Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on AIS collision risk: closest point of approach and alert levels

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: $0.0068
Wall time: 27s
Human step-ins: 0
Outcome: Pass

Compare with other models on this task →

Exact prompt

# AIS collision risk: CPA, TCPA and alert levels `input.json` (synthetic; not for collision avoidance): `own` ship (`lat`, `lon`, `cog` degrees true, `sog` knots), `targets` (each `mmsi`, `lat`, `lon`, `cog`, `sog`), thresholds `cpa_danger_nm`, `cpa_caution_nm`, `tcpa_max_min`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `radar.svg`. Fresh inputs are used too. ## Rules 1. Skip a target (list its mmsi in `skipped`, ascending) if lat, lon, cog or sog is missing/null, sog >= 102.3 (AIS "not available"), or cog >= 360 (not available). Keep the rest in input order. 2. Local flat frame around own ship in nm: x = (lon - own lon) * 60 * cos(own lat), y = (lat - own lat) * 60. Velocity of a ship = (sog * sin(cog), sog * cos(cog)) knots. 3. Relative position r = target - own, relative velocity v = v_target - v_own. If |v|^2 < 1e-9, TCPA = 0. Otherwise TCPA (hours) = max(0, -(r . v) / |v|^2). CPA = |r + v * TCPA|. 4. `range_nm` = |r|, `bearing_deg` = atan2(x, y) mod 360 (true bearing from own ship), `tcpa_min` = TCPA * 60. 5. Level: `danger` if CPA <= cpa_danger and tcpa_min <= tcpa_max; else `caution` if CPA <= cpa_caution and tcpa_min <= tcpa_max; else `none`. 6. `alerts`: mmsi of danger then caution targets, each group sorted by tcpa_min then mmsi. Round range/CPA to 3 decimals, bearing to 1, tcpa_min to 2. ## Visual output: radar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="own-ship"` and one element `id="target-<mmsi>"` for every non-skipped target. The drawing itself is not graded. ## answer.json `{"targets": [{"mmsi", "range_nm", "bearing_deg", "cpa_nm", "tcpa_min", "level"}, ...], "skipped": [...], "alerts": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `filtering`, `range_bearing`, `cpa_tcpa`, `levels_and_alerts`, `radar_svg_static` (radar.svg exists, follows the static rules, has `own-ship` and every `target-<mmsi>`), `fresh_run_1`, `fresh_run_2`. Tolerances: distances 0.002 nm, bearings 0.11, minutes 0.011 (fresh runs 0.011 for all numbers). 60-second limit per run.

AIS not-available sentinels (sog 102.3, cog 360), a stationary target, diverging targets with TCPA clamped to 0, longitude scaling, near-threshold crossing targets, and a two-key alert sort. 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. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_2

Setup notes: Use Case Scout batch 10 case k08-ais-cpa-tcpa (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 (radar.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: $0.006794. 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 AIS collision risk: closest point of approach and alert levelsSuccess

Metrics

Estimated cost: $0.0068
Wall time: 27s
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	141,866	5,031	$0.0068

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. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_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.

Was this useful?

⇄ Rerun on another model

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": 25911,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.006793790000000001,
  "usage": {
   "input_tokens": 12,
   "cache_creation_input_tokens": 15045,
   "cache_read_input_tokens": 126809,
   "output_tokens": 5031,
   "output_tokens_details": {
    "thinking_tokens": 964
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 15045,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 622,
     "cache_read_input_tokens": 26331,
     "cache_creation_input_tokens": 1253,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 1253
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 12,
    "outputTokens": 5031,
    "cacheReadInputTokens": 126809,
    "cacheCreationInputTokens": 15045,
    "webSearchRequests": 0,
    "costUSD": 0.006793790000000001,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 964,
    "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": 7,
  "subtype": "success",
  "api_error_status": null,
  "result": "`solve.py` is written and runs cleanly on the provided `input.json`. It writes `answer.json` and `radar.svg`, and the SVG parses as valid XML with `own-ship` and all 10 `target-<mmsi>` elements.\n\nOn the sample input:\n- **Skipped:** 340778672 (cog = 360, not available) and 660159254 (sog = 102.3, not available).\n- **Alerts:** 795070736 and 613463677 (danger), then 265918776 and 562375419 (caution), each ordered by TCPA.\n\nThe implementation follows the rules in `TASK.md` directly: the same flat-frame geometry, the TCPA clamp and near-zero-velocity case, and rounding only at output. Levels and alerts are judged on unrounded values. I did not hand-check the numbers against an independent calculation, and I only ran the one input, not any fresh inputs.\n\nChoices I made that you may want to redirect:\n- **`radar.svg` location:** the spec doesn't say where it goes. The script writes it next to `answer.json` and also to the current working directory when those differ.\n- **Missing keys:** a target with a missing key is treated the same as a null value and skipped.\n- **Bearing wrap:** a bearing that rounds up to 360.0 is reported as 0.0.\n\nFiles are in `./usecase-runner/runs/k08-claude-haiku/work/`: `solve.py`, `answer.json`, `radar.svg`. This is not a git repository, so I did not commit.",
  "ttft_ms": 2692,
  "type": "result",
  "duration_ms": 26334,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 2602,
  "time_to_request_ms": 243,
  "first_content_frame_ms": 2602,
  "queued_turn_count": 0,
  "result_index": 0
 }
]