Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Post-stratified results for a two-wave customer survey with repeat respondents and mixed income units

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: $0.13
Wall time: 27s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Weighted results for a two-wave customer survey Inputs: `responses.csv`, `waves.json` (per wave: the unit of the income column), `population.json` (each region's share of the customer base). Rules: - One person may have answered more than once. Emails are case-insensitive and may carry stray spaces. Keep only each person's latest submission by actual time; submitted_at carries different UTC offsets. - Drop respondents (after de-duplication) whose age is blank or under 18. - In q1-q3, the code 99 means "don't know": leave it out of that question only. - Income is in the unit given for the response's wave in waves.json: "USD" or "kUSD" (thousands of USD). Report income in USD. - Weight for every kept respondent in region g = population share of g / (kept respondents in g / all kept respondents). - Weighted mean = sum(weight x value) / sum(weight) over the kept respondents who have a valid value. Round q means half-up to 3 decimals and income half-up to 2 decimals. Write `out/survey.json`: `{"n": <kept respondents>, "region_n": {"<region>": <int>, ...}, "weighted_means": {"q1": <n>, "q2": <n>, "q3": <n>}, "weighted_mean_income_usd": <n>}`. q means are checked to within 0.0005. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.

Clean about 2,500-3,500 survey responses and compute region-weighted means for three questions and income. Repeat respondents must be matched case- and space-insensitively and only their latest submission kept, comparing timestamps with mixed UTC offsets. Minors are dropped, 'don't know' codes are excluded per question, and one wave reports income in thousands. Means near rounding ties are redrawn. 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: dedup_kept_count region_counts q1_mean q2_q3_means income_units_and_weights

Setup notes: Batch 17 case s04-survey-weighting (offline, synthetic inputs, seed 188814826). Autonomous case: no stand-in user and no step-ins expected; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths); pass = every check true and no ask/ files. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.17 (8 cores). Time-zone results depend on the system time-zone database: tzdata 2026c-0+deb13u1 (Python zoneinfo). Claude Code's own reported cost for claude-opus-5.5: $0.1291. Score: 5/5. 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: $0.13
Wall time: 27s
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	97,569	2,093	$0.13

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: dedup_kept_count region_counts q1_mean q2_q3_means income_units_and_weights
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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  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
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  "subagent_stats": {
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  "result": "I've written `out/survey.json`. After de-duplication and the age filter, 2,298 respondents remain.\n\n```json\n{\"n\": 2298,\n \"region_n\": {\"Central\": 236, \"East\": 368, \"North\": 447, \"South\": 662, \"West\": 585},\n \"weighted_means\": {\"q1\": 3.279, \"q2\": 3.193, \"q3\": 3.028},\n \"weighted_mean_income_usd\": 128987.62}\n```\n\nHow the rules were applied:\n- **Duplicates:** I trimmed spaces from emails and lowercased them before matching. Timestamps were compared as actual moments in time, with their UTC offsets taken into account. That left 2,631 unique people out of 3,136 rows, and no person had two submissions at exactly the same time.\n- **Age:** I dropped people whose age was blank (64 rows had no age) or under 18 (some ages were 14 to 17).\n- **q1\u2013q3:** I left out 99 (\"don't know\") and blank answers from each question separately.\n- **Income:** Wave 2 was converted from kUSD to USD by multiplying by 1,000. After conversion both waves cover about the same range ($15k\u2013$240k), so the units look right. Blank incomes were left out of the income mean.\n- **Weights and rounding:** Each region's weight is its population share divided by its share of the kept respondents. Means were rounded half-up to 3 decimals for q1\u2013q3 and 2 decimals for income.\n\nThe script is at `/tmp/s.py`, outside the work folder. You can rerun it with `python3 -I /tmp/s.py .` from the work directory.",
  "ttft_ms": 4372,
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  "time_to_request_ms": 236,
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  "result_index": 0
 }
]