Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Read a synthetic annual-report excerpt and compute financial ratios
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-25 · by @bobsiopener
- Estimated cost
- $0.0011
- Wall time
- 40s
- Human step-ins
- 0
- Outcome
- Partial
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Summary
Exact prompt
# Read an annual-report excerpt and compute financial ratios `annual_report_excerpt.md` is an excerpt from a **fictional** company's annual report (statements plus notes). Read it and write `ratios.json` with the 16 values below. You may compute by hand or with a script (Python 3 standard library only). Use only this document. Watch the units and the notes. ## Rules 1. Dollar amounts in the statements are in thousands unless a line says otherwise. Notes can state amounts in millions; convert them to thousands. 2. Where a note restates a prior-year amount, use the restated amount everywhere that amount (or a total that includes it) is used. "Average X" = (FY2025 year-end X + FY2024 year-end X) / 2. 3. Total debt = current portion of long-term debt + long-term debt (net of current portion) + finance lease obligations (Note 7). Operating lease liabilities are not debt. 4. Free cash flow = net cash provided by operating activities - purchases of property and equipment - capitalized internal-use software. 5. Definitions (FY2025 unless stated): - `current_ratio` = total current assets / total current liabilities - `quick_ratio` = (cash and equivalents + short-term investments + accounts receivable) / total current liabilities - `gross_margin`, `operating_margin`, `net_margin` = gross profit, operating income, net income / net sales - `effective_tax_rate` = provision for income taxes / income before income taxes - `roe` = net income / average stockholders' equity - `total_debt_thousands` (rule 3, at Dec 31, 2025), `debt_to_equity` = total debt / Dec 31, 2025 equity - `interest_coverage` = operating income / interest expense - `fcf_thousands` (rule 4), `fcf_margin` = free cash flow / net sales - `inventory_turnover` = cost of sales / average inventory - `dso_days` = average accounts receivable / net sales * 365 - `revenue_growth` = FY2025 net sales / FY2024 net sales - 1 - `eps_diluted` = net income / diluted weighted-average shares (dollars per share) 6. Ratios and margins are plain fractions (0.25, not 25%). ## Output `ratios.json`: one JSON object with exactly these keys: `current_ratio`, `quick_ratio`, `gross_margin`, `operating_margin`, `net_margin`, `effective_tax_rate`, `roe`, `total_debt_thousands`, `debt_to_equity`, `interest_coverage`, `fcf_thousands`, `fcf_margin`, `inventory_turnover`, `dso_days`, `revenue_growth`, `eps_diluted`. (That is 16 keys: the 15 ratios plus total debt.) ## Scoring Hidden checks (partial credit = fraction passed; all must pass). Tolerances: fractions and ratios within 0.0005; `total_debt_thousands` and `fcf_thousands` within 0.5; `dso_days` within 0.05; `eps_diluted` within 0.005. - `liquidity`: current_ratio, quick_ratio - `margins_and_tax`: gross_margin, operating_margin, net_margin, effective_tax_rate - `return_on_equity`: roe (needs the restated equity) - `debt_and_coverage`: total_debt_thousands, debt_to_equity, interest_coverage (needs the note in millions) - `free_cash_flow`: fcf_thousands, fcf_margin - `efficiency`: inventory_turnover (needs the restated inventory), dso_days - `growth_and_eps`: revenue_growth, eps_diluted
A restated prior-year inventory and equity in a note, finance-lease debt stated in millions inside a note, a non-GAAP distractor, and averages that need the restated column. Inspired by https://news.ycombinator.com/item?id=47766370 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. liquidity 2. margins_and_tax 3. return_on_equity 4. debt_and_coverage 5. free_cash_flow 6. efficiency 7. growth_and_eps
Setup notes: Use Case Scout batch 9 case j09-10k-ratio-extraction (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 v1.3.1 (/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 $0.005, 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. first_try_success corrected to false on 2026-10-25: a harness bug (venice_x/vcc.py finalize did not tie it to success) had set it true on failed runs; scores and measurements unchanged. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.005 run cap). Score: 5/7. 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.
