Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Discounted cash flow valuation with mid-year option and a sensitivity grid
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-25 · by @bobsiopener
- Estimated cost
- $0.0015
- Wall time
- 48s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Rescored on 2026-10-28. Previous score: 94.0 / 100, outcome: success. See the previous run.
Summary
Exact prompt
# Discounted cash flow valuation with a sensitivity grid `input.json` gives forecast free cash flows and valuation assumptions for a fictional company (synthetic). Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` computes the valuation. We re-run it on fresh inputs, so do not hard-code anything. ## Input `fcf` (forecast free cash flow for years 1..N), `wacc`, `terminal_growth`, `mid_year` (bool), `net_debt` (can be negative = net cash), `shares`, `ebitda_last` (year-N EBITDA), `sens_wacc`, `sens_growth` (lists). ## Rules 1. Discount year t (t = 1..N) by (1 + wacc) ** t, or by (1 + wacc) ** (t - 0.5) when `mid_year` is true. `pv_fcf` = sum of discounted cash flows. 2. Terminal value (Gordon growth, at the end of year N): `terminal_value` = fcf_N * (1 + g) / (wacc - g). It is always discounted by (1 + wacc) ** N, even with the mid-year convention: `pv_terminal`. 3. `enterprise_value` = pv_fcf + pv_terminal; `equity_value` = enterprise_value - net_debt; `per_share` = equity_value / shares; `terminal_share` = pv_terminal / enterprise_value; `implied_ev_ebitda` = enterprise_value / ebitda_last. 4. If terminal_growth >= wacc, the whole answer is `{"error": "growth_not_below_wacc"}`. 5. `sensitivity`: a list with one row per value in `sens_wacc` (same order); each row has one entry per value in `sens_growth`: the per-share value with that wacc and growth (other inputs unchanged), rounded to 4 decimals, or `null` when growth >= wacc. 6. Round money values to 2 decimals and `per_share`, `terminal_share`, `implied_ev_ebitda` to 6 decimals when writing. ## Output `answer.json` keys: `pv_fcf`, `terminal_value`, `pv_terminal`, `enterprise_value`, `equity_value`, `per_share`, `terminal_share`, `implied_ev_ebitda`, `sensitivity`. ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `pv_of_cash_flows`, `terminal_value`, `ev_equity_per_share` (money within 0.011, the three ratios within 0.000001), `sensitivity_grid` (within 0.0001, nulls exact) on the given input; `fresh_end_of_year` and `fresh_mid_year` (the whole answer, tolerance 0.011); `growth_not_below_wacc` (rule 4). Each run has a 60-second limit.
Mid-year discounting of cash flows but not of the terminal value, net cash vs net debt, null cells where growth >= discount rate, and the error case. 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 the site's price table).
These are synthetic test cases for comparing AI models, not financial advice.
Success criteria: 1. pv_of_cash_flows 2. terminal_value 3. ev_equity_per_share 4. sensitivity_grid 5. fresh_end_of_year 6. fresh_mid_year 7. growth_not_below_wacc
Setup notes: Use Case Scout batch 9 case j10-dcf-valuation (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 $0.0334, 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, $0.0334 run cap). Score: 7/7. 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
- $0.0015
- Wall time
- 48s
- 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 | 12,468 | 1,227 | $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 v1.3.1, cloud. - It counts as a success only if it meets every success criterion: 1. pv_of_cash_flows 2. terminal_value 3. ev_equity_per_share 4. sensitivity_grid 5. fresh_end_of_year 6. fresh_mid_year 7. growth_not_below_wacc
- 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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"content": "import json\r\nimport math\r\n\r\ndef discounted_cash_flow(input_data):\r\n fcf = input_data['fcf']\r\n wacc = input_data['wacc']\r\n terminal_growth = input_data['terminal_growth']\r\n mid_year = input_data['mid_year']\r\n net_debt = input_data['net_debt']\r\n shares = input_data['shares']\r\n ebitda_last = input_data['ebitda_last']\r\n sens_wacc = input_data['sens_wacc']\r\n sens_growth = input_data['sens_growth']\r\n \r\n # Calculate pv_fcf\r\n pv_fcf = 0.0\r\n for t in range(1, len(fcf) + 1):\r\n discount_factor = (1 + wacc) ** (t - 0.5) if mid_year else (1 + wacc) ** t\r\n pv_fcf += fcf[t - 1] / discount_factor\r\n \r\n # Calculate terminal_value\r\n if terminal_growth >= wacc:\r\n return {\"error\": \"growth_not_below_wacc\"}\r\n \r\n fcf_N = fcf[-1]\r\n terminal_value = fcf_N * (1 + terminal_growth) / (wacc - terminal_growth)\r\n pv_terminal = terminal_value / ((1 + wacc) ** len(fcf))\r\n \r\n # Calculate enterprise_value and other metrics\r\n enterprise_value = pv_fcf + pv_terminal\r\n equity_value = enterprise_value - net_debt\r\n per_share = equity_value / shares\r\n terminal_share = pv_terminal / enterprise_value\r\n implied_ev_ebitda = enterprise_value / ebitda_last\r\n \r\n # Calculate sensitivity grid\r\n sensitivity = []\r\n for w in sens_wacc:\r\n row = []\r\n for g in sens_growth:\r\n if g >= w:\r\n row.append(None)\r\n continue\r\n \r\n # Recalculate pv_fcf with new wacc\r\n pv_fcf_new = 0.0\r\n for t in range(1, len(fcf) + 1):\r\n discount_factor = (1 + w) ** (t - 0.5) if mid_year else (1 + w) ** t\r\n pv_fcf_new += fcf[t - 1] / discount_factor\r\n \r\n # Recalculate terminal_value with new wacc and growth\r\n terminal_value_new = fcf_N * (1 + g) / (w - g)\r\n pv_terminal_new = terminal_value_new / ((1 + w) ** len(fcf))\r\n \r\n # Recalculate equity_value and per_share with new pv_fcf and pv_terminal\r\n enterprise_value_new = pv_fcf_new + pv_terminal_new\r\n equity_value_new = enterprise_value_new - net_debt\r\n per_share_new = equity_value_new / shares\r\n \r\n row.append(round(per_share_new, 4))\r\n sensitivity.append(row)\r\n \r\n # Round the results\r\n result = {\r\n \"pv_fcf\": round(pv_fcf, 2),\r\n \"terminal_value\": round(terminal_value, 2),\r\n \"pv_terminal\": round(pv_terminal, 2),\r\n \"enterprise_value\": round(enterprise_value, 2),\r\n \"equity_value\": round(equity_value, 2),\r\n \"per_share\": round(per_share, 6),\r\n \"terminal_share\": round(terminal_share, 6),\r\n \"implied_ev_ebitda\": round(implied_ev_ebitda, 6),\r\n \"sensitivity\": sensitivity\r\n }\r\n \r\n return result\r\n\r\ndef main():\r\n import sys\r\n \r\n if len(sys.argv) != 3:\r\n print(\"Usage: python solve.py input.json answer.json\")\r\n sys.exit(1)\r\n \r\n input_file = sys.argv[1]\r\n output_file = sys.argv[2]\r\n \r\n with open(input_file, 'r') as f:\r\n input_data = json.load(f)\r\n \r\n result = discounted_cash_flow(input_data)\r\n \r\n with open(output_file, 'w') as f:\r\n json.dump(result, f, indent=4)\r\n\r\nif __name__ == \"__main__\":\r\n main()"
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