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

AClaude Opus 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: $0.14
Wall time: 40s
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 3.09 (8 cores). Claude Code's own reported cost for claude-opus-5.5: $0.137199. 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: $0.14
Wall time: 40s
Human step-ins: 0
Turns: 4

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	77,125	2,709	$0.14

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Opus 5.5 (anthropic/claude-opus-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

[
 {
  "duration_api_ms": 28870,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.13719900000000002,
  "usage": {
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   "cache_creation_input_tokens": 8662,
   "cache_read_input_tokens": 68455,
   "output_tokens": 2709,
   "output_tokens_details": {
    "thinking_tokens": 410
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 8662,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 393,
     "cache_read_input_tokens": 19839,
     "cache_creation_input_tokens": 969,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 969
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-opus-5-5": {
    "inputTokens": 8,
    "outputTokens": 2709,
    "cacheReadInputTokens": 68455,
    "cacheCreationInputTokens": 8662,
    "webSearchRequests": 0,
    "costUSD": 0.13719900000000002,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 410,
    "canonicalModel": "claude-opus-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": 4,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `solve.py`. Its output matched a brute-force checker on the given `input.json` and on 300 random small inputs that included invalid, zero-length and duplicate bookings. A synthetic run with 150,000 bookings and 300,000 queries finished in about 0.76 s, well inside the 20 s limit.\n\n**How it works:**\n- **Bookings:** each booking with start > end adds 1 to `invalid_bookings`. Bookings with start == end are skipped. Every valid booking adds +1 at its start and \u22121 at its end.\n- **Occupancy:** the distinct start and end times are sorted, and a running sum gives the occupancy between each pair of neighbouring times. Before the first time and after the last, occupancy is 0.\n- **Peak:** `peak` is the highest of those values, and `peak_first_time` is where it first appears.\n- **Points:** each time is found with a binary search and answered directly.\n- **Ranges:** for [l, r) the code looks at every interval touching l through r\u22121 and takes the maximum. It uses a precomputed lookup table, so each query takes constant time, and l \u2265 r returns null.\n\nThe code assumes all times are integers, as the task states.",
  "ttft_ms": 2726,
  "type": "result",
  "duration_ms": 39223,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1700,
  "time_to_request_ms": 256,
  "first_content_frame_ms": 1700,
  "queued_turn_count": 0,
  "result_index": 0
 }
]