Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Required minimum distributions from a supplied divisor table, with and without delaying the first one
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3 · cloud · 2026-10-23 · by @bobsiopener
- Estimated cost
- $0.0015
- Wall time
- 40s
- Human step-ins
- 0
- Outcome
- Fail
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Summary
Exact prompt
# Required minimum distributions from a supplied divisor table, with and without delaying the first one `input.json` describes retirement accounts, yearly growth, a **fictional** life-expectancy divisor table and fictional tax brackets. Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` builds the year-by-year distribution schedule for two choices and compares the tax. Use only the rules below and the input. We re-run it on fresh inputs, so do not hard-code anything. ## Input `birth_year`, `start_age` (first age with a required distribution), `first_year` (first calendar year to simulate), `accounts` (each with `balance` = balance on Dec 31 of the year before `first_year`), `growth` (one yearly return per simulated year; the number of years simulated is its length), `other_income`, `brackets` (`[lower_bound, rate]` list as in a progressive tax), `divisor_table` (age as string -> divisor). ## Rules 1. All accounts are pooled: the balance is the sum of the account balances. 2. For calendar year Y (index j, Y = first_year + j) the age is Y - birth_year. If age >= start_age, the required amount is rmd = (balance on Dec 31 of the previous year) / divisor(age), else 0. For ages above the table's largest age use the largest age's divisor. 3. Withdrawals happen at the start of the year, then the balance grows: end balance = (previous balance - withdrawal) * (1 + growth[j]). 4. **no_delay**: withdrawal = min(balance, rmd) every year. 5. **delay_first**: the first year with rmd > 0 withdraws 0 and that rmd is carried to the next year, which withdraws min(balance, its own rmd + the carried amount). The next year's own rmd still uses its previous Dec 31 balance (which was not reduced by the carried amount). After that, same as no_delay. 6. Tax for a year = tax(other_income + withdrawal) - tax(other_income), using the progressive brackets: rate j applies to income above bound j up to bound j+1. 7. `lower_tax` = "no_delay" or "delay_first" (smaller total tax), or "same" if the totals differ by less than 0.005. 8. Round money to 2 decimals only when writing. ## Output `answer.json`: `{"no_delay": S, "delay_first": S, "lower_tax": ...}` where S = `{"rows": [{"year", "age", "rmd", "withdrawal", "tax", "end_balance"}, ... one per simulated year], "total_withdrawn", "total_tax", "final_balance"}`. ## Scoring Hidden checks (partial credit = fraction passed; all must pass), money within 0.011, years/ages/labels exact: `rmd_amounts_no_delay`, `totals_no_delay`, `delay_first_schedule`, `delay_first_totals_and_choice` on the given input; `fresh_run_1`, `fresh_run_2`: the whole answer on fresh inputs. Each run has a 60-second limit.
Pooled accounts, ages above the table, the carried first distribution that does not reduce the next year's base, and bunching tax under progressive brackets. 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 pricing.json list price). Rescore note (Oct 22, 2026): Re-scored 2026-10-22 from the saved work dir: the original scoring step failed to start (exit 127, scorer interpreter missing), so the first score was not a real result. Model output unchanged; original kept as .orig-scorer127.
These are synthetic test cases for comparing AI models, not financial advice.
Success criteria: 1. rmd_amounts_no_delay 2. totals_no_delay 3. delay_first_schedule 4. delay_first_totals_and_choice 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch 9 case j06-required-distributions (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: 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 (/workspace/usecase-runner/venice_agent2.py) via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max_tokens 16000/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_prices.json list price; paid from the Venice DIEM daily allowance. Harness: venice-mini-agent v1.3 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 0/6. Cost is an ESTIMATE: measured per-call tokens x pricing.json list price (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
