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Reconcile a messy CSV ledger against bank statements

Exact prompt

You are the bookkeeper for Bramble & Oak Bakery LLC. This folder contains ledger.csv (the general ledger for the business checking account, July-September 2026; amount > 0 is a deposit, amount < 0 is a payment; the opening book balance on July 1, 2026 was 48,213.57 and agreed with the bank) and three bank statement PDFs (statement_2026-07.pdf, statement_2026-08.pdf, statement_2026-09.pdf). pdftotext is installed. Reconcile the ledger against the bank statements for the whole quarter. Ledger entries are often dated 0-3 days before the bank posts them, and ledger descriptions differ from bank descriptions; neither of those is a discrepancy. Report only real discrepancies. Write two files in this folder: 1. discrepancies.csv with header: type,ledger_entry_ids,bank_date,bank_amount,ledger_amount,explanation - type is one of: duplicate_in_ledger, amount_mismatch, sign_error, missing_in_ledger (on the bank statement but not in the ledger), missing_in_bank (in the ledger but never on any statement) - ledger_entry_ids: the ledger entry_id(s) involved, separated by ';' (empty for missing_in_ledger) - bank_date as YYYY-MM-DD (empty if not on the bank statement); amounts as signed numbers without $ or commas 2. reconciliation.json with keys: bank_closing_balance_2026_09_30, ledger_closing_balance_2026_09_30 (opening balance plus all ledger entries), sum_of_adjustments (the net amount your discrepancies add to the ledger balance to reach the bank balance), unexplained_difference (bank closing minus ledger closing minus sum_of_adjustments).

Reconciling a bakery's three-month checking-account ledger (about 1,800 rows) against three PDF bank statements. Twenty-five discrepancies are seeded (duplicates, transposed digits, sign errors and items missing on either side), plus legitimate look-alike purchases as false-positive traps. The agent must list every discrepancy and make the balances reconcile to zero. 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).

Success criteria: Finds all 25 seeded discrepancies with ≤ 2 false positives; totals match.

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.02, 2m).

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All runs (3)

100% pass rate across 3 eligible runs: 3 pass, 0 partial, 0 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
AClaude Opus 5.5default · thinking on$0.341m0Fully autonomouspassTrust 80%2026-10-16
AClaude Sonnet 5.5default · thinking on$0.1135s0Fully autonomouspassTrust 80%2026-10-16
AClaude Haiku 5.5default · thinking on$0.022m0Fully autonomouspassTrust 80%2026-10-16

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