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Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Boat fuel range with a reserve over a planned trip

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.06
Wall time
16s
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 (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

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 batch 10 case k11-fuel-range-reserve (offline fixture). Prepared with batch10/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-sonnet-5.5: $0.061194. Score: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.06
Wall time
16s
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on79,8371,946$0.06

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. 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
  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
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   "server_tool_use": {
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  "modelUsage": {
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    "thinkingTokens": 270,
    "canonicalModel": "claude-sonnet-5-5",
    "provider": "firstParty",
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  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
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   "killed": {
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  "safety_stops": 0,
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  "num_turns": 4,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `solve.py` (standard library only). I ran it on the provided `input.json` and the output looks plausible, but I haven't checked it against the hidden checks. I also haven't tried any fresh inputs. Run it with `python3`, because `python` isn't installed here.\n\nOn the provided input the results are:\n- **Reserve:** 60.00 L.\n- **Reserve breach:** after leg 4 (0-based), where 27.96 L remain.\n- **Runs dry:** during leg 5, about 9.31 nm in. Legs 5 and 6 still print negative remaining fuel, as the rules say.\n- **Economy:** the best row is 1400 rpm at 0.6429 nm/L, so the economy range is 124.71 nm.\n\nI read the rules on the two ambiguous points this way:\n- **Unrunnable leg:** if speed over ground is zero or negative, I record that leg with null hours and fuel, keep the current remaining fuel, and stop. If the boat hasn't run dry yet, that leg becomes the dry leg with `dry_after_nm_into_leg` set to 0. I didn't hit this case in the provided input.\n- **Rounding:** every figure is computed unrounded and rounded only when written out.",
  "ttft_ms": 1912,
  "type": "result",
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  "uuid": "[REDACTED]",
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 }
]

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