Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Mortgage amortization with extra monthly payments and lump sums
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-27 · by @bobsiopener
- Estimated cost
- $0.0030
- Wall time
- 37s
- Human step-ins
- 0
- Outcome
- Pass
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Superseded. This run was rescored on 2026-10-28 and replaced by a newer run. It no longer counts toward rankings, use-case tables or the data export; it is kept here for the record.
Summary
Exact prompt
# Mortgage amortization with extra monthly payments and lump sums `input.json` describes a fixed-rate loan plus planned prepayments (synthetic). Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` produces the payment, the baseline payoff, and the full schedule with prepayments. We re-run it on fresh inputs, so do not hard-code anything. ## Input `principal`, `annual_rate` (percent, e.g. 6.125), `years`, `extra_monthly`, `extra_start_month` (first month the extra is paid; months count from 1), `lump_sums` (list of `{"month", "amount"}`; several can fall in the same month and then add up). ## Rules 1. Money is exact cents with `decimal.Decimal` and ROUND_HALF_UP to 0.01 (convert input numbers with `Decimal(str(x))`). 2. Scheduled payment: with float r = annual_rate / 1200 and n = years * 12, x = principal * r / (1 - (1 + r) ** -n) computed in floats; payment = `Decimal(str(x))` rounded to cents. 3. Each month m: interest = round(balance * annual_rate / 1200) (in Decimal). Prepayment = extra_monthly if m >= extra_start_month (else 0) plus the lump sums for month m. If payment + prepayment >= balance + interest, this is the last month: the amount paid is balance + interest and the balance becomes 0. Otherwise the amount paid is payment + prepayment and the balance drops by (amount paid - interest). 4. The baseline is the same loan with no extra and no lump sums. 5. `interest_saved` = baseline total interest - total interest with prepayments; `months_saved` = baseline months - months with prepayments. ## Output `answer.json`: ``` {"payment": ..., "baseline": {"months": ..., "total_interest": ..., "last_payment": ...}, "with_extra": {"months": ..., "total_interest": ..., "schedule": [[month, amount_paid, interest, principal_paid, balance_after], ...]}, "interest_saved": ..., "months_saved": ...} ``` One schedule row per month, money as numbers with 2 decimals. ## 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="balance">` drawing the with-extra remaining balance by month. ## 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 within 0.011, counts exact: `payment`, `baseline`, `schedule_rows` (every row), `savings` on the given input; `fresh_run_1`, `fresh_run_2` (the whole answer on fresh inputs; one has two lump sums in the same month). Each run has a 60-second limit.
Every schedule row is compared: payment rounding, interest rounding each month, extra start month, same-month lump sums that add up, and the short final payment. Inspired by https://x.com/girlinAI/status/2079167648093622450 Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x pricing.json list price).
These are synthetic test cases for comparing AI models, not financial advice.
Success criteria: 1. payment 2. baseline 3. schedule_rows 4. savings 5. fresh_run_1 6. fresh_run_2 7. chart_svg_static
Setup notes: Use Case Scout batch 9 case j08-amortization-extra-payments (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. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Harness: venice-mini-agent v1.3.1 (/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 $0.0087, 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 v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.0087 run cap). Score: 7/7. 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.
