Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Pension claiming-age comparison: lifetime totals, present values and break-even ages
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-23 · by @bobsiopener
- Estimated cost
- $0.0024
- Wall time
- 43s
- Human step-ins
- 0
- Outcome
- Fail
Compare with other models on this task →
Rescored on 2026-10-28. Previous score: 17.5 / 100, outcome: fail. See the previous run.
Summary
Exact prompt
# Pension claiming age: lifetime totals, present values and break-even ages `input.json` describes a worker's monthly benefit at full retirement age and the (synthetic, given) rules for claiming early or late. Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` compares claiming ages. We re-run it on fresh inputs, so do not hard-code anything. Use only the rules below and the numbers in the input. ## Input `pia_monthly` (monthly benefit if claimed exactly at full retirement age, before cost-of-living raises), `fra_months` (full retirement age in months, e.g. 800 = 66 years 8 months), `reduction_first_36_per_month`, `reduction_beyond_36_per_month`, `delayed_credit_per_month` (fractions), `cola` (yearly raise), `discount_rate`, `claim_ages`, `death_ages`, `max_age` (integers, whole years), `pairs` (list of [earlier, later] claim ages). ## Rules 1. Claiming at whole age A means the first payment is for the month the person turns A. Let m = A*12 - `fra_months`. 2. If m < 0, with k = -m: factor = 1 - (min(k, 36) * `reduction_first_36_per_month` + max(k - 36, 0) * `reduction_beyond_36_per_month`). If m >= 0: factor = 1 + m * `delayed_credit_per_month`. 3. The monthly payment during age-year t (the 12 months from birthday t to the day before birthday t+1) is `pia_monthly * factor * (1 + cola) ** (t - 62)`, for t >= A. Raises apply from age 62 whether or not the person has claimed yet. No rounding of payments. 4. `monthly_at_claim` = the monthly payment for age-year A. 5. `lifetime[D]` = sum of all monthly payments for age-years A .. D-1 (death at birthday D, nothing paid from then on). 6. `pv[D]` = sum over age-years t = A .. D-1 of (12 * monthly payment for t) / (1 + discount_rate) ** (t - 62). 7. Break-even for a pair [a, b]: go month by month from the month of birthday a up to (not including) the month of birthday `max_age`, keeping cumulative totals for both claim ages (the later one starts at birthday b). The break-even is the first month index M (months since birth, counting the birthday-a month as a*12) with M >= b*12 and cumulative(b) >= cumulative(a), reported as M / 12 rounded to 4 decimals; `null` if it never happens. 8. `best_by_pv[D]` = the claim age with the highest `pv[D]` (lower age on ties). 9. Round money to 2 decimals and `factor` to 6 decimals only when writing. ## Output `answer.json`: ``` {"claims": {"62": {"factor": ..., "monthly_at_claim": ..., "lifetime": {"72": ..., ...}, "pv": {"72": ..., ...}}, ... one entry per claim age}, "breakeven": {"62-67": ..., ...one key per pair "a-b"}, "best_by_pv": {"72": 70, ...one key per death age}} ``` Keys are strings of the integer ages. ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `factors_and_monthly` (factor within 0.000001, money within 0.011), `lifetime_totals`, `present_values` (within 0.011), `breakeven_ages` (within 0.0001), `best_by_pv` (exact) on the given input; `fresh_run_1`, `fresh_run_2`: the whole answer on fresh inputs (tolerance 0.011; integers exact). Each run has a 60-second limit.
Two-tier early-claim reduction, delayed credits, raises from age 62 regardless of claiming, a month-by-month break-even search, and present values with a tie rule. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x the site's price table).
These are synthetic test cases for comparing AI models, not financial advice.
