Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Best sailing angle and leg times from a polar table
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.0080
- Wall time
- 30s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Best sailing angle from a polar table `input.json` (synthetic) has a boat polar (`polar.tws` wind speeds in knots, `polar.twa` true wind angles in degrees ascending, `polar.speed[j][k]` = boat speed at `twa[j]`, `tws[k]`), the current true wind speed `tws`, and a list of `legs` each with `bearing` (degrees true to the mark), `distance_nm` and `twd` (true wind direction, degrees the wind blows FROM). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `polar.svg`. Fresh inputs are used too, so no hard-coding. ## Rules 1. Boat speed at (tws, a): for each table row, interpolate linearly in tws (clamped to the first/last column outside the range); then interpolate linearly in twa between rows (clamped to 180 at the end). For a < `twa[0]` (no-go zone) speed is 0. 2. `upwind`: over integer a in 0..90, maximise VMG = speed*cos(a); `downwind`: over integer a in 90..180, maximise -speed*cos(a). Compare VMG rounded to 4 decimals; on a tie take the smaller angle. Report `{"twa": a, "vmg": value rounded to 3}`. 3. Each leg: over integer courses c = 0..359, a = smallest angle between c and `twd` (0..180), s = speed(tws, a), VMC = s*cos(c - bearing). Take the c with the greatest VMC compared at 6 decimals; ties keep the smaller c. Report `course`, `twa` (a), `boat_speed` (3 dp), `vmc` (3 dp), `hours` = distance / VMC (3 dp) or null if VMC <= 0. 4. `total_hours` = sum of non-null leg hours (3 dp). ## Visual output: polar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links, fonts or other external assets; renders headless), containing an element `id="polar-curve"` (the polar curve at the given tws) and `<line id="best-upwind">` and `<line id="best-downwind">` drawn from the origin along the best angles. The drawing itself is not graded. ## answer.json `{"upwind": {"twa", "vmg"}, "downwind": {"twa", "vmg"}, "legs": [{"course", "twa", "boat_speed", "vmc", "hours"}, ...], "total_hours"}` ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `best_upwind_downwind`, `upwind_and_downwind_legs` (legs 0-1), `reaching_legs_and_total` (legs 2-3 and total), `polar_svg_static` (polar.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Numbers within 0.002; integers exact. 60-second limit per run.
Bilinear polar lookup with clamping and a no-go zone, integer angle searches with stated rounding and tie rules, and course-vs-mark VMC that forces tacking or gybing angles on beats and runs. 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. best_upwind_downwind 2. upwind_and_downwind_legs 3. reaching_legs_and_total 4. polar_svg_static 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch 10 case k02-polar-vmg-optimizer (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 (polar.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: $0.007969. 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.
Output
Metrics
- Estimated cost
- $0.0080
- Wall time
- 30s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 150,325 | 5,948 | $0.0080 |
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. best_upwind_downwind 2. upwind_and_downwind_legs 3. reaching_legs_and_total 4. polar_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.
Was this useful?
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
[
{
"duration_api_ms": 28902,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.00796901,
"usage": {
"input_tokens": 12,
"cache_creation_input_tokens": 18372,
"cache_read_input_tokens": 131941,
"output_tokens": 5948,
"output_tokens_details": {
"thinking_tokens": 2370
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 18372,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 652,
"cache_read_input_tokens": 29563,
"cache_creation_input_tokens": 1348,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 1348
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 12,
"outputTokens": 5948,
"cacheReadInputTokens": 131941,
"cacheCreationInputTokens": 18372,
"webSearchRequests": 0,
"costUSD": 0.00796901,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 2370,
"canonicalModel": "claude-haiku-5-5",
"provider": "firstParty",
"costBasis": "list"
}
},
"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,
"spawned_by_subagents": 0,
"completed": 0,
"failed": 0,
"killed": {
"parent": 0,
"user": 0,
"system": 0
},
"refused": {
"depth_limit": 0,
"concurrency_limit": 0,
"budget": 0
},
"by_type": {}
},
"safety_stops": 0,
"is_error": false,
"num_turns": 6,
"subtype": "success",
"api_error_status": null,
"result": "`solve.py` is written and runs cleanly on the provided input. It writes `answer.json` and `polar.svg` to the same directory as the answer path.\n\n**Results for `input.json`:**\n- Upwind: TWA 39\u00b0, VMG 5.085\n- Downwind: TWA 165\u00b0, VMG 7.385\n- Legs: 0 \u2192 course 49, TWA 39, 1.629 h; 1 \u2192 course 61, TWA 165, 1.106 h; 2 \u2192 course 30, TWA 91, 0.665 h; 3 \u2192 course 265, TWA 136, 0.457 h\n- Total: 3.857 h\n\nI checked leg 0 by hand against the rules (speed 6.543 at TWA 39, VMG 5.085), and it matches the script.\n\n**Choices I made where the spec was open:**\n- `polar.svg` goes next to `answer.json`, not necessarily the current directory. The two are the same in the normal run.\n- The `polar-curve` element is a `<polyline>` of the speed at every integer TWA from 0 to 180, plotted in polar form with the bow pointing up.\n- The best-angle lines are drawn with length equal to the boat speed at that angle, and are coloured red (upwind) and green (downwind).\n- The existing `polar.svg` in the work directory was a stale sample, and it has been overwritten by the new output.\n\n**Checks run:** the SVG parses as XML and contains the `polar-curve`, `best-upwind` and `best-downwind` ids. A modified input with TWS 25 (above the table, so clamped) and a leg whose best VMC is negative ran without errors. I did not run the hidden checks, so the fresh-input and scoring results are unverified.",
"ttft_ms": 1400,
"type": "result",
"duration_ms": 29383,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1268,
"time_to_request_ms": 232,
"first_content_frame_ms": 1268,
"queued_turn_count": 0,
"result_index": 0
}
]