Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Retirement drawdown with a 4% rule, inflation raises and a skip-raise-after-loss guardrail
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.0062
- Wall time
- 23s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Retirement drawdown: the 4% rule with inflation raises and a guardrail `input.json` describes one retiree and one assumed sequence of yearly market returns and inflation rates (synthetic data). Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` reads the input and writes the answer. We re-run it on fresh inputs of the same shape, so do not hard-code anything. ## Input `start_age`, `end_age` (integers), `start_balance`, `withdrawal_rate` (e.g. 0.04), `fee_rate` (yearly fraction), `returns` and `inflation` (lists of fractions, one per year, length `end_age - start_age`). ## Rules 1. There are n = `end_age - start_age` years, k = 0 .. n-1. Year k is the year the retiree is age `start_age + k`. 2. The planned withdrawal for year 0 is W0 = `start_balance * withdrawal_rate`. For k >= 1 the planned withdrawal is last year's planned withdrawal times `(1 + inflation[k-1])`. 3. **Guardrail scenario only:** for k >= 1, if `returns[k-1] < 0` (strictly) the raise is skipped that year: the planned withdrawal stays equal to last year's planned withdrawal. Later raises apply to the frozen amount; there is no catch-up. 4. Each year, the withdrawal is taken at the start of the year: actual withdrawal w = min(planned, balance). Then balance = (balance - w) * (1 + returns[k]) * (1 - fee_rate). 5. `depletion_age` = `start_age + k` for the first year k where w < planned (the full planned amount could not be paid), otherwise `null`. `shortfall_total` = sum over all years of (planned - w). `total_withdrawn` = sum of w. 6. `path` = the balance at the end of each year (after rule 4), n values. `final_balance` = the last one. `real_final_balance` = `final_balance` divided by the product of `(1 + inflation[k])` over all n years. 7. Keep full floating-point precision while computing; round money outputs to 2 decimals only when writing. ## Output `answer.json`: `{"standard": S, "guardrail": G}` where each of S and G is an object with keys `final_balance`, `real_final_balance`, `total_withdrawn`, `shortfall_total`, `depletion_age`, `path`. "standard" ignores rule 3; "guardrail" applies it. ## 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 `<polyline id="standard">` and `<polyline id="guardrail">` drawing the two `path` balance series against 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). Money values must match the reference within 0.011; `depletion_age` exactly; `path` must have exactly n values. - `standard_path`, `standard_summary` (the five summary keys), `guardrail_scenario`: on the given `input.json`. - `fresh_standard`, `fresh_guardrail`: on a fresh input. - `depletion_and_shortfall`: both scenarios on a fresh input where the money runs out. Each run has a 60-second limit.
Exact year-by-year balances depend on withdrawal timing, fee application order, the strict-negative guardrail with no catch-up, and how depletion and shortfall are recorded; fresh inputs include one where money runs out. 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. standard_path 2. standard_summary 3. guardrail_scenario 4. fresh_standard 5. fresh_guardrail 6. depletion_and_shortfall 7. chart_svg_static
Setup notes: Use Case Scout batch 9 case j01-retirement-drawdown (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-haiku-5.5: $0.006171. 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
Metrics
- Estimated cost
- $0.0062
- Wall time
- 23s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 139,457 | 4,352 | $0.0062 |
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. standard_path 2. standard_summary 3. guardrail_scenario 4. fresh_standard 5. fresh_guardrail 6. depletion_and_shortfall 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.
Was this useful?
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 wrote `solve.py` and ran it on the provided `input.json`. It produces `answer.json` and `chart.svg`, and both pass my structural checks.\n\n**Results on the given input (age 65 to 91, 26 years)**\n\n| | standard | guardrail |\n|---|---|---|\n| final_balance | 686,614.75 | 1,008,139.92 |\n| real_final_balance | 353,101.06 | 518,449.79 |\n| total_withdrawn | 1,817,310.89 | 1,652,713.06 |\n| shortfall_total | 0.00 | 0.00 |\n| depletion_age | null | null |\n\nI also ran a synthetic input where the money runs out (8% withdrawal, a 300k start, and a crash in year 0). Both scenarios depleted, at ages 71 (standard) and 72 (guardrail), with positive shortfalls and the guardrail's frozen raises visibly lowering withdrawals. I can't see the hidden reference, so these numbers are checked against my reading of the rules, not against it.\n\n**Choices I made**\n- The solver follows the rules as written. Money is rounded to 2 decimals only when writing, and `real_final_balance` divides by the inflation product over all n years.\n- `chart.svg` goes in the current working directory, as the spec says. It is a plain SVG with the two `<polyline>` elements and no scripts, links, or external references. I checked that it parses and that its root is `<svg>`.\n- I did not run `git` commit since this directory is not a repository, and you didn't ask for a commit.\n\nFiles are in `./usecase-runner/runs/j01-claude-haiku/work/`: `solve.py`, `answer.json`, `chart.svg`.",
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