Success criteria: 1. factors_and_monthly 2. lifetime_totals 3. present_values 4. breakeven_ages 5. best_by_pv 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 9 case j03-claiming-age-breakeven (offline fixture). Prepared with the case's setup script; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored automatically with the scout's scorer. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. Harness: venice-mini-agent v1.3.1 via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max 16,000 output tokens per call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.05, thinking on (model default)). Same task text, work-dir layout and scorer as the Claude runs of this case. Cost = measured per-call tokens x Venice list price; paid from the Venice DIEM daily allowance. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 1/7. Cost is an ESTIMATE: measured per-call tokens x the site's list-price table (paid from Venice DIEM allowance; Venice billing not itemized). input tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.0024
- Wall time
- 43s
- Human step-ins
- 0
- Turns
- 7
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 21,502 | 1,708 | $0.0024 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Mistral Small 3.2 24B Instruct (Venice) (venice/mistral-small-3-2-24b-instruct, thinking on) via
venice-mini-agent v1.3.1, cloud. - It counts as a success only if it meets every success criterion: 1. factors_and_monthly 2. lifetime_totals 3. present_values 4. breakeven_ages 5. best_by_pv 6. fresh_run_1 7. fresh_run_2
- 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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"arguments": "{\"path\": \"solve.py\", \"content\": \"import json\\n\\n\\ndef calculate_factors(pia_monthly, fra_months, reduction_first_36_per_month, reduction_beyond_36_per_month, delayed_credit_per_month, cola, discount_rate, claim_ages, death_ages, max_age, pairs):\\n factors = {}\\n monthly_at_claim = {}\\n\\n for age in claim_ages:\\n m = age * 12 - fra_months\\n if m < 0:\\n k = -m\\n factor = 1 - (min(k, 36) * reduction_first_36_per_month + max(k - 36, 0) * reduction_beyond_36_per_month)\\n else:\\n factor = 1 + m * delayed_credit_per_month\\n \\n factors[str(age)] = round(factor, 6)\\n monthly_at_claim[str(age)] = round(pia_monthly * factor, 2)\\n\\n return factors, monthly_at_claim\\n\\ndef calculate_lifetime(pia_monthly, fra_months, cola, claim_ages, death_ages, factors, monthly_at_claim):\\n lifetime = {age: {} for age in claim_ages}\\n\\n for claim_age in claim_ages:\\n for death_age in death_ages:\\n if death_age < claim_age:\\n lifetime[claim_age][str(death_age)] = 0.0\\n continue\\n\\n total = 0.0\\n for t in range(claim_age, death_age):\\n factor = factors[str(claim_age)] * (1 + cola) ** (t - 62)\\n monthly_payment = pia_monthly * factor\\n total += monthly_payment\\n\\n lifetime[claim_age][str(death_age)] = round(total, 2)\\n\\n return lifetime\\n\\ndef calculate_pv(pia_monthly, fra_months, cola, discount_rate, claim_ages, death_ages, factors):\\n pv = {age: {} for age in claim_ages}\\n\\n for claim_age in claim_ages:\\n for death_age in death_ages:\\n if death_age < claim_age:\\n pv[claim_age][str(death_age)] = 0.0\\n continue\\n\\n total = 0.0\\n for t in range(claim_age, death_age):\\n factor = factors[str(claim_age)] * (1 + cola) ** (t - 62)\\n monthly_payment = pia_monthly * factor\\n total += (12 * monthly_payment) / (1 + discount_rate) ** (t - 62)\\n\\n pv[claim_age][str(death_age)] = round(total, 2)\\n\\n return pv\\n\\ndef calculate_breakeven(pia_monthly, fra_months, cola, claim_ages, max_age, pairs, factors):\\n breakeven = {}\\n\\n for a, b in pairs:\\n if a > b or b > max_age:\\n breakeven[f\\\"{a}-{b}\\\"] = None\\n