Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Go/no-go departure time from a gridded wind file
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.0065
- Wall time
- 26s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Go/no-go departure time from a gridded wind file `input.json` (synthetic; not a forecast) names `grid.json` and gives `from` and `to` [lat, lon], `passage_hours` D (integer), `latest_departure_h`, and `limits`. `grid.json` has `lats`, `lons`, `times_h` (ascending) and fields `u_kt`, `v_kt` (eastward/northward wind components, knots, the direction the air moves TOWARD) and `gust_kt`, each indexed `[time][lat][lon]`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `window.svg`. Fresh inputs are used too. ## Rules 1. Interpolation of each field: bilinear in (lat, lon) at the two bracketing grid times, then linear in time. Outside a grid axis, clamp to its edge. 2. Course = atan2((lon_to - lon_from) * cos(mean lat), lat_to - lat_from) in degrees mod 360. 3. For every integer departure hour t0 = 0 .. latest_departure_h: sample k = 0..D at time t0 + k and position from + (to - from) * k / D (straight in lat/lon). At each sample: wind speed = hypot(u, v); wind FROM direction = atan2(-u, -v) mod 360; off = smallest angle between it and the course. The sample fails, checked in this order, with reason `wind@<t>` if speed > max_wind_kt, `gust@<t>` if gust > max_gust_kt, `headwind@<t>` if off < min_wind_angle_deg and speed > headwind_max_kt. Stop at the first failing sample; that departure is not ok and its reason is recorded (t as an integer). Otherwise ok with reason null. 4. `earliest_ok_h`: first ok departure or null. `longest_ok_run`: [first, last] departure hours of the longest run of consecutive ok departures (earliest wins ties), or null. ## Visual output: window.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="timeline"` (one mark per departure hour) and an element `id="best-departure"`. The drawing itself is not graded. ## answer.json `{"course_deg", "departures": [{"depart_h", "ok", "reason"}, ...], "earliest_ok_h", "longest_ok_run"}` ## Scoring Hidden checks (partial credit; all must pass): `course` (within 0.11), `departure_flags` (ok flags), `failure_reasons` (exact), `best_departure`, `window_svg_static` (window.svg exists, follows the static rules, has the ids above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Space-time interpolation of vector components (not speed and direction), the toward/from convention, ordered failure reasons per sample along a moving position, and run-length 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. course 2. departure_flags 3. failure_reasons 4. best_departure 5. window_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 10 case k10-wind-grid-departure (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 (window.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: $0.006455. 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
- $0.0065
- Wall time
- 26s
- Human step-ins
- 0
- Turns
- 5
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 113,055 | 5,423 | $0.0065 |
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. course 2. departure_flags 3. failure_reasons 4. best_departure 5. window_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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"service_tier": "standard",
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"type": "message"
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"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 1292,
"canonicalModel": "claude-haiku-5-5",
"provider": "firstParty",
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},
"permission_denials": [],
"terminal_reason": "completed",
"fast_mode_state": "off",
"fast_mode_disabled_reason": "sdk_opt_in_required",
"subagent_stats": {
"spawned": 0,
"requested": {
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"subtype": "success",
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"result": "I wrote `solve.py` and ran it on the provided input. It produces `answer.json` and `window.svg` in about 0.04 s.\n\n**Result:** course is 39.98\u00b0. Departures 0\u201311 fail (wind or gust limits, at samples t = 6 to 11), and departures 12\u201330 are all ok. So `earliest_ok_h` is 12 and `longest_ok_run` is `[12, 30]`. The headwind rule never fires on this input.\n\n**Checks I ran:** the course matches a hand calculation (atan2(0.841, 1.004) \u2248 39.97\u00b0), and `window.svg` parses as XML and contains both `id=\"timeline\"` and `id=\"best-departure\"`. I have not compared the flags against an independent implementation, and I have not tested fresh inputs.\n\n**Choices I made where the spec was open:**\n- **Time clamping:** I clamp sample times to the grid's time axis as well as lat/lon, following the \"outside a grid axis, clamp\" rule.\n- **`best_departure`:** the spec doesn't define it in `answer.json`. I took it to mean `earliest_ok_h`, and the `best-departure` marker in the SVG points at that hour.\n- **Zero-length passage:** if `passage_hours` is 0, the solver samples only the departure point and doesn't divide by zero.\n- **Output location:** `window.svg` is written next to `answer.json`.\n- **Precision:** `course_deg` is not rounded.\n\nNothing is committed; the directory is not a git repository.",
"ttft_ms": 1720,
"type": "result",
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"uuid": "[REDACTED]",
"ttft_stream_ms": 1589,
"time_to_request_ms": 513,
"first_content_frame_ms": 1589,
"queued_turn_count": 0,
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]