GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Tide height by rule of twelfths and cosine, with under-keel clearance windows
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.05
- Wall time
- 1m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Tide height by rule of twelfths and cosine, and under-keel clearance `input.json` (synthetic, not for navigation) gives two consecutive tide events `event1` and `event2` (`time_min` minutes, `height_m`; either rising or falling), `chart_depth_m`, the boat's `draft_m`, required `min_ukc_m`, `need_minutes`, and `queries` (minutes). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `tide.svg`. Fresh inputs are used too. ## Rules Let T = event2 time - event1 time and f = (t - event1 time) / T clamped to [0, 1]. Height = h1 + (h2 - h1) * F(f). - **Twelfths**: split [0, 1] into 6 equal parts; cumulative fractions at the part boundaries are 0, 1, 3, 6, 9, 11, 12 twelfths; interpolate linearly inside a part. - **Cosine**: F(f) = (1 - cos(pi * f)) / 2. - Under-keel clearance UKC = chart_depth + height(twelfths) - draft. A query is `safe` if UKC >= min_ukc. `required_height_m` = draft + min_ukc - chart_depth. - Safe window (per method): scan whole minutes t from event1 to event2 inclusive; a run is consecutive minutes with height >= required height. Report the first run whose length end - start (minutes) is >= `need_minutes` as `{"start", "end"}` (first and last safe minute), or null. - `max_method_diff_m`: the largest |twelfths - cosine| over those whole minutes. Use a 1e-9 tolerance on the >= comparisons. Round heights and UKC to 3 decimals. ## Visual output: tide.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="tide-twelfths">`, `<polyline id="tide-cosine">` and `<line id="min-depth-line">` at the required height. The drawing itself is not graded. ## answer.json `{"required_height_m", "queries": [{"t", "twelfths", "cosine", "ukc", "safe"}, ...], "window_twelfths", "window_cosine", "max_method_diff_m"}` ## Scoring Hidden checks (partial credit; all must pass): `query_heights`, `safe_windows` (exact), `method_difference`, `tide_svg_static` (tide.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Numbers within 0.0015. 60-second limit per run.
Piecewise twelfths interpolation vs the cosine curve on rising and falling tides, a clamped fraction, a minute-by-minute run search with a length rule, and exact window endpoints. 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).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. query_heights 2. safe_windows 3. method_difference 4. tide_svg_static 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch10 case k05-tide-underkeel-clearance (offline fixture). Prepared with batch10/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. Visual output (tide.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. 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.
Output
Metrics
- Estimated cost
- $0.05
- Wall time
- 1m
- 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 | 84,528 | 2,207 | $0.05 |
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. query_heights 2. safe_windows 3. method_difference 4. tide_svg_static 5. fresh_run_1 6. fresh_run_2
- 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": "01a163fb-8dbc-7970-bc84-c11dd4a02fb1"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the input, implement the tide calculations and SVG output, then check rising and falling cases."}}
{"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/112_k05/work\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "{\n \"event1\": {\n \"time_min\": 597,\n \"height_m\": 0.96\n },\n \"event2\": {\n \"time_min\": 937,\n \"height_m\": 2.91\n },\n \"chart_depth_m\": 1.2,\n \"draft_m\": 2.14,\n \"min_ukc_m\": 0.6,\n \"need_minutes\": 120,\n \"queries\": [\n 597,\n 628,\n 648,\n 653,\n 656,\n 767,\n 867,\n 880,\n 902,\n 937\n ]\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/112_k05/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/112_k05/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json'", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && python3 - <<'PY'\nimport json\nimport xml.etree.ElementTree as ET\nfrom solve import solve\nbase=dict(event1=dict(time_min=0,height_m=0),event2=dict(time_min=360,height_m=12),chart_depth_m=0,draft_m=6,min_ukc_m=0,need_minutes=120,queries=[-1,0,60,120,180,240,300,360,400])\na,_=solve(base)\nassert [q['twelfths'] for q in a['queries']]==[0,0,1,3,6,9,11,12,12]\nassert a['window_twelfths']==a['window_cosine']==dict(start=180,end=360)\nbase['event1']['height_m'],base['event2']['height_m']=12,0\na,_=solve(base)\nassert a['window_twelfths']==a['window_cosine']==dict(start=0,end=180)\nbase['need_minutes']=181\nassert solve(base)[0]['window_twelfths'] is None\nroot=ET.parse('tide.svg').getroot()\nns='{http://www.w3.org/2000/svg}'\nfor tag,ident in [('polyline','tide-twelfths'),('polyline','tide-cosine'),('line','min-depth-line')]:\n assert root.find(f\\\"{ns}{tag}[@id='{ident}']\\\") is not None\nanswer=json.load(open('answer.json'))\nprint('Checks passed; sample windows:',answer['window_twelfths'],answer['window_cosine'])\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && python3 - <<'PY'\nimport json\nimport xml.etree.ElementTree as ET\nfrom solve import solve\nbase=dict(event1=dict(time_min=0,height_m=0),event2=dict(time_min=360,height_m=12),chart_depth_m=0,draft_m=6,min_ukc_m=0,need_minutes=120,queries=[-1,0,60,120,180,240,300,360,400])\na,_=solve(base)\nassert [q['twelfths'] for q in a['queries']]==[0,0,1,3,6,9,11,12,12]\nassert a['window_twelfths']==a['window_cosine']==dict(start=180,end=360)\nbase['event1']['height_m'],base['event2']['height_m']=12,0\na,_=solve(base)\nassert a['window_twelfths']==a['window_cosine']==dict(start=0,end=180)\nbase['need_minutes']=181\nassert solve(base)[0]['window_twelfths'] is None\nroot=ET.parse('tide.svg').getroot()\nns='{http://www.w3.org/2000/svg}'\nfor tag,ident in [('polyline','tide-twelfths'),('polyline','tide-cosine'),('line','min-depth-line')]:\n assert root.find(f\\\"{ns}{tag}[@id='{ident}']\\\") is not None\nanswer=json.load(open('answer.json'))\nprint('Checks passed; sample windows:',answer['window_twelfths'],answer['window_cosine'])\nPY\"", "aggregated_output": "Checks passed; sample windows: {'start': 722, 'end': 937} {'start': 722, 'end': 937}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "Created [solve.py](solve.py), [answer.json](answer.json), and [tide.svg](tide.svg). Verified rising/falling tides, clamping, safe windows, and required SVG elements.\n\nRun with `python3 solve.py input.json answer.json` (`python` isn\u2019t installed here)."}}
{"type": "turn.completed", "usage": {"input_tokens": 84528, "cached_input_tokens": 74368, "cache_write_input_tokens": 0, "output_tokens": 2207, "reasoning_output_tokens": 38}}