- $0.0015
- Wall time
- 40s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 12,477 | 1,193 | $0.0015 |
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, cloud. - It counts as a success only if it meets every success criterion: 1. rmd_amounts_no_delay 2. totals_no_delay 3. delay_first_schedule 4. delay_first_totals_and_choice 5. fresh_run_1 6. 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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"result": "{\n \"note\": \"Fictional divisor table and tax brackets for this exercise.\",\n \"birth_year\": 1957,\n \"start_age\": 75,\n \"first_year\": 2029,\n \"accounts\": [\n {\n \"name\": \"IRA-A\",\n \"balance\": 710000\n },\n {\n \"name\": \"IRA-B\",\n \"balance\": 160000\n }\n ],\n \"growth\": [\n 0.0733,\n 0.0357,\n 0.0433,\n 0.0685,\n 0.1631,\n -0.2086,\n 0.1072,\n 0.0635,\n 0.0877,\n 0.031,\n 0.0769,\n -0.0328,\n 0.2329,\n 0.0818,\n -0.0065,\n -0.0134,\n -0.1601,\n -0.0143,\n 0.1179,\n -0.0468,\n -0.0588,\n 0.0893\n ],\n \"other_income\": 60000,\n \"brackets\": [\n [\n 0,\n 0.0\n ],\n [\n 14280,\n 0.1\n ],\n [\n 40800,\n 0.18\n ],\n [\n 96900,\n 0.26\n ],\n [\n 193800,\n 0.34\n ]\n ],\n \"divisor_table\": {\n \"72\": 27.4,\n \"73\": 26.5,\n \"74\": 25.6,\n \"75\": 24.6,\n \"76\": 23.7,\n \"77\": 22.9,\n \"78\": 22.0,\n \"79\": 21.1,\n \"80\": 20.2,\n \"81\": 19.4,\n \"82\": 18.5,\n \"83\": 17.7,\n \"84\": 16.8,\n \"85\": 16.0,\n \"86\": 15.2,\n \"87\": 14.3,\n \"88\": 13.5,\n \"89\": 12.7,\n \"90\": 12.0,\n \"91\": 11.2,\n \"92\": 10.4,\n \"93\": 9.6,\n \"94\": 8.9,\n \"95\": 8.1,\n \"96\": 7.4,\n \"97\": 6.6,\n \"98\": 5.9,\n \"99\": 5.2,\n \"100\": 4.5,\n \"101\": 3.8,\n \"102\": 3.1,\n \"103\": 2.4,\n \"104\": 2.0,\n \"105\": 2.0\n }\n}"
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\r\\nimport sys\\r\\n\\r\\ndef calculate_tax(income, brackets):\\r\\n tax = 0.0\\r\\n prev_bound = 0.0\\r\\n for bound, rate in brackets:\\r\\n taxable_income = min(income, bound) - prev_bound\\r\\n tax += taxable_income * rate\\r\\n if income <= bound:\\r\\n break\\r\\n prev_bound = bound\\r\\n return tax\\r\\n\\r\\ndef calculate_rmd(balance, age, divisor_table, max_age):\\r\\n if age < 75:\\r\\n return 0.0\\r\\n divisor = divisor_table.get(str(max_age), divisor_table[str(max_age - 1)])\\r\\n return balance / divisor\\r\\n\\r\\ndef simulate_withdrawals(input_data, delay_first):\\r\\n birth_year = input_data['birth_year']\\r\\n start_age = input_data['start_age']\\r\\n first_year = input_data['first_year']\\r\\n accounts = input_data['accounts']\\r\\n growth = input_data['growth']\\r\\n other_income = input_data['other_income']\\r\\n brackets = input_data['brackets']\\r\\n divisor_table = input_data['divisor_table']\\r\\n\\r\\n total_balance = sum(account['balance'] for account in accounts)\\r\\n rows = []\\r\\n total_withdrawn = 0.0\\r\\n total_tax = 0.0\\r\\n carried_rmd = 0.0\\r\\n\\r\\n for j in range(len(growth)):\\r\\n year = first_year + j\\r\\n age = year - birth_year\\r\\n max_age = max(int(k) for k in divisor_table.keys())\\r\\n rmd = calculate_rmd(total_balance, age, divisor_table, max_age)\\r\\n\\r\\n if age < start_age:\\r\\n withdrawal = 0.0\\r\\n else:\\r\\n if delay_first and carried_rmd == 0.0 and rmd > 0.0:\\r\\n carried_rmd = rmd\\r\\n withdrawal = 0.0\\r\\n else:\\r\\n withdrawal = min(total_balance, rmd + carried_rmd)\\r\\n carried_rmd = 0.0\\r\\n\\r\\n tax = calculate_tax(other_income + withdrawal, brackets) - calculate_tax(other_income, brackets)\\r\\n total_withdrawn += withdrawal\\r\\n total_tax += tax\\r\\n\\r\\n total_balance = (total_balance - withdrawal) * (1 + growth[j])\\r\\n\\r\\n rows.append({\\r\\n 'year': year,\\r\\n 'age': age,\\r\\n 'rmd': round(rmd, 2),\\r\\n 'withdrawal': round(withdrawal, 2),\\r\\n 'tax': round(tax, 