DeepSeek V4.1 Flash (Venice) (thinking on) on Discounted cash flow valuation with mid-year option and a sensitivity grid
DDeepSeek V4.1 Flash (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-24 · by @bobsiopener
- Estimated cost
- $0.0034
- Wall time
- 11s
- Human step-ins
- 0
- Outcome
- Pass
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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. 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 23, 2026): re-scored 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 and measured data unchanged.
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 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 $0.05, 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 v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 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.
Metrics
- Estimated cost
- $0.0034
- Wall time
- 11s
- Human step-ins
- 0
- Turns
- 5
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| DDeepSeek V4.1 Flash (Venice)venice/deepseek-v4-1-flashthinking on | 10,244 | 1,474 | $0.0034 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: DeepSeek V4.1 Flash (Venice) (venice/deepseek-v4-1-flash, 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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"final_message": "Done. `solve.py` computes the DCF valuation from `input.json` and writes `answer.json`.\n\nKey implementation points:\n- **PV of FCF**: discounts each year by `(1+wacc)**t`, or `(1+wacc)**(t-0.5)` under the mid-year convention.\n- **Terminal value**: Gordon growth `fcf_N*(1+g)/(wacc-g)`, always discounted by `(1+wacc)**N` (even mid-year).\n- **Derived metrics**: EV, equity, per-share, terminal share, implied EV/EBITDA.\n- **Error case**: if `terminal_growth >= wacc`, writes `{\"error\": \"growth_not_below_wacc\"}`.\n- **Sensitivity grid**: one row per `sens_wacc`, one entry per `sens_growth`, per-share rounded to 4 decimals, `null` when growth \u2265 that row's wacc.\n- **Rounding**: money to 2 decimals; `per_share`, `terminal_share`, `implied_ev_ebitda` to 6 decimals.\n\nVerified on the given input (mid-year) and on a growth\u2265wacc case.",
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"content": "Done. `solve.py` computes the DCF valuation from `input.json` and writes `answer.json`.\n\nKey implementation points:\n- **PV of FCF**: discounts each year by `(1+wacc)**t`, or `(1+wacc)**(t-0.5)` under the mid-year convention.\n- **Terminal value**: Gordon growth `fcf_N*(1+g)/(wacc-g)`, always discounted by `(1+wacc)**N` (even mid-year).\n- **Derived metrics**: EV, equity, per-share, terminal share, implied EV/EBITDA.\n- **Error case**: if `terminal_growth >= wacc`, writes `{\"error\": \"growth_not_below_wacc\"}`.\n- **Sensitivity grid**: one row per `sens_wacc`, one entry per `sens_growth`, per-share rounded to 4 decimals, `null` when growth \u2265 that row's wacc.\n- **Rounding**: money to 2 decimals; `per_share`, `terminal_share`, `implied_ev_ebitda` to 6 decimals.\n\nVerified on the given input (mid-year) and on a growth\u2265wacc case.",
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"reasoning_content": "Both work. Let me double-check the sensitivity null condition: \"null when growth >= wacc\". Yes. And the last row has growth 0.1183 == wacc 0.1283? No, 0.1183 < 0.1283, so computed. Good.\n\nEverything looks correct."
},
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