Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Seeded Monte Carlo retirement odds against a stated success threshold

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: $0.16
Wall time: 33s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Seeded Monte Carlo retirement odds `input.json` holds the assumptions for a Monte Carlo retirement simulation (synthetic). Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` runs the simulation exactly as specified and writes the statistics. Because the random sequence is fixed by the seed, the answer is exact. We re-run it on fresh inputs, so do not hard-code anything. ## Input `seed`, `sims`, `years` (integers), `start_balance`, `annual_spend` (first-year spending), `mean_return`, `sd_return`, `mean_inflation`, `sd_inflation`, `success_threshold` (fractions). ## Rules 1. Create one generator `rng = random.Random(seed)` and use it for everything, in this order. Run simulations s = 0 .. sims-1 one after another. 2. Each simulation starts with balance = `start_balance` and spend = `annual_spend` (as floats). For each year y = 0 .. years-1: draw `r = rng.gauss(mean_return, sd_return)`, then `i = rng.gauss(mean_inflation, sd_inflation)` (both are drawn every year, in that order, before anything else), then balance -= spend. 3. If the balance is now below 0, the simulation **fails** in year y+1: its ending balance is 0 and it stops immediately (no more draws for it). Otherwise balance *= (1 + r) and spend *= (1 + i). 4. A simulation that never fails succeeds; its ending balance is the balance after the last year. 5. `success_rate` = successes / sims (round to 6 decimals). `meets_threshold` = (unrounded success rate >= `success_threshold`). `sims_failed` = number of failures. 6. Percentiles: sort all ending balances ascending (failed ones count as 0) into E; `pX` = E[int(X/100 * (sims - 1))] for X = 10, 50, 90 (round to 2 decimals). 7. `median_failure_year`: sort the failure years ascending into F and take F[int(0.5 * (len(F) - 1))]; `null` if nothing failed. 8. `best_sim` = index of the highest ending balance (lowest index on ties). `earliest_failure_sim` = index of the simulation that failed in the earliest year (lowest index on ties); `null` if nothing failed. ## Output `answer.json`: an object with keys `success_rate`, `meets_threshold`, `sims_failed`, `p10`, `p50`, `p90`, `median_failure_year`, `best_sim`, `earliest_failure_sim`. ## Chart (required visual) Also write `chart.svg` in the working directory: a static, self-contained SVG (root element `<svg>`, no scripts, no external images, fonts, stylesheets or links) that can be rendered headless. It must contain a percentile fan: `<polyline id="p10">`, `<polyline id="p50">`, `<polyline id="p90">`, each the 10th/50th/90th percentile end-of-year balance across simulations for each year (a failed simulation counts as 0 from its failure year). ## Scoring The `chart_svg_static` check (one more check, counted in partial credit) passes only if `chart.svg` exists after the fixture run, parses, has root `<svg>`, contains the ids above with the stated tags, and has no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, or `href`/`src` pointing anywhere except `#...` or `data:`. Its drawing is not graded. Hidden checks (partial credit = fraction passed; all must pass): `success_rate_and_threshold` (rate within 0.000001, the other two exact), `percentiles` (within 0.011), `failure_stats` (exact), `best_sim` (exact) on the given input; `fresh_run_1` and `fresh_run_2`: every key on a fresh input (money within 0.011, rates within 0.011, counts and indexes exact). Each run has a 120-second limit.

With a fixed seed the success rate is exact only if the draw order (return then inflation, every year, stop on failure) and the percentile/tie rules are followed exactly; approximate simulations score near zero. 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).

These are synthetic test cases for comparing AI models, not financial advice.

Success criteria: 1. success_rate_and_threshold 2. percentiles 3. failure_stats 4. best_sim 5. fresh_run_1 6. fresh_run_2 7. chart_svg_static

Setup notes: Use Case Scout batch 9 case j02-retirement-monte-carlo (offline fixture). Prepared with batch9/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 (chart.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-opus-5.5: $0.160155. 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 Opus 5.5 (effort default (no --effort flag) · thinking on) on Seeded Monte Carlo retirement odds against a stated success thresholdSuccess

Metrics

Estimated cost: $0.16
Wall time: 33s
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	102,786	3,156	$0.16

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. success_rate_and_threshold 2. percentiles 3. failure_stats 4. best_sim 5. fresh_run_1 6. fresh_run_2 7. chart_svg_static
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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    "provider": "firstParty",
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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 `solve.py`, and `python solve.py input.json answer.json` runs in about 0.05 seconds, well under the 120-second limit. I checked the main output against a separate, cut-down recomputation of the ending balances, and the median matched. The other statistics only come from the one script, so I haven't cross-checked them.\n\nResults on the given input:\n\n| Key | Value |\n|---|---|\n| `success_rate` | 0.824667 |\n| `meets_threshold` | false (threshold is 0.9) |\n| `sims_failed` | 263 |\n| `p10` | 0.0 |\n| `p50` | 1473403.0 |\n| `p90` | 5177505.89 |\n| `median_failure_year` | 23 |\n| `best_sim` | 692 |\n| `earliest_failure_sim` | 294 |\n\n- **`p10` is 0** because more than 10% of the simulations ran out of money.\n- **`p50` looks suspiciously round,** so I checked it: the unrounded value is 1473403.00029, so it's genuine and not a bug.\n- **Chart:** the script also writes `chart.svg` in the working directory. It's a plain SVG with three lines, `p10`, `p50` and `p90`, showing those percentiles of the end-of-year balance for each year, with failed simulations counted as 0 from the year they fail. It has no scripts, images or links. I confirmed it parses, has an `<svg>` root and contains all three line ids; I didn't render it to look at the drawing.\n\nNothing is hard-coded: every value comes from the input file, so fresh inputs run the same way.",
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