Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Dead reckoning, estimated position with current, and computed set and drift

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

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.0074
Wall time: 29s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Dead reckoning, estimated position with current, and computed set and drift `input.json` (synthetic, not for navigation): `start` [lat, lon] in decimal degrees, `legs` (each `course` degrees true, `speed_kn` through the water, `minutes`), a forecast `current` (`set` = direction it flows TOWARD, degrees true; `drift_kn`), and a GPS `fix` [lat, lon] at the end of the last leg. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `track.svg`. Fresh inputs are used too. ## Rules (plane sailing, 1 minute of latitude = 1 nm) 1. A move of n nm north and e nm east from (lat, lon): dlat = n / 60; mid = lat + dlat / 2; new lat = lat + dlat; new lon = lon + e / (60 * cos(mid)). 2. **DR**: per leg, distance = speed * hours along the course. **EP**: per leg, add the current vector (drift * hours toward set) to the leg's water vector, then move once. 3. Difference from point A to point B: north = (latB - latA) * 60, east = (lonB - lonA) * 60 * cos((latA + latB) / 2). 4. **Set/drift**: vector from the final DR position to the fix; set = atan2(east, north) in degrees mod 360; drift = length / total hours. `ep_error_nm` = distance from the final EP to the fix. **CMG/SMG**: same from `start` to the fix. 5. Round positions to 5 decimals, hours to 4, angles to 1, speeds and distances to 3. `dr` and `ep` list the start and the position after every leg. ## Visual output: track.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="dr-track">`, `<polyline id="ep-track">` and `<circle id="fix">`. The drawing itself is not graded. ## answer.json `{"dr": [[lat, lon], ...], "ep": [...], "hours", "set_deg", "drift_kn", "ep_error_nm", "cmg_deg", "smg_kn"}` ## Scoring Hidden checks (partial credit; all must pass): `dr_positions`, `ep_positions`, `set_and_drift`, `course_speed_made_good`, `track_svg_static` (track.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Positions within 2e-5 degrees, angles within 0.11, speeds and distances within 0.002. 60-second limit per run.

Mid-latitude longitude scaling per leg, current applied per leg as a vector, 'set' as the direction the current flows toward, angle wrap, and positions checked at five decimals at high latitudes. 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. dr_positions 2. ep_positions 3. set_and_drift 4. course_speed_made_good 5. track_svg_static 6. fresh_run_1 7. fresh_run_2

Setup notes: Use Case Scout batch 10 case k06-dead-reckoning-set-drift (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 (track.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-haiku-5.5: $0.007377. 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

Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Dead reckoning, estimated position with current, and computed set and driftSuccess

Metrics

Estimated cost: $0.0074
Wall time: 29s
Human step-ins: 0
Turns: 7

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	165,381	5,775	$0.0074

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. dr_positions 2. ep_positions 3. set_and_drift 4. course_speed_made_good 5. track_svg_static 6. fresh_run_1 7. 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

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    "canonicalModel": "claude-haiku-5-5",
    "provider": "firstParty",
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  "permission_denials": [],
  "terminal_reason": "completed",
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  "subagent_stats": {
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  "result": "I wrote `solve.py` in the work directory. It runs with `python solve.py input.json answer.json`, writes `answer.json` and `track.svg`, and uses only the standard library. I ran it on the sample input and it finished at once. I checked the first DR leg by hand and it matches. I did not have the hidden checker, so the scoring tolerances are unverified.\n\nResults for the sample input:\n- `hours`: 5.25\n- `set_deg`: 248.8, `drift_kn`: 0.889\n- `ep_error_nm`: 1.42\n- `cmg_deg`: 117.3, `smg_kn`: 1.84\n\nChoices I made where the spec left room:\n- **Full precision internally.** Each leg is computed from unrounded values and rounding happens only on output, so errors don't accumulate from rounded intermediates.\n- **Current applied per leg.** Each leg's EP adds `drift \u00d7 hours` toward `set` to that leg's water vector, then moves once from the previous EP.\n- **`track.svg` location.** It is written into the same directory as `answer.json`. If the grader expects it in the current directory, that differs only when the output path has a directory component.\n- **SVG.** It is an 800\u00d7800 static file with a white background, a north-up projection scaled by cos(mid-latitude), and the three required ids. I confirmed it parses as XML.\n\nI also generated `answer.json` and `track.svg` in the work directory from `input.json`. Nothing was committed, since this is not a git repository.",
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