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Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Scale a recipe to a new serving count and convert it to metric

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

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.0038
Wall time
12s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# 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

Setup notes: Batch 16 case r01-recipe-scaling (offline, synthetic inputs, seed 21730242). Autonomous case: no stand-in user and no step-ins expected; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths); pass = every check true and no ask/ files. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.17 (8 cores). Claude Code's own reported cost for claude-haiku-5.5: $0.003836. 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
$0.0038
Wall time
12s
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on83,5012,239$0.0038

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. It counts as a success only if it meets every success criterion: all_ingredients_and_units volume_amounts_ml weight_amounts_g small_amounts_one_decimal count_items_rounded_up fraction_quantities_parsed totals
  4. 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

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Evidence log
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  "result": "I wrote `out/scaled.json` for 7 servings (the original recipe makes 6, so the scale factor is 7/6). I computed the values by hand and did not run a script or validate the JSON.\n\n| Ingredient | Original | Scaled, converted, rounded | Unit |\n|---|---|---|---|\n| baking soda | 1/2 lb | 264.83 g \u2192 265 | g |\n| chicken stock | 8 ml | 9.33 ml \u2192 9.3 | ml |\n| butter | 250 g | 291.67 g \u2192 292 | g |\n| flour | 3/4 cup | 0.875 cup \u00d7 240 = 210 ml \u2192 210 | ml |\n| lemon juice | 0.5 ml | 0.583 ml \u2192 0.6 | ml |\n| eggs | 1 each | 1.17 \u2192 rounded up to 2 | each |\n| rice | 4 lb | 4.667 lb \u00d7 454 = 2118.67 g \u2192 2119 | g |\n| milk | 3/4 lb | 0.875 lb \u00d7 454 = 397.25 g \u2192 397 | g |\n| cinnamon | 1 1/2 oz | 1.75 oz \u00d7 28 = 49 g \u2192 49 | g |\n| garlic | 2 each | 2.33 \u2192 rounded up to 3 | each |\n| olive oil | 3 fl oz | 3.5 fl oz \u00d7 30 = 105 ml \u2192 105 | ml |\n\n- **total_g:** 265 + 292 + 2119 + 397 + 49 = 3122\n- **total_ml:** 9.3 + 210 + 0.6 + 105 = 324.9\n\nThe output file is a plain JSON object with the same keys and structure the spec asks for.",
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]

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