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Scale a recipe to a new serving count and convert it to metric

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# Scale a recipe and convert it to metric Inputs: `recipe.json` (original servings and ingredients), `request.json` (target servings), `conversions.json` (unit factors). Rules: - Scale every quantity by target servings / original servings. - Quantities may be written as fractions or mixed numbers such as "1 1/2". - Convert volume units to ml and weight units to g using conversions.json; amounts already in g or ml keep their unit. - Round metric amounts of 10 or more to a whole number, and amounts below 10 to 1 decimal place, half-up. Decide using the exact scaled, converted amount. - Count items (unit "each") are rounded UP to a whole number and keep the unit "each". - total_g is the sum of the rounded g amounts and total_ml the sum of the rounded ml amounts. Write `out/scaled.json`: `{"servings": <target>, "ingredients": {"<name>": {"amount": <number>, "unit": "g" | "ml" | "each"}, ...}, "total_g": <number>, "total_ml": <number>}` with every ingredient from recipe.json, keyed by its name. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.

Scale a 9-12 ingredient recipe written in cups, spoons, ounces, sticks and fractions to a new number of servings and convert it to grams and millilitres. Amounts follow a two-tier half-up rounding rule and count items round up. Inputs are drawn fresh per seed, with draws near rounding ties or the 10-unit threshold rejected. 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: all_ingredients_and_units volume_amounts_ml weight_amounts_g small_amounts_one_decimal count_items_rounded_up fraction_quantities_parsed totals

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0038, 12s).

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All runs (4)

75% pass rate across 4 eligible runs: 3 pass, 0 partial, 1 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
QQwen3 Coder 480B Turbo (Venice)thinking on$0.001819s0Fully autonomousfailTrust 80%2026-10-28
AClaude Opus 5.5default · thinking on$0.1014s0Fully autonomouspassTrust 80%2026-10-20
AClaude Haiku 5.5default · thinking on$0.003812s0Fully autonomouspassTrust 80%2026-10-20
AClaude Sonnet 5.5default · thinking on$0.0410s0Fully autonomouspassTrust 80%2026-10-20

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