Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Berth occupancy point and range-maximum queries at scale

AClaude Sonnet 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: $0.05
Wall time: 16s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Marina berth occupancy queries at scale `input.json` (synthetic) has `bookings` ([start, end) integer minute pairs), `points` (integer times) and `ranges` ([l, r) pairs). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. One hidden input has **150,000 bookings and about 300,000 queries and must finish within 20 seconds** (other runs: 30 s). Answers must be exact. ## Rules 1. A booking with start > end is invalid: count it in `invalid_bookings` and ignore it. A booking with start == end is ignored (not invalid). Duplicate bookings count separately. 2. Occupancy at integer time t = number of valid bookings with start <= t < end. 3. `points`: occupancy at each time, in input order. 4. `ranges`: for each [l, r): if l >= r the answer is null; otherwise the maximum occupancy over all integer t with l <= t < r. In input order. 5. `peak` = maximum occupancy over all times (0 if there are no valid bookings); `peak_first_time` = the earliest time it is reached (null if peak is 0). ## answer.json `{"invalid_bookings", "peak", "peak_first_time", "points": [...], "ranges": [...]}` ## Scoring Hidden checks (partial credit; all must pass; all exact): `invalid_and_peak`, `point_queries`, `range_max_queries` on the given input; `fresh_run_1`, `fresh_run_2`; `big_150k_bookings_300k_queries_20s`.

The rules are simple and a brute-force answer is correct on small inputs, so separation comes from choosing a sweep plus range-maximum structure and getting the half-open boundaries right. 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).

Success criteria: 1. invalid_and_peak 2. point_queries 3. range_max_queries 4. fresh_run_1 5. fresh_run_2 6. big_150k_bookings_300k_queries_20s

Setup notes: Use Case Scout batch 12 case m08-berth-occupancy-queries (offline, synthetic fixture). Prepared with batch12/prepare.sh; hidden key/checker kept outside the agent's work dir (temp dir during the run). Prompt = TASK.md verbatim. Scored with the case's scorer: `python score.py WORKDIR KEYDIR` (python3, run from the case dir). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.35 (8 cores). Claude Code's own reported cost for claude-sonnet-5.5: $0.051905. 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.

Metrics

Estimated cost: $0.05
Wall time: 16s
Human step-ins: 0
Turns: 2

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	38,412	1,883	$0.05

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-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. invalid_and_peak 2. point_queries 3. range_max_queries 4. fresh_run_1 5. fresh_run_2 6. big_150k_bookings_300k_queries_20s
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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   "output_tokens_details": {
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   "server_tool_use": {
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   "cache_creation": {
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    }
   ],
   "speed": "standard",
   "fallback_credit": null
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  "modelUsage": {
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    "costUSD": 0.0519054,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 487,
    "canonicalModel": "claude-sonnet-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": {
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   "requested": {
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  "safety_stops": 0,
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  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `solve.py`. It handled the large generated input in about 0.6 s, against the 20 s limit.\n\n- **How it works:** it records +1/\u22121 changes at each valid booking's start and end, then sorts the distinct times and takes running sums. That gives the occupancy of every stretch between event times. Points are answered with a binary search. Range maximums use a sparse table, so each range query is O(1) after setup.\n- **Checks run:** a small hand-made input and one random input with 150,000 bookings and 300,000 queries (150,000 points, 150,000 ranges).\n- **Small-input check:** I read the output by eye and it looks right. It counted 1 invalid booking, a peak of 3 first reached at time 3, and `null` for the empty range.\n- **Not checked:** I did not compare the output against a brute-force solution, so exactness on large inputs rests on the logic, not on a cross-check.\n\nI didn't create `answer.json` for the real `input.json`, since there was none in the working directory. To produce it, run `python solve.py input.json answer.json`.",
  "ttft_ms": 5448,
  "type": "result",
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  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1012,
  "time_to_request_ms": 261,
  "first_content_frame_ms": 1012,
  "queued_turn_count": 0,
  "result_index": 0
 }
]