Claude Sonnet 5.5 (effort default (no --effort flag) · thinking off) on Read a synthetic annual-report excerpt and compute financial ratios

AClaude Sonnet 5.5default · thinking off · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.05
Wall time: 9s
Human step-ins: 0
Outcome: Pass

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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. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

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: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-sonnet-5.5: $0.053825. Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.05
Wall time: 9s
Human step-ins: 0
Turns: 3

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking off	62,054	1,126	$0.05

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-5.5, effort default (no --effort flag) · thinking off) via Claude Code 2.1.299 (headless -p), 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.

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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

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  "result": "I wrote `ratios.json` with all 16 keys, computed with a short Python script from the excerpt.\n\n| Key | Value |\n|---|---|\n| `current_ratio` | 2.4000 |\n| `quick_ratio` | 1.2487 |\n| `gross_margin` | 0.4019 |\n| `operating_margin` | 0.1166 |\n| `net_margin` | 0.0830 |\n| `effective_tax_rate` | 0.2200 |\n| `roe` | 0.2629 |\n| `total_debt_thousands` | 166,900 |\n| `debt_to_equity` | 0.5807 |\n| `interest_coverage` | 9.9536 |\n| `fcf_thousands` | 69,565 |\n| `fcf_margin` | 0.0826 |\n| `inventory_turnover` | 3.7237 |\n| `dso_days` | 36.40 |\n| `revenue_growth` | 0.0925 |\n| `eps_diluted` | 1.66 |\n\nThe file holds the full-precision values; the table rounds them.\n\nThe notes changed three of these:\n- **Restated 2024 figures (Note 4):** Dec 31, 2024 equity is 248,200 \u2212 3,550 = 244,650 and inventory is 127,905. Those feed `roe` (average equity) and `inventory_turnover` (average inventory).\n- **Finance leases (Note 7):** the $6.4 million becomes 6,400 thousand and is added to the two debt lines. That gives 12,500 + 148,000 + 6,400 = 166,900.\n- **Unused figures:** I did not use the non-GAAP adjusted operating income or the segment sales.\n\nI did not restate 2024 current assets or total assets, since no ratio uses them.",
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