Output
Metrics
- Estimated cost
- $0.0030
- Wall time
- 37s
- Human step-ins
- 0
- Turns
- 9
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 24,676 | 2,643 | $0.0030 |
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. payment 2. baseline 3. schedule_rows 4. savings 5. fresh_run_1 6. fresh_run_2 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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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\nfrom decimal import Decimal, ROUND_HALF_UP\\nimport math\\n\\ndef compute_payment(principal, annual_rate, years):\\n r = annual_rate / 1200\\n n = years * 12\\n x = principal * r / (1 - (1 + r) ** -n)\\n return Decimal(str(x)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n\\ndef compute_amortization_schedule(principal, annual_rate, years, extra_monthly=0, extra_start_month=1, lump_sums=None):\\n if lump_sums is None:\\n lump_sums = []\\n payment = compute_payment(principal, annual_rate, years)\\n balance = principal\\n schedule = []\\n total_paid = 0\\n total_interest = 0\\n month = 1\\n while balance > 0:\\n interest = Decimal(str(balance * annual_rate / 1200)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n prepayment = extra_monthly if month >= extra_start_month else 0\\n for ls in lump_sums:\\n if ls['month'] == month:\\n prepayment += ls['amount']\\n amount_paid = payment + prepayment\\n if amount_paid >= balance + interest:\\n amount_paid = balance + interest\\n principal_paid = amount_paid - interest\\n balance -= principal_paid\\n if balance < 0:\\n balance = 0\\n schedule.append([month, float(amount_paid), float(interest), float(principal_paid), float(balance)])\\n total_paid += amount_paid\\n total_interest += interest\\n month += 1\\n return schedule, total_interest, month - 1, payment\\n\\ndef compute_baseline(principal, annual_rate, years):\\n schedule, total_interest, months, payment = compute_amortization_schedule(principal, annual_rate, years)\\n last_payment = schedule[-1][1]\\n return {'months': months, 'total_interest': float(total_interest), 'last_payment': last_payment}\\n\\ndef compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums):\\n schedule, total_interest, months, payment = compute_amortization_schedule(\\n principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\\n return {'months': months, 'total_interest': float(total_interest), 'schedule': schedule}\\n\\ndef compute_savings(baseline, with_extra):\\n return {\\n 'interest_saved': baseline['total_interest'] - with_extra['total_interest'],\\n 'months_saved': baseline['months'] - with_extra['months']\\n }\\n\\ndef generate_chart(schedule):\\n svg_content = \\\"\\\"\\\"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n<svg width=\\\"800\\\" height=\\\"600\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n<polyline id=\\\"balance\\\" fill=\\\"none\\\" stroke=\\\"black\\\" stroke-width=\\\"2\\\" points=\\\"\\\"\\n\\\"\\\"\\\"\\n points = \\\"\\\"\\n for month, amount_paid, interest, principal_paid, balance_after in schedule:\\n points += f\\\"{month * 10}, {600 - balance_after * 600 / 300000} \\\" # Scale balance to fit in the SVG\\n svg_content += points + \\\"\\\"\\\"\\n\\\" />\\n</svg>\\n\\\"\\\"\\\"\\n return svg_content\\n\\ndef main(input_file, output_file):\\n with open(input_file) as f:\\n input_data = json.load(f)\\n principal = input_data['principal']\\n annual_rate = input_data['annual_rate']\\n years = input_data['years']\\n extra_monthly = input_data.get('extra_monthly', 0)\\n extra_start_month = input_data.get('extra_start_month', 1)\\n lump_sums = input_data.get('lump_sums', [])\\n\\n baseline = compute_baseline(principal, annual_rate, years)\\n with_extra = compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\\n savings = compute_savings(baseline, with_extra)\\n\\n payment = with_extra['schedule'][0][1] # Payment is the same as the first month's amount paid\\n\\n output_data = {\\n 'payment': payment,\\n 'baseline': baseline,\\n 'with_extra': with_extra,\\n 'interest_saved': savings['interest_saved'],\\n 'months_saved': savings['months_saved']\\n }\\n\\n with open(output_file, 'w') as f:\\n json.dump(output_data, f, indent=4)\\n\\n chart_content = generate_chart(with_extra['schedule'])\\n with open('chart.svg', 'w') as f:\\n f.write(chart_content)\\n\\nif __name__ == '__main__':\\n import sys\\n if len(sys.argv) != 3:\\n print(\\\"Usage: python solve.py input.json answer.json\\\")\\n sys.exit(1)\\n main(sys.argv[1], sys.argv[2])\"}"