Metrics
- Estimated cost
- $0.0011
- Wall time
- 40s
- Human step-ins
- 0
- Turns
- 3
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 7,977 | 1,393 | $0.0011 |
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. liquidity 2. margins_and_tax 3. return_on_equity 4. debt_and_coverage 5. free_cash_flow 6. efficiency 7. growth_and_eps
- 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": "# Larkspur Outdoor Goods, Inc. - Annual Report excerpt (fiscal year ended December 31, 2025)\n\n*Fictional company. All figures are synthetic and were made up for this exercise.*\n\n## Item 7. Management's Discussion and Analysis (excerpt)\n\nNet sales grew on the strength of the trail-footwear line and the reopening of two distribution centers. Management also tracks a non-GAAP \"adjusted operating income\" of $112,400 thousand, which adds back restructuring and brand amortization; this measure is not used anywhere in the GAAP statements below.\n\nSegment net sales (in thousands): Footwear 391,220; Apparel 286,905; Equipment 164,490. Segment figures are shown for context only.\n\n## Item 8. Financial Statements\n\n### Consolidated Statements of Operations\n(in thousands, except per-share amounts)\n\n| | FY2025 | FY2024 |\n|---|---:|---:|\n| Net sales | 842,615 | 771,302 |\n| Cost of sales | 503,977 | 468,215 |\n| Gross profit | 338,638 | 303,087 |\n| Selling, general and administrative | 221,450 | 204,118 |\n| Research and development | 18,906 | 17,240 |\n| Operating income | 98,282 | 81,729 |\n| Interest expense | (9,874) | (10,512) |\n| Other income, net | 1,240 | 655 |\n| Income before income taxes | 89,648 | 71,872 |\n| Provision for income taxes | 19,723 | 15,812 |\n| Net income | 69,925 | 56,060 |\n| Weighted-average shares, basic (thousands) | 41,200 | 41,650 |\n| Weighted-average shares, diluted (thousands) | 42,050 | 42,300 |\n\n### Consolidated Balance Sheets\n(in thousands; FY2024 column as originally reported, see Note 4)\n\n| | Dec 31, 2025 | Dec 31, 2024 |\n|---|---:|---:|\n| Cash and cash equivalents | 61,340 | 48,905 |\n| Short-term investments | 15,000 | 22,500 |\n| Accounts receivable, net | 88,412 | 79,630 |\n| Inventories | 142,780 | 131,455 |\n| Prepaid expenses and other | 9,118 | 8,240 |\n| **Total current assets** | 316,650 | 290,730 |\n| Property and equipment, net | 204,500 | 196,300 |\n| Operating lease right-of-use assets | 38,200 | 41,000 |\n| Goodwill | 55,000 | 55,000 |\n| **Total assets** | 614,350 | 583,030 |\n| Accounts payable | 70,215 | 66,980 |\n| Accrued liabilities | 41,320 | 38,770 |\n| Current portion of long-term debt | 12,500 | 12,500 |\n| Current operating lease liabilities | 7,900 | 7,600 |\n| **Total current liabilities** | 131,935 | 125,850 |\n| Long-term debt, net of current portion | 148,000 | 160,500 |\n| Noncurrent operating lease liabilities | 32,400 | 35,600 |\n| Other long-term liabilities | 14,615 | 12,880 |\n| **Total stockholders' equity** | 287,400 | 248,200 |\n| **Total liabilities and equity** | 614,350 | 583,030 |\n\n### Consolidated Statement of Cash Flows (summary)\n(in thousands)\n\n| | FY2025 |\n|---|---:|\n| Net cash provided by operating activities | 112,640 |\n| Purchases of property and equipment | (38,975) |\n| Capitalized internal-use software | (4,100) |\n| Purchases of short-term investments, net of maturities | 7,500 |\n| Repayment of long-term debt | (12,500) |\n| Share repurchases | (29,580) |\n| Dividends paid | (14,920) |\n\n## Notes to th"
