Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Sailboat instrument dashboard from an NMEA 0183 log
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.01
- Wall time
- 1m
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Sailboat instrument dashboard from an NMEA 0183 log `log.nmea` is a **synthetic** instrument log from a sailboat; `input.json` names it and gives alarm thresholds. Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` parses the log, computes true wind and VMG, finds alarm periods, writes `answer.json`, **and** writes a static display `dashboard.html` in the working directory. We re-run it on fresh logs, so do not hard-code anything. ## Log format Each line is `<t>,<sentence>` where `<t>` is an integer second and `<sentence>` is an NMEA 0183 sentence `$<body>*<hh>`. The checksum hh is two hex digits (either case) equal to the XOR of all characters of `<body>` (between `$` and `*`). ## Rules 1. Blank lines are ignored and not counted. A line is **malformed** if it has no comma before the sentence, `<t>` is not an integer, the sentence does not start with `$`, has no `*`, the checksum is not exactly 2 hex digits, or (after the checksum passes) the sentence type is not one of those below or a needed field is missing or not a number. A line whose checksum does not match is **bad_checksum**. Every other line is **valid** (this includes MWV lines that are skipped by rule 3). 2. The sentence type is the last 3 characters of the first field. Fields are split on commas (field 0 is the talker+type): - `HDT`: field 1 = true heading (degrees). - `VHW`: field 5 = boat speed through water (knots). - `DBT`: field 3 = depth (metres). - `VTG`: field 5 = speed over ground (knots). - `MWV`: field 1 = apparent wind angle (degrees clockwise from the bow, 0-360), field 2 = reference, field 3 = apparent wind speed (knots), field 5 = status. 3. Keep the latest heading and boat speed. For an MWV with reference `R` and status `A`, if a heading and a boat speed have already been seen, make a **true wind sample** at its time: x = AWS*cos(AWA) - BSP, y = AWS*sin(AWA); TWS = hypot(x, y); TWA = atan2(y, x) in degrees mod 360; TWD = (heading + TWA) mod 360; VMG = BSP*cos(TWA). MWV lines with another reference or status are valid but make no sample. 4. Alarms: depth alarm on DBT readings with depth < `depth_min`; wind alarm on true wind samples with TWS > `tws_max`; speed alarm on VTG readings with SOG > `sog_max`. For each type, an alarm is a maximal run of consecutive readings of that type (in file order) that break the limit; `start`/`end` are the times of its first and last breaking reading. Sort all alarms by start, then type name. 5. `last` holds the last sample's tws, twa, twd, vmg (2 decimals) and the last boat speed and depth values as read. ## answer.json `{"counts": {"valid", "bad_checksum", "malformed"}, "true_wind_samples": n, "last": {"tws", "twa", "twd", "vmg", "bsp", "depth"}, "tws_mean": .., "tws_max": .., "alarms": [{"type": "depth"|"wind"|"speed", "start", "end"}, ...]}` (mean and max of TWS over all samples, 2 decimals). ## dashboard.html A self-contained static page (no scripts or network needed) with these element ids: `tws`, `twa`, `twd`, `vmg`, `bsp`, `depth` (each element's text is only the value from `last` with 1 decimal), an `<svg id="wind-rose">` containing a `<line id="twd-needle">` pointing toward TWD, and `<ul id="alarm-list">` with exactly one `<li>` per alarm. Styling is up to you. It must be static and self-contained so it can be screenshotted headless: no `<script>`, no `<img>`/`<link>`/`<iframe>`/`<object>`/`<embed>`, no `on*` handlers, no `href`/`src` except `#...` or `data:`, no CSS `url(http...)` or `@import`, no fetches. ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `sentence_counts` (exact), `true_wind_and_vmg` (within 0.011, sample count exact), `alarm_intervals` (exact), `dashboard_elements` (file exists, static rules above, ids present, `wind-rose` is `<svg>`, `twd-needle` is `<line>`, `alarm-list` is `<ul>`), `dashboard_values` (each value within 0.051 of the answer, li count) on the given log; `fresh_log_1` (all answer keys plus dashboard values) and `fresh_log_2` (all answer keys) on fresh logs. Each run has a 60-second limit.