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"content": "import json\nfrom decimal import Decimal, ROUND_HALF_UP\nimport math\n\ndef compute_payment(principal, annual_rate, years):\n r = annual_rate / 1200\n n = years * 12\n x = principal * r / (1 - (1 + r) ** -n)\n return Decimal(str(x)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n\ndef compute_amortization_schedule(principal, annual_rate, years, extra_monthly=0, extra_start_month=1, lump_sums=None):\n if lump_sums is None:\n lump_sums = []\n payment = compute_payment(principal, annual_rate, years)\n balance = principal\n schedule = []\n total_paid = 0\n total_interest = 0\n month = 1\n while balance > 0:\n interest = Decimal(str(balance * annual_rate / 1200)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n prepayment = extra_monthly if month >= extra_start_month else 0\n for ls in lump_sums:\n if ls['month'] == month:\n prepayment += ls['amount']\n amount_paid = payment + prepayment\n if amount_paid >= balance + interest:\n amount_paid = balance + interest\n principal_paid = amount_paid - interest\n balance -= principal_paid\n if balance < 0:\n balance = 0\n schedule.append([month, float(amount_paid), float(interest), float(principal_paid), float(balance)])\n total_paid += amount_paid\n total_interest += interest\n month += 1\n return schedule, total_interest, month - 1, payment\n\ndef compute_baseline(principal, annual_rate, years):\n schedule, total_interest, months, payment = compute_amortization_schedule(principal, annual_rate, years)\n last_payment = schedule[-1][1]\n return {'months': months, 'total_interest': float(total_interest), 'last_payment': last_payment}\n\ndef compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums):\n schedule, total_interest, months, payment = compute_amortization_schedule(\n principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\n return {'months': months, 'total_interest': float(total_interest), 'schedule': schedule}\n\ndef compute_savings(baseline, with_extra):\n return {\n 'interest_saved': baseline['total_interest'] - with_extra['total_interest'],\n 'months_saved': baseline['months'] - with_extra['months']\n }\n\ndef generate_chart(schedule):\n svg_content = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<svg width=\"800\" height=\"600\" xmlns=\"http://www.w3.org/2000/svg\">\n<polyline id=\"balance\" fill=\"none\" stroke=\"black\" stroke-width=\"2\" points=\"\"\n\"\"\"\n points = \"\"\n for month, amount_paid, interest, principal_paid, balance_after in schedule:\n points += f\"{month * 10}, {600 - balance_after * 600 / 300000} \" # Scale balance to fit in the SVG\n svg_content += points + \"\"\"\n\" />\n</svg>\n\"\"\"\n return svg_content\n\ndef main(input_file, output_file):\n with open(input_file) as f:\n input_data = json.load(f)\n principal = input_data['principal']\n annual_rate = input_data['annual_rate']\n years = input_data['years']\n extra_monthly = input_data.get('extra_monthly', 0)\n extra_start_month = input_data.get('extra_start_month', 1)\n lump_sums = input_data.get('lump_sums', [])\n\n baseline = compute_baseline(principal, annual_rate, years)\n with_extra = compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\n savings = compute_savings(baseline, with_extra)\n\n payment = with_extra['schedule'][0][1] # Payment is the same as the first month's amount paid\n\n output_data = {\n 'payment': payment,\n 'baseline': baseline,\n 'with_extra': with_extra,\n 'interest_saved': savings['interest_saved'],\n 'months_saved': savings['months_saved']\n }\n\n with open(output_file, 'w') as f:\n json.dump(output_data, f, indent=4)\n\n chart_content = generate_chart(with_extra['schedule'])\n with open('chart.svg', 'w') as f:\n f.write(chart_content)\n\nif __name__ == '__main__':\n import sys\n if len(sys.argv) != 3:\n print(\"Usage: python solve.py input.json answer.json\")\n sys.exit(1)\n main(sys.argv[1], sys.argv[2])"