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"arguments": "{\"command\": \"python3 -c \\\"import json\\n\\n# Read the annual report excerpt\\nwith open('annual_report_excerpt.md', 'r') as file:\\n annual_report = file.read()\\n\\n# Extract financial data from the annual report excerpt\\n# (This is a simplified approach; in a real scenario, you might need a more robust parsing method)\\n\\n# Consolidated Statements of Operations\\ngross_profit_2025 = 338638\\nnet_sales_2025 = 842615\\nselling_general_admin_2025 = 221450\\nresearch_development_2025 = 18906\\noperating_income_2025 = 98282\\ninterest_expense_2025 = 9874\\nother_income_net_2025 = 1240\\nincome_before_taxes_2025 = 89648\\nprovision_for_income_taxes_2025 = 19723\\nnet_income_2025 = 69925\\nweighted_average_shares_diluted_2025 = 42050\\n\\n# Consolidated Balance Sheets\\ncurrent_assets_2025 = 316650\\ncurrent_liabilities_2025 = 131935\\ncash_and_equivalents_2025 = 61340\\nshort_term_investments_2025 = 15000\\naccounts_receivable_2025 = 88412\\ninventories_2025 = 142780\\n\\n# Consolidated Statement of Cash Flows\\nnet_cash_provided_by_operating_activities_2025 = 112640\\npurchases_of_property_and_equipment_2025 = 38975\\ncapitalized_internal_use_software_2025 = 4100\\n\\n# Notes to the Financial Statements\\nfinance_lease_obligations_2025 = 6400 # in thousands\\n\\n# Compute financial ratios\\ncurrent_ratio = current_assets_2025 / current_liabilities_2025\\nquick_ratio = (cash_and_equivalents_2025 + short_term_investments_2025 + accounts_receivable_2025) / current_liabilities_2025\\ngross_margin = gross_profit_2025 / net_sales_2025\\noperating_margin = operating_income_2025 / net_sales_2025\\nnet_margin = net_income_2025 / net_sales_2025\\neffective_tax_rate = provision_for_income_taxes_2025 / income_before_taxes_2025\\nroe = net_income_2025 / (287400 + 248200) / 2 # Average stockholders' equity\\n\\n# Total debt\\nlong_term_debt_2025 = 148000\\ncurrent_portion_long_term_debt_2025 = 12500\\ntotal_debt_thousands = current_portion_long_term_debt_2025 + long_term_debt_2025 + finance_lease_obligations_2025\\ndebt_to_equity = total_debt_thousands / 287400\\ninterest_coverage = operating_income_2025 / interest_expense_2025\\n\\n# Free cash flow\\nfree_cash_flow_thousands = net_cash_provided_by_operating_activities_2025 - purchases_of_property_and_equipment_2025 - capitalized_internal_use_software_2025\\nfcf_margin = free_cash_flow_thousands / net_sales_2025\\n\\n# Inventory turnover\\ninventory_turnover = 503977 / ((142780 + 127905) / 2) # Average inventory\\n\\n# Days sales outstanding (DSO)\\ndso_days = (accounts_receivable_2025 / net_sales_2025) * 365\\n\\n# Revenue growth\\nnet_sales_2024 = 771302\\nrevenue_growth = net_sales_2025 / net_sales_2024 - 1\\n\\n# Earnings per share (EPS) diluted\\neps_diluted = net_income_2025 / weighted_average_shares_diluted_2025\\n\\n# Create a dictionary with the computed ratios\\nratios = {\\n 'current_ratio': current_ratio,\\n 'quick_ratio': quick_ratio,\\n 'gross_margin': gross_margin,\\n 'operating_margin': operating_margin,\\n 'net_margin': net_margin,\\n 'effective_tax_rate': effective_tax_rate,\\n 'roe': roe,\\n 'total_debt_thousands': total_debt_thousands,\\n 'debt_to_equity': debt_to_equity,\\n 'interest_coverage': interest_coverage,\\n 'fcf_thousands': free_cash_flow_thousands,\\n 'fcf_margin': fcf_margin,\\n 'inventory_turnover': inventory_turnover,\\n 'dso_days': dso_days,\\n 'revenue_growth': revenue_growth,\\n 'eps_diluted': eps_diluted\\n}\\n\\n# Write the ratios to a JSON file\\nwith open('ratios.json', 'w') as json_file:\\n json.dump(ratios, json_file, indent=4)\\\"\"}"