Checksum XOR with lowercase hex, malformed vs bad-checksum vs skipped MWV, true wind from apparent wind with the latest heading and speed, alarm runs per reading type, and a static HTML display whose values are cross-checked. 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. sentence_counts 2. true_wind_and_vmg 3. alarm_intervals 4. dashboard_elements 5. dashboard_values 6. fresh_log_1 7. fresh_log_2
Setup notes: Use Case Scout batch 10 case k01-nmea-instrument-dashboard (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 (dashboard.html) 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: $0.014469. 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
- $0.01
- Wall time
- 1m
- Human step-ins
- 0
- Turns
- 12
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 356,084 | 12,971 | $0.01 |
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. sentence_counts 2. true_wind_and_vmg 3. alarm_intervals 4. dashboard_elements 5. dashboard_values 6. fresh_log_1 7. fresh_log_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": 69873,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.014468850000000002,
"usage": {
"input_tokens": 24,
"cache_creation_input_tokens": 23265,
"cache_read_input_tokens": 332795,
"output_tokens": 12971,
"output_tokens_details": {
"thinking_tokens": 4860
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 23265,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 842,
"cache_read_input_tokens": 34609,
"cache_creation_input_tokens": 1195,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 1195
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 24,
"outputTokens": 12971,
"cacheReadInputTokens": 332795,
"cacheCreationInputTokens": 23265,
"webSearchRequests": 0,
"costUSD": 0.014468850000000002,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 4860,
"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": 12,
"subtype": "success",
"api_error_status": null,
"result": "`solve.py` is written and runs on the given log in about 25 ms. It writes `answer.json` and `dashboard.html`, and I checked both against the spec.\n\n**Results on `log.nmea`**\n- Counts: 487 valid, 11 bad checksum, 3 malformed. The malformed lines are an `MWV` with a non-numeric AWA (`x62.3`), an `XDR` sentence type that isn't in the list, and a line with no `*`.\n- 95 true-wind samples. TWS mean 17.39, max 19.79.\n- 23 alarms, sorted by start then type.\n- Last values: TWS 17.35, TWA 69.52, TWD 47.72, VMG 1.77, BSP 5.06, depth 5.6.\n\n**Dashboard checks**\n- All six value ids are present, and the `wind-rose` svg, `twd-needle` line and `alarm-list` ul are all there.\n- The dashboard has 23 `<li>` entries, one per alarm.\n- The needle endpoint matches TWD 47.72\u00b0.\n- The page has no script, no `img`, `link`, `iframe`, `object` or `embed`, no `on*` handlers, no `href`/`src`, and no `url(`.\n- It overwrote the stale `dashboard.html` that was in the directory.\n\n**Edge case tested:** a log with no true-wind samples produces `null` for the wind values, and the dashboard shows `--`.\n\n**Interpretation choices**\n- Malformed checks run before the checksum, so a line with a bad checksum counts as `bad_checksum` even if its type is unknown.\n- Alarm runs are consecutive within each sensor's own readings, so an intervening reading of another type does not break a run.\n- The log path in `input.json` is resolved relative to the input file's directory, falling back to the current directory. `dashboard.html` goes in the current directory.\n- `last.bsp` and `last.depth` are the latest values as parsed, not rounded.\n\nThe working directory is not a git repo, so I made no commit. The files are `solve.py`, `answer.json` and `dashboard.html`.",
"ttft_ms": 2170,
"type": "result",
"duration_ms": 70570,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1920,
"time_to_request_ms": 235,
"first_content_frame_ms": 1920,
"queued_turn_count": 0,
"result_index": 0
}
]