GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Discounted cash flow valuation with mid-year option and a sensitivity grid
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.05
- Wall time
- 41s
- 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. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
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 batch9 case j10-dcf-valuation (offline fixture). Prepared with batch9/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 4 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.05
- Wall time
- 41s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 84,131 | 1,504 | $0.05 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), 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.
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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
{"type": "thread.started", "thread_id": "01a163f4-6d7a-7ab0-93c6-df707bb83aa9"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll check the workspace, implement `solve.py`, and verify both discounting conventions and the growth error case.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py'\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/105_j10/work\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "{\n \"units\": \"millions, except per share\",\n \"fcf\": [\n 364.6,\n 338.9,\n 322.3,\n 298.6,\n 334.7,\n 383.7,\n 379.0,\n 440.0,\n 442.1\n ],\n \"wacc\": 0.1083,\n \"terminal_growth\": 0.031,\n \"mid_year\": true,\n \"net_debt\": 1248.7,\n \"shares\": 287.69,\n \"ebitda_last\": 751.2,\n \"sens_wacc\": [\n 0.0883,\n 0.0983,\n 0.1083,\n 0.1183,\n 0.1283\n ],\n \"sens_growth\": [\n 0.0,\n 0.015,\n 0.025,\n 0.035,\n 0.1183\n ]\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/105_j10/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/105_j10/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python solve.py input.json answer.json && python - <<'PY'\nfrom solve import solve\nfor mid in (False, True):\n d = dict(fcf=[100, 120], wacc=.1, terminal_growth=.02, mid_year=mid, net_debt=-20, shares=10, ebitda_last=50, sens_wacc=[.1,.02], sens_growth=[.02,.1])\n r = solve(d)\n pv = 100/1.1**(1-.5*mid)+120/1.1**(2-.5*mid)\n tv = 120*1.02/.08\n ev = pv+tv/1.1**2\n assert r['pv_fcf'] == round(pv,2)\n assert r['pv_terminal'] == round(tv/1.1**2,2)\n assert r['per_share'] == round((ev+20)/10,6)\n assert r['sensitivity'] == [[round((ev+20)/10,4),None],[None,None]]\n d['terminal_growth'] = .1\n assert solve(d) == {'error':'growth_not_below_wacc'}\nprint('Checks passed for end-of-year, mid-year, net cash, sensitivity nulls, and invalid growth.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc \"python solve.py input.json answer.json && python - <<'PY'\nfrom solve import solve\nfor mid in (False, True):\n d = dict(fcf=[100, 120], wacc=.1, terminal_growth=.02, mid_year=mid, net_debt=-20, shares=10, ebitda_last=50, sens_wacc=[.1,.02], sens_growth=[.02,.1])\n r = solve(d)\n pv = 100/1.1**(1-.5*mid)+120/1.1**(2-.5*mid)\n tv = 120*1.02/.08\n ev = pv+tv/1.1**2\n assert r['pv_fcf'] == round(pv,2)\n assert r['pv_terminal'] == round(tv/1.1**2,2)\n assert r['per_share'] == round((ev+20)/10,6)\n assert r['sensitivity'] == [[round((ev+20)/10,4),None],[None,None]]\n d['terminal_growth'] = .1\n assert solve(d) == {'error':'growth_not_below_wacc'}\nprint('Checks passed for end-of-year, mid-year, net cash, sensitivity nulls, and invalid growth.')\nPY\"", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && python3 - <<'PY'\nfrom solve import solve\nfor mid in (False, True):\n d = dict(fcf=[100,120], wacc=.1, terminal_growth=.02, mid_year=mid, net_debt=-20, shares=10, ebitda_last=50, sens_wacc=[.1,.02], sens_growth=[.02,.1])\n r = solve(d)\n pv = 100/1.1**(1-.5*mid)+120/1.1**(2-.5*mid)\n tv = 120*1.02/(.1-.02)\n ev = pv+tv/1.1**2\n assert r['pv_fcf'] == round(pv,2)\n assert r['pv_terminal'] == round(tv/1.1**2,2)\n assert r['per_share'] == round((ev+20)/10,6)\n assert r['sensitivity'] == [[round((ev+20)/10,4),None],[None,None]]\n d['terminal_growth'] = .1\n assert solve(d) == {'error':'growth_not_below_wacc'}\nprint('All checks passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && python3 - <<'PY'\nfrom solve import solve\nfor mid in (False, True):\n d = dict(fcf=[100,120], wacc=.1, terminal_growth=.02, mid_year=mid, net_debt=-20, shares=10, ebitda_last=50, sens_wacc=[.1,.02], sens_growth=[.02,.1])\n r = solve(d)\n pv = 100/1.1**(1-.5*mid)+120/1.1**(2-.5*mid)\n tv = 120*1.02/(.1-.02)\n ev = pv+tv/1.1**2\n assert r['pv_fcf'] == round(pv,2)\n assert r['pv_terminal'] == round(tv/1.1**2,2)\n assert r['per_share'] == round((ev+20)/10,6)\n assert r['sensitivity'] == [[round((ev+20)/10,4),None],[None,None]]\n d['terminal_growth'] = .1\n assert solve(d) == {'error':'growth_not_below_wacc'}\nprint('All checks passed.')\nPY\"", "aggregated_output": "All checks passed.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "agent_message", "text": "Created [solve.py](solve.py) and generated [answer.json](answer.json).\n\nVerified both discounting conventions, sensitivity nulls, net cash, and invalid growth handling. Uses only Python\u2019s standard library.\n\nRun with `python3 solve.py input.json answer.json` (`python` is unavailable in this environment)."}}
{"type": "turn.completed", "usage": {"input_tokens": 84131, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 1504, "reasoning_output_tokens": 7}}