max(tree[2 * i], tree[2 * i + 1])\n\n right = bisect.bisect_right\n left = bisect.bisect_left\n points = []\n for time in data['points']:\n i = right(times, time) - 1\n points.append(tree[n + i] if i >= 0 else 0)\n\n ranges = []\n for start, end in data['ranges']:\n if start >= end:\n ranges.append(None)\n continue\n # Include the segment containing start, plus every change before end.\n lo = max(0, right(times, start) - 1) + n\n hi = left(times, end) + n\n best = 0\n while lo < hi:\n if lo & 1:\n best = max(best, tree[lo])\n lo += 1\n if hi & 1:\n hi -= 1\n best = max(best, tree[hi])\n lo >>= 1\n hi >>= 1\n ranges.append(best)\n\n return {'invalid_bookings': invalid, 'peak': peak,\n 'peak_first_time': first, 'points': points, 'ranges': ranges}\n\n\ndef main():\n if len(sys.argv) \"'!= 3:\n raise SystemExit('\"'Usage: python solve.py input.json answer.json')\n with open(sys.argv[1], encoding='utf-8') as source:\n data = json.load(source)\n answer = solve(data)\n with open(sys.argv[2], 'w', encoding='utf-8') as destination:\n json.dump(answer, destination, separators=(',', ':'))\n destination.write('\\\\n')\n\n\nif __name__ == '__main__':\n main()\nPY\npython solve.py input.json answer.json\"", "aggregated_output": "/bin/bash: line 81: 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 random\nfrom solve import solve\nrng = random.Random(725)\nfor case in range(3000):\n bookings = [[rng.randrange(-15, 16), rng.randrange(-15, 16)] for _ in range(rng.randrange(35))]\n points = list(range(-18, 19))\n ranges = [[rng.randrange(-18, 19), rng.randrange(-18, 19)] for _ in range(50)]\n valid = [(s,e) for s,e in bookings if s < e]\n occupancy = lambda t: sum(s <= t < e for s,e in valid)\n peak = max(map(occupancy, points))\n expected = dict(invalid_bookings=sum(s > e for s,e in bookings), peak=peak,\n peak_first_time=next((t for t in points if occupancy(t)==peak), None) if peak else None,\n points=list(map(occupancy, points)),\n ranges=[max(occupancy(t) for t in range(l,r)) if l < r else None for l,r in ranges])\n assert solve(dict(bookings=bookings, points=points, ranges=ranges)) == expected, case\nprint('3,000 randomized brute-force comparisons passed')\nPY\npython3 solve.py input.json answer.json\"", "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 random\nfrom solve import solve\nrng = random.Random(725)\nfor case in range(3000):\n bookings = [[rng.randrange(-15, 16), rng.randrange(-15, 16)] for _ in range(rng.randrange(35))]\n points = list(range(-18, 19))\n ranges = [[rng.randrange(-18, 19), rng.randrange(-18, 19)] for _ in range(50)]\n valid = [(s,e) for s,e in bookings if s < e]\n occupancy = lambda t: sum(s <= t < e for s,e in valid)\n peak = max(map(occupancy, points))\n expected = dict(invalid_bookings=sum(s > e for s,e in bookings), peak=peak,\n peak_first_time=next((t for t in points if occupancy(t)==peak), None) if peak else None,\n points=list(map(occupancy, points)),\n ranges=[max(occupancy(t) for t in range(l,r)) if l < r else None for l,r in ranges])\n assert solve(dict(bookings=bookings, points=points, ranges=ranges)) == expected, case\nprint('3,000 randomized brute-force comparisons passed')\nPY\npython3 solve.py input.json answer.json\"", "aggregated_output": "3,000 randomized brute-force comparisons passed\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport json, random, subprocess, time\nrng = random.Random(121)\nn = 150000\ndata = {'bookings': [[i*100, i*100+rng.randrange(1, 10000000)] for i in range(n)],\n 'points': [rng.randrange(-100, 25000000) for _ in range(150000)],\n 'ranges': [sorted([rng.randrange(-100, 25000000), rng.randrange(-100, 25000000)]) for _ in range(150000)]}\nwith open('/tmp/marina_big_input.json','w') as f: json.dump(data,f)\nstart = time.perf_counter()\nsubprocess.run(['python3','solve.py','/tmp/marina_big_input.json','/tmp/marina_big_answer.json'],check=True)\nprint('150,000 bookings, 300,000 queries: %.2f seconds including JSON I/O' % (time.perf_counter()-start))\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 json, random, subprocess, time\nrng = random.Random(121)\nn = 150000\ndata = {'bookings': [[i*100, i*100+rng.randrange(1, 10000000)] for i in range(n)],\n 'points': [rng.randrange(-100, 25000000) for _ in range(150000)],\n 'ranges': [sorted([rng.randrange(-100, 25000000), rng.randrange(-100, 25000000)]) for _ in range(150000)]}\nwith open('/tmp/marina_big_input.json','w') as f: json.dump(data,f)\nstart = time.perf_counter()\nsubprocess.run(['python3','solve.py','/tmp/marina_big_input.json','/tmp/marina_big_answer.json'],check=True)\nprint('150,000 bookings, 300,000 queries: %.2f seconds including JSON I/O' % (time.perf_counter()-start))\nPY\"", "aggregated_output": "150,000 bookings, 300,000 queries: 1.31 seconds including JSON I/O\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "agent_message", "text": "Created [solve.py](solve.py) and generated [answer.json](answer.json).\n\nVerified 3,000 randomized cases against brute force. A benchmark with 150,000 bookings and 300,000 queries finished in 1.31 seconds, including JSON I/O.\n\nRun with `python3 solve.py input.json answer.json` (`python` is unavailable here)."}}
{"type": "turn.completed", "usage": {"input_tokens": 82489, "cached_input_tokens": 72064, "cache_write_input_tokens": 0, "output_tokens": 1620, "reasoning_output_tokens": 170}}