GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Debt avalanche vs snowball schedule to the cent

OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.06
Wall time: 43s
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. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).

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 batch9 case j07-debt-avalanche-snowball (offline fixture). Prepared with batch9/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 4 (1 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.06
Wall time: 43s
Human step-ins: 0
Turns: 1

Model usage
Model	Input tok	Output tok	Cost
OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on	84,913	1,935	$0.06

Reproduce this run

Use the use case prompt on the use case page.
Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via Codex CLI 0.163.0 (codex exec --json), 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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{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the input, implement the simulation with Decimal arithmetic, and check the output.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py' -g '*test*'\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py' -g '*test*'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/102_j07/work\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "{\n \"debts\": [\n  {\n   \"name\": \"card-blue\",\n   \"balance\": 15874.05,\n   \"apr\": 26.48,\n   \"min_payment\": 282.85\n  },\n  {\n   \"name\": \"card-gold\",\n   \"balance\": 13937.48,\n   \"apr\": 0,\n   \"min_payment\": 247.54\n  },\n  {\n   \"name\": \"car\",\n   \"balance\": 6216.73,\n   \"apr\": 9.46,\n   \"min_payment\": 111.25\n  },\n  {\n   \"name\": \"student\",\n   \"balance\": 12177.17,\n   \"apr\": 25.63,\n   \"min_payment\": 345.46\n  },\n  {\n   \"name\": \"store-card\",\n   \"balance\": 13937.48,\n   \"apr\": 16.35,\n   \"min_payment\": 307.79\n  },\n  {\n   \"name\": \"personal\",\n   \"balance\": 15991.62,\n   \"apr\": 0,\n   \"min_payment\": 412.14\n  },\n  {\n   \"name\": \"medical\",\n   \"balance\": 13650.2,\n   \"apr\": 6.19,\n   \"min_payment\": 245.12\n  }\n ],\n \"monthly_budget\": 2839.15\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/102_j07/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/102_j07/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json && cat answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json && cat answer.json'", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && cat answer.json\npython3 - <<'PY'\nfrom solve import solve\nfrom decimal import Decimal\nbase = {'debts': [{'name':'b','balance':10,'apr':0,'min_payment':1}, {'name':'a','balance':10,'apr':12,'min_payment':1}], 'monthly_budget':15}\nr = solve(base)\nassert r['avalanche']['order'] == ['a','b']\nassert r['snowball']['order'] == ['a','b']\nassert r['avalanche']['months'] == 2\nassert r['avalanche']['total_interest'] == Decimal('0.10')\nassert r['avalanche']['total_paid'] == Decimal('20.10')\nassert r['avalanche']['payoff_month'] == {'b':2,'a':1}\nbase['monthly_budget'] = 1\nassert solve(base) == {'error':'budget_below_minimums'}\nbase['monthly_budget'] = 2\nbase['debts'][0]['balance'] = 10000\nassert solve(base)['avalanche'] == {'error':'not_paid_off'}\nassert solve({'debts': [], 'monthly_budget':0})['avalanche']['months'] == 0\nprint('Edge checks passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && cat answer.json\npython3 - <<'PY'\nfrom solve import solve\nfrom decimal import Decimal\nbase = {'debts': [{'name':'b','balance':10,'apr':0,'min_payment':1}, {'name':'a','balance':10,'apr':12,'min_payment':1}], 'monthly_budget':15}\nr = solve(base)\nassert r['avalanche']['order'] == ['a','b']\nassert r['snowball']['order'] == ['a','b']\nassert r['avalanche']['months'] == 2\nassert r['avalanche']['total_interest'] == Decimal('0.10')\nassert r['avalanche']['total_paid'] == Decimal('20.10')\nassert r['avalanche']['payoff_month'] == {'b':2,'a':1}\nbase['monthly_budget'] = 1\nassert solve(base) == {'error':'budget_below_minimums'}\nbase['monthly_budget'] = 2\nbase['debts'][0]['balance'] = 10000\nassert solve(base)['avalanche'] == {'error':'not_paid_off'}\nassert solve({'debts': [], 'monthly_budget':0})['avalanche']['months'] == 0\nprint('Edge checks passed.')\nPY\"", "aggregated_output": "{\"avalanche\": {\"months\": 38, \"total_interest\": 15924.12, \"total_paid\": 107708.85, \"payoff_month\": {\"card-blue\": 17, \"card-gold\": 38, \"car\": 33, \"student\": 24, \"store-card\": 31, \"personal\": 38, \"medical\": 36}, \"order\": [\"card-blue\", \"student\", \"store-card\", \"car\", \"medical\", \"card-gold\", \"personal\"]}, \"snowball\": {\"months\": 41, \"total_interest\": 23082.79, \"total_paid\": 114867.52, \"payoff_month\": {\"card-blue\": 41, \"card-gold\": 33, \"car\": 7, \"student\": 17, \"store-card\": 29, \"personal\": 39, \"medical\": 23}, \"order\": [\"car\", \"student\", \"medical\", \"store-card\", \"card-gold\", \"card-blue\", \"personal\"]}, \"interest_saved_by_avalanche\": 7158.67}\nEdge checks passed.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "Created [solve.py](solve.py) and generated [answer.json](answer.json).\n\n- Avalanche: 38 months, $15,924.12 interest.\n- Snowball: 41 months, $23,082.79 interest.\n- Avalanche interest savings: **$7,158.67**.\n\nVerified tie ordering, 0% APR, insufficient budget, and the 600-month limit. Run with `python3` here; `python` is unavailable."}}
{"type": "turn.completed", "usage": {"input_tokens": 84913, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 1935, "reasoning_output_tokens": 62}}