2),\\r\\n 'end_balance': round(total_balance, 2)\\r\\n })\\r\\n\\r\\n return {\\r\\n 'rows': rows,\\r\\n 'total_withdrawn': round(total_withdrawn, 2),\\r\\n 'total_tax': round(total_tax, 2),\\r\\n 'final_balance': round(total_balance, 2)\\r\\n }\\r\\n\\r\\ndef main():\\r\\n if len(sys.argv) != 3:\\r\\n print(\\\"Usage: python solve.py input.json answer.json\\\")\\r\\n return\\r\\n\\r\\n with open(sys.argv[1], 'r') as f:\\r\\n input_data = json.load(f)\\r\\n\\r\\n no_delay = simulate_withdrawals(input_data, False)\\r\\n delay_first = simulate_withdrawals(input_data, True)\\r\\n\\r\\n if abs(no_delay['total_tax'] - delay_first['total_tax']) < 0.005:\\r\\n lower_tax = \\\"same\\\"\\r\\n elif no_delay['total_tax'] < delay_first['total_tax']:\\r\\n lower_tax = \\\"no_delay\\\"\\r\\n else:\\r\\n lower_tax = \\\"delay_first\\\"\\r\\n\\r\\n answer = {\\r\\n 'no_delay': no_delay,\\r\\n 'delay_first': delay_first,\\r\\n 'lower_tax': lower_tax\\r\\n }\\r\\n\\r\\n with open(sys.argv[2], 'w') as f:\\r\\n json.dump(answer, f, indent=4)\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n main()\"}"
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"content": "import json\r\nimport sys\r\n\r\ndef calculate_tax(income, brackets):\r\n tax = 0.0\r\n prev_bound = 0.0\r\n for bound, rate in brackets:\r\n taxable_income = min(income, bound) - prev_bound\r\n tax += taxable_income * rate\r\n if income <= bound:\r\n break\r\n prev_bound = bound\r\n return tax\r\n\r\ndef calculate_rmd(balance, age, divisor_table, max_age):\r\n if age < 75:\r\n return 0.0\r\n divisor = divisor_table.get(str(max_age), divisor_table[str(max_age - 1)])\r\n return balance / divisor\r\n\r\ndef simulate_withdrawals(input_data, delay_first):\r\n birth_year = input_data['birth_year']\r\n start_age = input_data['start_age']\r\n first_year = input_data['first_year']\r\n accounts = input_data['accounts']\r\n growth = input_data['growth']\r\n other_income = input_data['other_income']\r\n brackets = input_data['brackets']\r\n divisor_table = input_data['divisor_table']\r\n\r\n total_balance = sum(account['balance'] for account in accounts)\r\n rows = []\r\n total_withdrawn = 0.0\r\n total_tax = 0.0\r\n carried_rmd = 0.0\r\n\r\n for j in range(len(growth)):\r\n year = first_year + j\r\n age = year - birth_year\r\n max_age = max(int(k) for k in divisor_table.keys())\r\n rmd = calculate_rmd(total_balance, age, divisor_table, max_age)\r\n\r\n if age < start_age:\r\n withdrawal = 0.0\r\n else:\r\n if delay_first and carried_rmd == 0.0 and rmd > 0.0:\r\n carried_rmd = rmd\r\n withdrawal = 0.0\r\n else:\r\n withdrawal = min(total_balance, rmd + carried_rmd)\r\n carried_rmd = 0.0\r\n\r\n tax = calculate_tax(other_income + withdrawal, brackets) - calculate_tax(other_income, brackets)\r\n total_withdrawn += withdrawal\r\n total_tax += tax\r\n\r\n total_balance = (total_balance - withdrawal) * (1 + growth[j])\r\n\r\n rows.append({\r\n 'year': year,\r\n 'age': age,\r\n 'rmd': round(rmd, 2),\r\n 'withdrawal': round(withdrawal, 2),\r\n 'tax': round(tax, 2),\r\n 'end_balance': round(total_balance, 2)\r\n })\r\n\r\n return {\r\n 'rows': rows,\r\n 'total_withdrawn': round(total_withdrawn, 2),\r\n 'total_tax': round(total_tax, 2),\r\n 'final_balance': round(total_balance, 2)\r\n }\r\n\r\ndef main():\r\n if len(sys.argv) != 3:\r\n print(\"Usage: python solve.py input.json answer.json\")\r\n return\r\n\r\n with open(sys.argv[1], 'r') as f:\r\n input_data = json.load(f)\r\n\r\n no_delay = simulate_withdrawals(input_data, False)\r\n delay_first = simulate_withdrawals(input_data, True)\r\n\r\n if abs(no_delay['total_tax'] - delay_first['total_tax']) < 0.005:\r\n lower_tax = \"same\"\r\n elif no_delay['total_tax'] < delay_first['total_tax']:\r\n lower_tax = \"no_delay\"\r\n else:\r\n lower_tax = \"delay_first\"\r\n\r\n answer = {\r\n 'no_delay': no_delay,\r\n 'delay_first': delay_first,\r\n 'lower_tax': lower_tax\r\n }\r\n\r\n with open(sys.argv[2], 'w') as f:\r\n json.dump(answer, f, indent=4)\r\n\r\nif __name__ == \"__main__\":\r\n main()"
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