Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Debt avalanche vs snowball schedule to the cent

AClaude Haiku 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: $0.0071
Wall time: 30s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Debt avalanche vs snowball, to the cent `input.json` lists debts and a fixed monthly budget (synthetic). Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` simulates both payoff methods month by month. We re-run it on fresh inputs, so do not hard-code anything. ## Input `debts`: list of `{"name", "balance", "apr" (yearly percent, e.g. 21.99), "min_payment"}`; `monthly_budget` (total paid each month, including minimums). ## Rules 1. Work in exact cents (use `decimal.Decimal`; convert input numbers with `Decimal(str(x))`). Round with ROUND_HALF_UP to 0.01. 2. If `monthly_budget` < the sum of all minimum payments, the whole answer is `{"error": "budget_below_minimums"}`. 3. Target order (fixed at the start, using the original balances): **avalanche** = highest apr first, ties by smaller balance, then name; **snowball** = smallest balance first, ties by higher apr, then name. 4. Each month, in this order: (a) every debt with a positive balance gets interest = round(balance * apr / 1200) added to its balance; (b) in input order, each debt with a positive balance is paid min(min_payment, balance) from the budget; (c) whatever is left of the budget goes to debts in target order: pay min(left, balance) to the first target with a positive balance, then any remainder to the next, and so on in the same month. Any budget still left at the end of the month is not used. 5. `payoff_month[name]` = the month number (first month = 1) at whose end the debt's balance is 0. `months` = month at which all are 0. If debts remain after month 600, that method's result is `{"error": "not_paid_off"}`. 6. `total_interest` = all interest added; `total_paid` = all payments. `interest_saved_by_avalanche` = snowball total_interest - avalanche total_interest (only when both methods finish). ## Output `answer.json`: `{"avalanche": R, "snowball": R, "interest_saved_by_avalanche": x}` with R = `{"months", "total_interest", "total_paid", "payoff_month": {name: month}, "order": [names in target order]}`. Money as numbers with 2 decimals. ## Scoring Hidden checks (partial credit = fraction passed; all must pass), money within 0.011, months and orders exact: `orders`, `avalanche_totals`, `snowball_totals`, `payoff_months` (and interest saved) on the given input; `fresh_run_1`, `fresh_run_2` (whole answer on fresh inputs, which can contain 0% debts and equal balances); `budget_below_minimums` (a fresh input whose budget is too small). Each run has a 60-second limit.

Cent rounding of monthly interest, minimums before extra, same-month rollover of leftover money, tie rules for 0% debts and equal balances, and the budget-below-minimums error. 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. orders 2. avalanche_totals 3. snowball_totals 4. payoff_months 5. fresh_run_1 6. fresh_run_2 7. budget_below_minimums

Setup notes: Use Case Scout batch 9 case j07-debt-avalanche-snowball (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. Claude Code's own reported cost for claude-haiku-5.5: $0.007118. 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.

Metrics

Estimated cost: $0.0071
Wall time: 30s
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	141,352	6,040	$0.0071

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. orders 2. avalanche_totals 3. snowball_totals 4. payoff_months 5. fresh_run_1 6. fresh_run_2 7. budget_below_minimums
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",
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  "subagent_stats": {
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  "result": "I wrote `solve.py` in the work directory and ran it on `input.json`, which produced `answer.json`. Edge-case runs (budget below minimums, and a case that never pays off) also behaved as the spec describes. I did not check the given-input numbers against an independent calculation, so the scoring checks are unverified.\n\nHeadline results on the given input:\n\n- **Avalanche:** 38 months, total interest 15,924.12, total paid 107,708.85\n- **Snowball:** 41 months, total interest 23,082.79, total paid 114,867.52\n- **Interest saved by avalanche:** 7,158.67\n\nThe script uses only the standard library, converts inputs with `Decimal(str(x))`, rounds with ROUND_HALF_UP to the cent, and follows the monthly order (interest, minimums in input order, remainder down the target order). Output is written with `json.dump`.\n\nChoices I made where the spec was silent:\n- **`interest_saved_by_avalanche`** is `null` when either method returns an error. The spec only says to compute it when both finish, so I chose `null` over omitting the key.\n- **Money values** are emitted as JSON floats, so `1234.50` appears as `1234.5`. The value is exact to the cent, but the text has no trailing zero.\n- **A debt that starts at a zero balance** gets `payoff_month` 0, and `months` is 0 when every debt starts at zero. The spec does not cover this case.",
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]