GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Boat fuel range with a reserve over a planned trip
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
- 1m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Boat fuel range with a reserve over a planned trip `input.json` (synthetic; not for real trip planning): `tank_l`, `fuel_l` on board, `reserve_pct` (of the tank), `burn_table` (rows `rpm`, `speed_kn` through the water, `lph` litres per hour; not sorted), and `legs` (`distance_nm`, `rpm`, `current_kn`: positive = with the boat, negative = against). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh inputs are used too. ## Rules 1. Speed and burn at an rpm: sort the table by rpm, interpolate each linearly, clamp to the first/last row outside the range. 2. Each leg in order: SOG = speed + current_kn. If SOG <= 0 the leg cannot be run: append `{"sog_kn", "hours": null, "fuel_l": null, "remaining_l"}` and stop processing legs (this leg is the dry leg if none yet, with `dry_after_nm_into_leg` 0 if none yet). Otherwise hours = distance / SOG, fuel = lph * hours, remaining -= fuel (remaining may go negative; keep going). 3. `reserve_l` = tank * pct / 100. `reserve_breach_leg` = index (0-based) of the first leg after which remaining < reserve, or null. `runs_dry_leg` = first leg whose fuel exceeds the fuel left at its start, and `dry_after_nm_into_leg` = fuel left at its start / lph * SOG; both null if never. 4. Economy: the table row with the greatest speed_kn / lph (compared at 6 decimals; ties -> lower rpm). `economy_range_nm` = max(0, fuel_l - reserve) * that ratio (still water). Round speeds and hours to 3, litres and nm to 2, `economy_nm_per_l` to 4. ## answer.json `{"reserve_l", "legs": [{"sog_kn", "hours", "fuel_l", "remaining_l"}, ...], "reserve_breach_leg", "runs_dry_leg", "dry_after_nm_into_leg", "economy_rpm", "economy_nm_per_l", "economy_range_nm"}` ## Scoring Hidden checks (partial credit; all must pass): `leg_speeds_and_burn` (first three legs), `remaining_fuel`, `reserve_and_dry`, `economy`, `fresh_run_1`, `fresh_run_2`. Tolerance 0.011 (0.0002 for nm per litre). 60-second limit per run.
Unsorted burn table with clamped interpolation, current added to speed (including a leg where the boat cannot make way), separate reserve-breach and run-dry legs, and the economy-rpm tie rule. 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. leg_speeds_and_burn 2. remaining_fuel 3. reserve_and_dry 4. economy 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch10 case k11-fuel-range-reserve (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. 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
- 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 | 85,569 | 1,824 | $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. leg_speeds_and_burn 2. remaining_fuel 3. reserve_and_dry 4. economy 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": "01a16401-7a9d-71c2-8bba-3686cdca092c"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll write `solve.py` and check interpolation, reserve and dry-fuel tracking, and blocked-leg behavior with synthetic inputs.\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/118_k11/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 \"tank_l\": 300,\n \"fuel_l\": 254,\n \"reserve_pct\": 20,\n \"burn_table\": [\n {\n \"rpm\": 1000,\n \"speed_kn\": 2.83,\n \"lph\": 4.9\n },\n {\n \"rpm\": 1400,\n \"speed_kn\": 3.96,\n \"lph\": 6.16\n },\n {\n \"rpm\": 3000,\n \"speed_kn\": 8.15,\n \"lph\": 19.66\n },\n {\n \"rpm\": 2200,\n \"speed_kn\": 6.22,\n \"lph\": 10.99\n },\n {\n \"rpm\": 2600,\n \"speed_kn\": 7.36,\n \"lph\": 14.79\n },\n {\n \"rpm\": 1800,\n \"speed_kn\": 5.09,\n \"lph\": 8.15\n },\n {\n \"rpm\": 3400,\n \"speed_kn\": 10.15,\n \"lph\": 25.68\n }\n ],\n \"legs\": [\n {\n \"distance_nm\": 17.7,\n \"rpm\": 2100,\n \"current_kn\": -0.9\n },\n {\n \"distance_nm\": 19.0,\n \"rpm\": 3300,\n \"current_kn\": 0.8\n },\n {\n \"distance_nm\": 20.1,\n \"rpm\": 2100,\n \"current_kn\": -1.0\n },\n {\n \"distance_nm\": 20.9,\n \"rpm\": 1800,\n \"current_kn\": -0.9\n },\n {\n \"distance_nm\": 27.4,\n \"rpm\": 2500,\n \"current_kn\": -1.1\n },\n {\n \"distance_nm\": 14.1,\n \"rpm\": 3000,\n \"current_kn\": -1.6\n },\n {\n \"distance_nm\": 23.6,\n \"rpm\": 2500,\n \"current_kn\": 1.1\n }\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/118_k11/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/118_k11/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 && python - <<'PY'\nfrom solve import solve\nbase = dict(tank_l=100, fuel_l=15, reserve_pct=10,\n burn_table=[dict(rpm=2000,speed_kn=10,lph=10),dict(rpm=1000,speed_kn=5,lph=5)],\n legs=[dict(distance_nm=10,rpm=1500,current_kn=2.5),dict(distance_nm=10,rpm=3000,current_kn=0)])\nr=solve(base)\nassert r['legs'][0] == dict(sog_kn=10.0,hours=1.0,fuel_l=7.5,remaining_l=7.5)\nassert r['reserve_breach_leg']==0 and r['runs_dry_leg']==1 and r['dry_after_nm_into_leg']==7.5\nassert r['economy_rpm']==1000 and r['economy_range_nm']==5\nbase['legs']=[dict(distance_nm=10,rpm=500,current_kn=-5),dict(distance_nm=10,rpm=2000,current_kn=0)]\nr=solve(base)\nassert len(r['legs'])==1 and r['legs'][0]['hours'] is None\nassert r['runs_dry_leg']==0 and r['dry_after_nm_into_leg']==0\nbase['legs']=[dict(distance_nm=15,rpm=1000,current_kn=0)]\nr=solve(base)\nassert r['runs_dry_leg'] is None and r['legs'][0]['remaining_l']==0\nprint('Synthetic checks passed')\nPY\"", "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 && python - <<'PY'\nfrom solve import solve\nbase = dict(tank_l=100, fuel_l=15, reserve_pct=10,\n burn_table=[dict(rpm=2000,speed_kn=10,lph=10),dict(rpm=1000,speed_kn=5,lph=5)],\n legs=[dict(distance_nm=10,rpm=1500,current_kn=2.5),dict(distance_nm=10,rpm=3000,current_kn=0)])\nr=solve(base)\nassert r['legs'][0] == dict(sog_kn=10.0,hours=1.0,fuel_l=7.5,remaining_l=7.5)\nassert r['reserve_breach_leg']==0 and r['runs_dry_leg']==1 and r['dry_after_nm_into_leg']==7.5\nassert r['economy_rpm']==1000 and r['economy_range_nm']==5\nbase['legs']=[dict(distance_nm=10,rpm=500,current_kn=-5),dict(distance_nm=10,rpm=2000,current_kn=0)]\nr=solve(base)\nassert len(r['legs'])==1 and r['legs'][0]['hours'] is None\nassert r['runs_dry_leg']==0 and r['dry_after_nm_into_leg']==0\nbase['legs']=[dict(distance_nm=15,rpm=1000,current_kn=0)]\nr=solve(base)\nassert r['runs_dry_leg'] is None and r['legs'][0]['remaining_l']==0\nprint('Synthetic checks passed')\nPY\"", "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'\nfrom solve import solve\nbase = dict(tank_l=100, fuel_l=15, reserve_pct=10, burn_table=[dict(rpm=2000,speed_kn=10,lph=10),dict(rpm=1000,speed_kn=5,lph=5)], legs=[dict(distance_nm=10,rpm=1500,current_kn=2.5),dict(distance_nm=10,rpm=3000,current_kn=0)])\nr=solve(base)\nassert r['legs'][0] == dict(sog_kn=10.0,hours=1.0,fuel_l=7.5,remaining_l=7.5)\nassert r['reserve_breach_leg']==0 and r['runs_dry_leg']==1 and r['dry_after_nm_into_leg']==7.5\nassert r['economy_rpm']==1000 and r['economy_range_nm']==5\nbase['legs']=[dict(distance_nm=10,rpm=500,current_kn=-5),dict(distance_nm=10,rpm=2000,current_kn=0)]\nr=solve(base)\nassert len(r['legs'])==1 and r['legs'][0]['hours'] is None\nassert r['runs_dry_leg']==0 and r['dry_after_nm_into_leg']==0\nbase['legs']=[dict(distance_nm=15,rpm=1000,current_kn=0)]\nr=solve(base)\nassert r['runs_dry_leg'] is None and r['legs'][0]['remaining_l']==0\nprint('Synthetic checks passed')\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'\nfrom solve import solve\nbase = dict(tank_l=100, fuel_l=15, reserve_pct=10, burn_table=[dict(rpm=2000,speed_kn=10,lph=10),dict(rpm=1000,speed_kn=5,lph=5)], legs=[dict(distance_nm=10,rpm=1500,current_kn=2.5),dict(distance_nm=10,rpm=3000,current_kn=0)])\nr=solve(base)\nassert r['legs'][0] == dict(sog_kn=10.0,hours=1.0,fuel_l=7.5,remaining_l=7.5)\nassert r['reserve_breach_leg']==0 and r['runs_dry_leg']==1 and r['dry_after_nm_into_leg']==7.5\nassert r['economy_rpm']==1000 and r['economy_range_nm']==5\nbase['legs']=[dict(distance_nm=10,rpm=500,current_kn=-5),dict(distance_nm=10,rpm=2000,current_kn=0)]\nr=solve(base)\nassert len(r['legs'])==1 and r['legs'][0]['hours'] is None\nassert r['runs_dry_leg']==0 and r['dry_after_nm_into_leg']==0\nbase['legs']=[dict(distance_nm=15,rpm=1000,current_kn=0)]\nr=solve(base)\nassert r['runs_dry_leg'] is None and r['legs'][0]['remaining_l']==0\nprint('Synthetic checks passed')\nPY\"", "aggregated_output": "Synthetic checks passed\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "Created [solve.py](solve.py) and generated [answer.json](answer.json). Synthetic checks passed for interpolation, clamping, reserve breaches, dry-fuel tracking, blocked legs, and economy ties.\n\nThis environment uses `python3`:\n\n```bash\npython3 solve.py input.json answer.json\n```"}}
{"type": "turn.completed", "usage": {"input_tokens": 85569, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 1824, "reasoning_output_tokens": 8}}