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

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

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.04
Wall time: 10s
Human step-ins: 0
Outcome: Pass

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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 156438301). 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 0.18 (8 cores). Claude Code's own reported cost for claude-sonnet-5.5: $0.044558. 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: $0.04
Wall time: 10s
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 on	57,474	958	$0.04

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 on) via Claude Code 2.1.299 (headless -p), cloud.
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
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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     "type": "message"
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   "speed": "standard",
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  "modelUsage": {
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    "cacheReadInputTokens": 49976,
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    "webSearchRequests": 0,
    "costUSD": 0.0445576,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 21,
    "canonicalModel": "claude-sonnet-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
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   "started_in_background": 0,
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  "safety_stops": 0,
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  "num_turns": 3,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `out/scaled.json` with all 11 ingredients scaled from 6 to 14 servings (a factor of 7/3) and converted to metric. I haven't checked it against any reference answer.\n\n- **Sticks:** `conversions.json` lists \"stick\" under weight, at 113 g. So milk (12 sticks), chicken stock (8) and soy sauce (3/4) came out in grams: 3164 g, 2109 g and 198 g.\n- **Totals:** `total_g` is 5932 and `total_ml` is 1494.2.\n- **Brown sugar:** It scales to 1.2 ml. The 0.5 ml amount was already metric, so it only got the scaling and the below-10 rounding.\n- **Counts:** Garlic rounds up to 7, onions to 2 and eggs to 7, all kept as \"each\".\n\nI did the arithmetic with exact fractions and rounded half-up on the exact converted amounts, as the rules say.",
  "ttft_ms": 2478,
  "type": "result",
  "duration_ms": 9690,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1717,
  "time_to_request_ms": 344,
  "first_content_frame_ms": 1717,
  "queued_turn_count": 0,
  "result_index": 0
 }
]