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"result": "exit=1\nstdout:\n\nstderr:\nTraceback (most recent call last):\n File \"./usecase-runner/runs/j08-mistral-small-3-2-24b-instruct/work/solve.py\", line 105, in <module>\n main(sys.argv[1], sys.argv[2])\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"./usecase-runner/runs/j08-mistral-small-3-2-24b-instruct/work/solve.py\", line 79, in main\n baseline = compute_baseline(principal, annual_rate, years)\n File \"./usecase-runner/runs/j08-mistral-small-3-2-24b-instruct/work/solve.py\", line 40, in compute_baseline\n schedule, total_interest, months, payment = compute_amortization_schedule(principal, annual_rate, years)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"./usecase-runner/runs/j08-mistral-small-3-2-24b-instruct/work/solve.py\", line 21, in compute_amortization_schedule\n interest = Decimal(str(balance * annual_rate / 1200)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n ~~~~~~~~^~~~~~~~~~~~~\nTypeError: unsupported operand type(s) for *: 'decimal.Decimal' and 'float'\n"
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\nfrom decimal import Decimal, ROUND_HALF_UP\\nimport math\\n\\ndef compute_payment(principal, annual_rate, years):\\n r = annual_rate / 1200\\n n = years * 12\\n x = principal * r / (1 - (1 + r) ** -n)\\n return Decimal(str(x)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n\\ndef compute_amortization_schedule(principal, annual_rate, years, extra_monthly=0, extra_start_month=1, lump_sums=None):\\n if lump_sums is None:\\n lump_sums = []\\n payment = compute_payment(principal, annual_rate, years)\\n balance = Decimal(str(principal))\\n schedule = []\\n total_paid = 0\\n total_interest = 0\\n month = 1\\n while balance > 0:\\n interest = Decimal(str(float(balance) * annual_rate / 1200)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n prepayment = extra_monthly if month >= extra_start_month else 0\\n for ls in lump_sums:\\n if ls['month'] == month:\\n prepayment += ls['amount']\\n amount_paid = payment + prepayment\\n if amount_paid >= balance + interest:\\n amount_paid = balance + interest\\n principal_paid = amount_paid - interest\\n balance -= principal_paid\\n if balance < 0:\\n balance = 0\\n schedule.append([month, float(amount_paid), float(interest), float(principal_paid), float(balance)])\\n total_paid += amount_paid\\n total_interest += interest\\n month += 1\\n return schedule, total_interest, month - 1, payment\\n\\ndef compute_baseline(principal, annual_rate, years):\\n schedule, total_interest, months, payment = compute_amortization_schedule(principal, annual_rate, years)\\n last_payment = schedule[-1][1]\\n return {'months': months, 'total_interest': float(total_interest), 'last_payment': last_payment}\\n\\ndef compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums):\\n schedule, total_interest, months, payment = compute_amortization_schedule(\\n principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\\n return {'months': months, 'total_interest': float(total_interest), 'schedule': schedule}\\n\\ndef compute_savings(baseline, with_extra):\\n return {\\n 'interest_saved': baseline['total_interest'] - with_extra['total_interest'],\\n 'months_saved': baseline['months'] - with_extra['months']\\n }\\n\\ndef generate_chart(schedule):\\n svg_content = \\\"\\\"\\\"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n<svg width=\\\"800\\\" height=\\\"600\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n<polyline id=\\\"balance\\\" fill=\\\"none\\\" stroke=\\\"black\\\" stroke-width=\\\"2\\\" points=\\\"\\\"\\n\\\"\\\"\\\"\\n points = \\\"\\\"\\n for month, amount_paid, interest, principal_paid, balance_after in schedule:\\n points += f\\\"{month * 10}, {600 - balance_after * 600 / 300000} \\\" # Scale balance to fit in the SVG\\n svg_content += points + \\\"\\\"\\\"\\n\\\" />\\n</svg>\\n\\\"\\\"\\\"\\n return svg_content\\n\\ndef main(input_file, output_file):\\n with open(input_file) as f:\\n input_data = json.load(f)\\n principal = input_data['principal']\\n annual_rate = input_data['annual_rate']\\n years = input_data['years']\\n extra_monthly = input_data.get('extra_monthly', 0)\\n extra_start_month = input_data.get('extra_start_month', 1)\\n lump_sums = input_data.get('lump_sums', [])\\n\\n baseline = compute_baseline(principal, annual_rate, years)\\n with_extra = compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\\n savings = compute_savings(baseline, with_extra)\\n\\n payment = with_extra['schedule'][0][1] # Payment is the same as the first month's amount paid\\n\\n output_data = {\\n 'payment': payment,\\n 'baseline': baseline,\\n 'with_extra': with_extra,\\n 'interest_saved': savings['interest_saved'],\\n 'months_saved': savings['months_saved']\\n }\\n\\n with open(output_file, 'w') as f:\\n json.dump(output_data, f, indent=4)\\n\\n chart_content = generate_chart(with_extra['schedule'])\\n with open('chart.svg', 'w') as f:\\n f.write(chart_content)\\n\\nif __name__ == '__main__':\\n import sys\\n if len(sys.argv) != 3:\\n print(\\\"Usage: python solve.py input.json answer.json\\\")\\n sys.exit(1)\\n main(sys.argv[1], sys.argv[2])\"}"