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"command": "python3 -c \"import json\n\n# Read the annual report excerpt\nwith open('annual_report_excerpt.md', 'r') as file:\n annual_report = file.read()\n\n# Extract financial data from the annual report excerpt\n# (This is a simplified approach; in a real scenario, you might need a more robust parsing method)\n\n# Consolidated Statements of Operations\ngross_profit_2025 = 338638\nnet_sales_2025 = 842615\nselling_general_admin_2025 = 221450\nresearch_development_2025 = 18906\noperating_income_2025 = 98282\ninterest_expense_2025 = 9874\nother_income_net_2025 = 1240\nincome_before_taxes_2025 = 89648\nprovision_for_income_taxes_2025 = 19723\nnet_income_2025 = 69925\nweighted_average_shares_diluted_2025 = 42050\n\n# Consolidated Balance Sheets\ncurrent_assets_2025 = 316650\ncurrent_liabilities_2025 = 131935\ncash_and_equivalents_2025 = 61340\nshort_term_investments_2025 = 15000\naccounts_receivable_2025 = 88412\ninventories_2025 = 142780\n\n# Consolidated Statement of Cash Flows\nnet_cash_provided_by_operating_activities_2025 = 112640\npurchases_of_property_and_equipment_2025 = 38975\ncapitalized_internal_use_software_2025 = 4100\n\n# Notes to the Financial Statements\nfinance_lease_obligations_2025 = 6400 # in thousands\n\n# Compute financial ratios\ncurrent_ratio = current_assets_2025 / current_liabilities_2025\nquick_ratio = (cash_and_equivalents_2025 + short_term_investments_2025 + accounts_receivable_2025) / current_liabilities_2025\ngross_margin = gross_profit_2025 / net_sales_2025\noperating_margin = operating_income_2025 / net_sales_2025\nnet_margin = net_income_2025 / net_sales_2025\neffective_tax_rate = provision_for_income_taxes_2025 / income_before_taxes_2025\nroe = net_income_2025 / (287400 + 248200) / 2 # Average stockholders' equity\n\n# Total debt\nlong_term_debt_2025 = 148000\ncurrent_portion_long_term_debt_2025 = 12500\ntotal_debt_thousands = current_portion_long_term_debt_2025 + long_term_debt_2025 + finance_lease_obligations_2025\ndebt_to_equity = total_debt_thousands / 287400\ninterest_coverage = operating_income_2025 / interest_expense_2025\n\n# Free cash flow\nfree_cash_flow_thousands = net_cash_provided_by_operating_activities_2025 - purchases_of_property_and_equipment_2025 - capitalized_internal_use_software_2025\nfcf_margin = free_cash_flow_thousands / net_sales_2025\n\n# Inventory turnover\ninventory_turnover = 503977 / ((142780 + 127905) / 2) # Average inventory\n\n# Days sales outstanding (DSO)\ndso_days = (accounts_receivable_2025 / net_sales_2025) * 365\n\n# Revenue growth\nnet_sales_2024 = 771302\nrevenue_growth = net_sales_2025 / net_sales_2024 - 1\n\n# Earnings per share (EPS) diluted\neps_diluted = net_income_2025 / weighted_average_shares_diluted_2025\n\n# Create a dictionary with the computed ratios\nratios = {\n 'current_ratio': current_ratio,\n 'quick_ratio': quick_ratio,\n 'gross_margin': gross_margin,\n 'operating_margin': operating_margin,\n 'net_margin': net_margin,\n 'effective_tax_rate': effective_tax_rate,\n 'roe': roe,\n 'total_debt_thousands': total_debt_thousands,\n 'debt_to_equity': debt_to_equity,\n 'interest_coverage': interest_coverage,\n 'fcf_thousands': free_cash_flow_thousands,\n 'fcf_margin': fcf_margin,\n 'inventory_turnover': inventory_turnover,\n 'dso_days': dso_days,\n 'revenue_growth': revenue_growth,\n 'eps_diluted': eps_diluted\n}\n\n# Write the ratios to a JSON file\nwith open('ratios.json', 'w') as json_file:\n json.dump(ratios, json_file, indent=4)\""
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