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Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Great-circle vs rhumb-line passage plan with ETAs

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
$0.08
Wall time
29s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Great-circle vs rhumb-line passage plan with ETAs `input.json` (synthetic; not for navigation): `waypoints` ([lat, lon] decimal degrees, legs may cross the 180° meridian), `speed_kn`, `depart_utc` ("YYYY-MM-DDTHH:MM:SSZ"). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `route.svg`. Fresh inputs are used too. ## Rules (sphere, R = 3440.065 nm) - Longitude difference dlon is always wrapped into [-180°, 180°). - **Great circle**: haversine distance; initial course = atan2(sin dlon * cos lat2, cos lat1 * sin lat2 - sin lat1 * cos lat2 * cos dlon) mod 360. - **Rhumb line**: dpsi = ln(tan(pi/4 + lat2/2) / tan(pi/4 + lat1/2)); q = dlat / dpsi, or cos(lat1) when |dpsi| <= 1e-12; distance = sqrt(dlat^2 + q^2 * dlon^2) * R (radians); course = atan2(dlon, dpsi) mod 360. - The boat sails the **rhumb** legs at `speed_kn`; leg ETA = departure + cumulative rhumb distance / speed, rounded to the nearest minute (half a minute rounds up), formatted "YYYY-MM-DDTHH:MMZ". - `rhumb_extra_pct` = (total rhumb - total GC) / total GC * 100. Round distances to 2 decimals, courses to 1, the percentage to 3. ## Visual output: route.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="route-gc">`, `<polyline id="route-rhumb">` and an element `id="waypoints"`. The drawing (projection etc.) is not graded. ## answer.json `{"legs": [{"gc_nm", "gc_initial_course", "rhumb_nm", "rhumb_course", "eta_utc"}, ...], "total_gc_nm", "total_rhumb_nm", "rhumb_extra_pct", "arrival_utc"}` ## Scoring Hidden checks (partial credit; all must pass): `great_circle_legs`, `rhumb_legs` (first four legs; distances within 0.011, courses within 0.11), `totals`, `etas` (exact strings), `route_svg_static` (route.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2` (all keys, within 0.02). 60-second limit per run.

Antimeridian wrap on both methods, the due east/west rhumb leg where dpsi is zero, initial great-circle course, and minute-rounded UTC ETAs that drift if per-leg rounding is used. 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. great_circle_legs 2. rhumb_legs 3. totals 4. etas 5. route_svg_static 6. fresh_run_1 7. fresh_run_2

Setup notes: Use Case Scout batch 10 case k09-passage-gc-vs-rhumb (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. Visual output (route.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Claude Code's own reported cost for claude-sonnet-5.5: $0.08112. Score: 7/7. 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.

Output

Metrics

Estimated cost
$0.08
Wall time
29s
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on82,1113,497$0.08

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. great_circle_legs 2. rhumb_legs 3. totals 4. etas 5. route_svg_static 6. fresh_run_1 7. 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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  "result": "`solve.py` is written and runs cleanly on `input.json` (`python3 solve.py input.json answer.json`). I haven't checked the output against any reference values, so none of the hidden checks are confirmed.\n\nOn the sample input:\n- **Totals:** great-circle 2105.17 nm, rhumb 2105.77 nm, so the rhumb route is 0.028% longer.\n- **Arrival:** 2026-12-01T04:11Z.\n\nHow it handles the rules:\n- **Date line:** longitude differences are wrapped into [-180\u00b0, 180\u00b0) for every leg, so the crossing from 178.38\u00b0 to -167.09\u00b0 is handled.\n- **ETAs:** each ETA comes from the cumulative unrounded rhumb distance divided by speed, rounded to the nearest minute with half a minute rounding up.\n- **Rounding:** distances to 2 decimals, courses to 1, the percentage to 3.\n- **`route.svg`:**\n  - It is a static SVG with `route-gc`, `route-rhumb` and `waypoints`.\n  - The great-circle path is drawn as interpolated points.\n  - Longitudes are unwrapped so the line doesn't jump across the map at the date line.\n  - It is written to the current directory, not next to `answer.json`.\n\nPython is only available as `python3` here; plain `python` isn't installed. If the grader calls `python`, that name needs to exist there.",
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