continue\\n\\n cumulative_a = 0.0\\n cumulative_b = 0.0\\n break_even_month = None\\n\\n for month in range(a * 12, max_age * 12 + 1):\\n year_a = month // 12\\n year_b = month // 12 - (b - a)\\n\\n if year_a < a:\\n continue\\n if year_b < b:\\n continue\\n\\n factor_a = factors[str(a)] * (1 + cola) ** (year_a - 62)\\n factor_b = factors[str(b)] * (1 + cola) ** (year_b - 62)\\n\\n monthly_payment_a = pia_monthly * factor_a\\n monthly_payment_b = pia_monthly * factor_b\\n\\n cumulative_a += monthly_payment_a\\n cumulative_b += monthly_payment_b\\n\\n if month >= b * 12 and cumulative_b >= cumulative_a:\\n break_even_month = month\\n break\\n\\n if break_even_month is not None:\\n breakeven[f\\\"{a}-{b}\\\"] = round(break_even_month / 12, 4)\\n else:\\n breakeven[f\\\"{a}-{b}\\\"] = None\\n\\n return breakeven\\n\\ndef calculate_best_by_pv(pv, death_ages):\\n best_by_pv = {}\\n\\n for death_age in death_ages:\\n max_pv = -1\\n best_age = None\\n\\n for claim_age in pv:\\n if pv[claim_age][str(death_age)] > max_pv:\\n max_pv = pv[claim_age][str(death_age)]\\n best_age = int(claim_age)\\n\\n best_by_pv[str(death_age)] = best_age\\n\\n return best_by_pv\\n\\ndef main():\\n with open('input.json', 'r') as f:\\n data = json.load(f)\\n\\n pia_monthly = data['pia_monthly']\\n fra_months = data['fra_months']\\n reduction_first_36_per_month = data['reduction_first_36_per_month']\\n reduction_beyond_36_per_month = data['reduction_beyond_36_per_month']\\n delayed_credit_per_month = data['delayed_credit_per_month']\\n cola = data['cola']\\n discount_rate = data['discount_rate']\\n claim_ages = data['claim_ages']\\n death_ages = data['death_ages']\\n max_age = data['max_age']\\n pairs = data['pairs']\\n\\n factors, monthly_at_claim = calculate_factors(pia_monthly, fra_months, reduction_first_36_per_month, reduction_beyond_36_per_month, delayed_credit_per_month, cola, discount_rate, claim_ages, death_ages, max_age, pairs)\\n lifetime = calculate_lifetime(pia_monthly, fra_months, cola, claim_ages, death_ages, factors, monthly_at_claim)\\n pv = calculate_pv(pia_monthly, fra_months, cola, discount_rate, claim_ages, death_ages, factors)\\n breakeven = calculate_breakeven(pia_monthly, fra_months, cola, claim_ages, max_age, pairs, factors)\\n best_by_pv = calculate_best_by_pv(pv, death_ages)\\n\\n answer = {\\n \\\"claims\\\": {str(age): {\\n \\\"factor\\\": factors[str(age)],\\n \\\"monthly_at_claim\\\": monthly_at_claim[str(age)],\\n \\\"lifetime\\\": lifetime[age],\\n \\\"pv\\\": pv[age]\\n } for age in claim_ages},\\n \\\"breakeven\\\": breakeven,\\n \\\"best_by_pv\\\": best_by_pv\\n }\\n\\n with open('answer.json', 'w') as f:\\n json.dump(answer, f, indent=4)\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"}"
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"content": "import json\n\n\ndef calculate_factors(pia_monthly, fra_months, reduction_first_36_per_month, reduction_beyond_36_per_month, delayed_credit_per_month, cola, discount_rate, claim_ages, death_ages, max_age, pairs):\n factors = {}\n monthly_at_claim = {}\n\n for age in claim_ages:\n m = age * 12 - fra_months\n if m < 0:\n k = -m\n factor = 1 - (min(k, 36) * reduction_first_36_per_month + max(k - 36, 0) * reduction_beyond_36_per_month)\n else:\n factor = 1 + m * delayed_credit_per_month\n \n factors[str(age)] = round(factor, 6)\n monthly_at_claim[str(age)] = round(pia_monthly * factor, 2)\n\n return factors, monthly_at_claim\n\ndef calculate_lifetime(pia_monthly, fra_months, cola, claim_ages, death_ages, factors, monthly_at_claim):\n lifetime = {age: {} for age in claim_ages}\n\n for claim_age in claim_ages:\n for death_age in death_ages:\n if death_age < claim_age:\n lifetime[claim_age][str(death_age)] = 