Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Best fishing windows from tide, solunar and weather data
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.0071
- Wall time
- 27s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Fishing trip planner from tide, solunar and weather data `input.json` (synthetic) covers 48 hours, hour index h = 0..47 meaning the hour [h, h+1). It has `tide_m` (49 hourly heights at h = 0..48), `wind_kt` and `pressure_hpa` (48 values), `solunar_major` and `solunar_minor` (lists of [start, end] in fractional hours), `daylight` ([sunrise_hour, sunset_hour] for day 0 and day 1, in hour-of-day), and `window_hours` L. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `windows.svg`. Fresh inputs are used too. ## Hourly score - If wind > 20 kt the hour is **unfishable**: score null, ignore the rest. - +3 if any major period overlaps the hour (start < h+1 and end > h); +1.5 if any minor period overlaps (both can apply). - Tide movement d = |tide[h+1] - tide[h]|: +2 if d >= 0.3, else +1 if d >= 0.15. - Wind above 10 kt (and <= 20): -1. - Pressure trend dp = pressure[h] - pressure[h-3] (0 for h < 3), rounded to 6 decimals: +1 if dp <= -1.0, -1 if dp >= +1.0. - Outside daylight: -2, where daylight means sunrise <= (h mod 24) < sunset for day h // 24. Round each score to 2 decimals. ## Windows A window starting at s covers hours s..s+L-1, all fishable; its score is the sum, rounded to 2 decimals. Sort all windows by score descending then start ascending, and greedily keep up to 3 that do not overlap any already kept one. ## answer.json `{"hourly_scores": [48 values or null], "unfishable_hours": n, "windows": [{"start", "end" (= start + L), "score"}, ...]}` ## Visual output: windows.svg Static, self-contained SVG (root `<svg>`; no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, `href`/`src` except `#...` or `data:`, CSS `url(http...)`/`@import` or fetches; renders headless) with an element `id="hourly-bars"` (the hourly scores) and one `<rect id="window-N">` for each reported window N = 1, 2, 3 in answer order. The drawing itself is not graded. ## Scoring Hidden checks (partial credit; all must pass): `hourly_scores_day1` (hours 0-23), `hourly_scores_day2` (24-47 plus unfishable count), `top_windows`, `windows_svg_static` (windows.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Tolerance 0.001. 60-second limit per run.
Overlap tests on fractional periods, threshold boundaries placed exactly at 10 and 20 kt, a 3-hour pressure trend with float rounding, daylight by day index, and greedy non-overlapping window selection with ties. 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. hourly_scores_day1 2. hourly_scores_day2 3. top_windows 4. windows_svg_static 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch 10 case k03-fishing-window-planner (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 (windows.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: $0.007126. 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
- $0.0071
- Wall time
- 27s
- 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 | 144,746 | 5,362 | $0.0071 |
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. hourly_scores_day1 2. hourly_scores_day2 3. top_windows 4. windows_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.
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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
[
{
"duration_api_ms": 26182,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.00712584,
"usage": {
"input_tokens": 12,
"cache_creation_input_tokens": 15770,
"cache_read_input_tokens": 128964,
"output_tokens": 5362,
"output_tokens_details": {
"thinking_tokens": 2084
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 15770,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 502,
"cache_read_input_tokens": 27009,
"cache_creation_input_tokens": 1300,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 1300
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 12,
"outputTokens": 5362,
"cacheReadInputTokens": 128964,
"cacheCreationInputTokens": 15770,
"webSearchRequests": 0,
"costUSD": 0.00712584,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 2084,
"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": "I wrote `solve.py` in the work directory. It uses only the standard library and runs cleanly on `input.json`, producing `answer.json` and `windows.svg`. I checked several hours by hand against the spec (hours 0, 1 and 6) and they match. I did not have a reference answer to compare against, so the hidden checks are untested.\n\n**Result for the given input:** 2 unfishable hours (h=33 and h=40). Top windows are 6\u20139 (12.0), 29\u201332 (10.0) and 11\u201314 (8.0).\n\n**Interpretation choices** (the spec left these open):\n- Tide movement `d` and pressure trend `dp` are rounded to 6 decimals before the threshold comparison. This stops float error from pushing an exact 0.30 or 0.15 across the line.\n- Daylight is indexed by `h // 24`. If an input has more days than `daylight` entries, the last entry is reused. The spec doesn't cover this case.\n- A window's score is the sum of its rounded hourly scores, then rounded to 2 decimals.\n- If fewer than 3 non-overlapping fishable windows exist, `answer.json` reports fewer and `windows.svg` draws only those `window-N` rects.\n- `windows.svg` is written into the same directory as `answer.json`.\n\nFiles are in `./usecase-runner/runs/k03-claude-haiku/work/`: `solve.py`, `answer.json`, `windows.svg`. The directory is not a git repo, so nothing was committed.",
"ttft_ms": 1411,
"type": "result",
"duration_ms": 26669,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1287,
"time_to_request_ms": 271,
"first_content_frame_ms": 1288,
"queued_turn_count": 0,
"result_index": 0
}
]