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"content": "import json\nfrom decimal import Decimal, ROUND_HALF_UP\nimport math\n\ndef compute_payment(principal, annual_rate, years):\n r = annual_rate / 1200\n n = years * 12\n x = principal * r / (1 - (1 + r) ** -n)\n return Decimal(str(x)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n\ndef compute_amortization_schedule(principal, annual_rate, years, extra_monthly=0, extra_start_month=1, lump_sums=None):\n if lump_sums is None:\n lump_sums = []\n payment = compute_payment(principal, annual_rate, years)\n balance = Decimal(str(principal))\n schedule = []\n total_paid = 0\n total_interest = 0\n month = 1\n while balance > 0:\n interest = Decimal(str(float(balance) * annual_rate / 1200)).quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n prepayment = extra_monthly if month >= extra_start_month else 0\n for ls in lump_sums:\n if ls['month'] == month:\n prepayment += ls['amount']\n amount_paid = payment + prepayment\n if amount_paid >= balance + interest:\n amount_paid = balance + interest\n principal_paid = amount_paid - interest\n balance -= principal_paid\n if balance < 0:\n balance = 0\n schedule.append([month, float(amount_paid), float(interest), float(principal_paid), float(balance)])\n total_paid += amount_paid\n total_interest += interest\n month += 1\n return schedule, total_interest, month - 1, payment\n\ndef compute_baseline(principal, annual_rate, years):\n schedule, total_interest, months, payment = compute_amortization_schedule(principal, annual_rate, years)\n last_payment = schedule[-1][1]\n return {'months': months, 'total_interest': float(total_interest), 'last_payment': last_payment}\n\ndef compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums):\n schedule, total_interest, months, payment = compute_amortization_schedule(\n principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\n return {'months': months, 'total_interest': float(total_interest), 'schedule': schedule}\n\ndef compute_savings(baseline, with_extra):\n return {\n 'interest_saved': baseline['total_interest'] - with_extra['total_interest'],\n 'months_saved': baseline['months'] - with_extra['months']\n }\n\ndef generate_chart(schedule):\n svg_content = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<svg width=\"800\" height=\"600\" xmlns=\"http://www.w3.org/2000/svg\">\n<polyline id=\"balance\" fill=\"none\" stroke=\"black\" stroke-width=\"2\" points=\"\"\n\"\"\"\n points = \"\"\n for month, amount_paid, interest, principal_paid, balance_after in schedule:\n points += f\"{month * 10}, {600 - balance_after * 600 / 300000} \" # Scale balance to fit in the SVG\n svg_content += points + \"\"\"\n\" />\n</svg>\n\"\"\"\n return svg_content\n\ndef main(input_file, output_file):\n with open(input_file) as f:\n input_data = json.load(f)\n principal = input_data['principal']\n annual_rate = input_data['annual_rate']\n years = input_data['years']\n extra_monthly = input_data.get('extra_monthly', 0)\n extra_start_month = input_data.get('extra_start_month', 1)\n lump_sums = input_data.get('lump_sums', [])\n\n baseline = compute_baseline(principal, annual_rate, years)\n with_extra = compute_with_extra(principal, annual_rate, years, extra_monthly, extra_start_month, lump_sums)\n savings = compute_savings(baseline, with_extra)\n\n payment = with_extra['schedule'][0][1] # Payment is the same as the first month's amount paid\n\n output_data = {\n 'payment': payment,\n 'baseline': baseline,\n 'with_extra': with_extra,\n 'interest_saved': savings['interest_saved'],\n 'months_saved': savings['months_saved']\n }\n\n with open(output_file, 'w') as f:\n json.dump(output_data, f, indent=4)\n\n chart_content = generate_chart(with_extra['schedule'])\n with open('chart.svg', 'w') as f:\n f.write(chart_content)\n\nif __name__ == '__main__':\n import sys\n if len(sys.argv) != 3:\n print(\"Usage: python solve.py input.json answer.json\")\n sys.exit(1)\n main(sys.argv[1], sys.argv[2])"
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