0.0\n continue\n\n total = 0.0\n for t in range(claim_age, death_age):\n factor = factors[str(claim_age)] * (1 + cola) ** (t - 62)\n monthly_payment = pia_monthly * factor\n total += monthly_payment\n\n lifetime[claim_age][str(death_age)] = round(total, 2)\n\n return lifetime\n\ndef calculate_pv(pia_monthly, fra_months, cola, discount_rate, claim_ages, death_ages, factors):\n pv = {age: {} for age in claim_ages}\n\n for claim_age in claim_ages:\n for death_age in death_ages:\n if death_age < claim_age:\n pv[claim_age][str(death_age)] = 0.0\n continue\n\n total = 0.0\n for t in range(claim_age, death_age):\n factor = factors[str(claim_age)] * (1 + cola) ** (t - 62)\n monthly_payment = pia_monthly * factor\n total += (12 * monthly_payment) / (1 + discount_rate) ** (t - 62)\n\n pv[claim_age][str(death_age)] = round(total, 2)\n\n return pv\n\ndef calculate_breakeven(pia_monthly, fra_months, cola, claim_ages, max_age, pairs, factors):\n breakeven = {}\n\n for a, b in pairs:\n if a > b or b > max_age:\n breakeven[f\"{a}-{b}\"] = None\n continue\n\n cumulative_a = 0.0\n cumulative_b = 0.0\n break_even_month = None\n\n for month in range(a * 12, max_age * 12 + 1):\n year_a = month // 12\n year_b = month // 12 - (b - a)\n\n if year_a < a:\n continue\n if year_b < b:\n continue\n\n factor_a = factors[str(a)] * (1 + cola) ** (year_a - 62)\n factor_b = factors[str(b)] * (1 + cola) ** (year_b - 62)\n\n monthly_payment_a = pia_monthly * factor_a\n monthly_payment_b = pia_monthly * factor_b\n\n cumulative_a += monthly_payment_a\n cumulative_b += monthly_payment_b\n\n if month >= b * 12 and cumulative_b >= cumulative_a:\n break_even_month = month\n break\n\n if break_even_month is not None:\n breakeven[f\"{a}-{b}\"] = round(break_even_month / 12, 4)\n else:\n breakeven[f\"{a}-{b}\"] = None\n\n return breakeven\n\ndef calculate_best_by_pv(pv, death_ages):\n best_by_pv = {}\n\n for death_age in death_ages:\n max_pv = -1\n best_age = None\n\n for claim_age in pv:\n if pv[claim_age][str(death_age)] > max_pv:\n max_pv = pv[claim_age][str(death_age)]\n best_age = int(claim_age)\n\n best_by_pv[str(death_age)] = best_age\n\n return best_by_pv\n\ndef main():\n with open('input.json', 'r') as f:\n data = json.load(f)\n\n pia_monthly = data['pia_monthly']\n fra_months = data['fra_months']\n reduction_first_36_per_month = data['reduction_first_36_per_month']\n reduction_beyond_36_per_month = data['reduction_beyond_36_per_month']\n delayed_credit_per_month = data['delayed_credit_per_month']\n cola = data['cola']\n discount_rate = data['discount_rate']\n claim_ages = data['claim_ages']\n death_ages = data['death_ages']\n max_age = data['max_age']\n pairs = data['pairs']\n\n factors, monthly_at_claim = calculate_factors(pia_monthly, fra_months, reduction_first_36_per_month, reduction_beyond_36_per_month, delayed_credit_per_month, cola, discount_rate, claim_ages, death_ages, max_age, pairs)\n lifetime = calculate_lifetime(pia_monthly, fra_months, cola, claim_ages, death_ages, factors, monthly_at_claim)\n pv = calculate_pv(pia_monthly, fra_months, cola, discount_rate, claim_ages, death_ages, factors)\n breakeven = calculate_breakeven(pia_monthly, fra_months, cola, claim_ages, max_age, pairs, factors)\n best_by_pv = calculate_best_by_pv(pv, death_ages)\n\n answer = {\n \"claims\": {str(age): {\n \"factor\": factors[str(age)],\n \"monthly_at_claim\": monthly_at_claim[str(age)],\n \"lifetime\": lifetime[age],\n \"pv\": pv[age]\n } for age in claim_ages},\n \"breakeven\": breakeven,\n \"best_by_pv\": best_by_pv\n }\n\n with open('answer.json', 'w') as f:\n json.dump(answer, f, indent=4)\n\nif __name__ == \"__main__\":\n main()"
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