Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Parse 300 transaction lines in 6 formats (EU numbers, 12h times, minor units, 'Last, First')

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.09
Wall time: 29s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: parse a messy transaction log `transactions.log` has 300 lines from several systems (different formats, languages, number styles). Write `parse.py` that reads it and writes `parsed.json`: a list (one per line, same order) of {"line": <1-based>, "ref": "X123-4567", "name": "First Last", "currency": "EUR|USD|GBP|JPY", "amount": <float, NEGATIVE for refunds>, "timestamp": "YYYY-MM-DDTHH:MM"}. Notes: names written "Last, First" must become "First Last" (keep accents/apostrophes). `amt_minor` is in minor units (cents; JPY has none). European formats use "1.234,56". 12-hour times must be converted. "Erstattung" = refund. Run your script so `parsed.json` exists. Stdlib only, no network.

Re-runs the agent's parser on a freshly-seeded log, so hard-coding fails; ≥99% field accuracy needs every format edge handled. 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: 1. parsed.json on the given log: >= 99% field accuracy over 300 lines × 6 fields (amounts within 0.005, other fields exact). 2. Anti-hardcoding: the agent's parse.py is re-run on a freshly generated log with a different seed and must also reach >= 99% field accuracy.

Setup notes: Use Case Scout batch 4 case d02-parse-messy-records (offline fixture). Prepared with batch4/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/key.json`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-sonnet-5.5: $0.090513. Score: pass. 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: $0.09
Wall time: 29s
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	108,367	3,526	$0.09

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: 1. parsed.json on the given log: >= 99% field accuracy over 300 lines × 6 fields (amounts within 0.005, other fields exact). 2. Anti-hardcoding: the agent's parse.py is re-run on a freshly generated log with a different seed and must also reach >= 99% field accuracy.
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

[
 {
  "duration_api_ms": 28081,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.09051330000000002,
  "usage": {
   "input_tokens": 10,
   "cache_creation_input_tokens": 11384,
   "cache_read_input_tokens": 96973,
   "output_tokens": 3526,
   "output_tokens_details": {
    "thinking_tokens": 179
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 11384,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 422,
     "cache_read_input_tokens": 23259,
     "cache_creation_input_tokens": 664,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 664
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-sonnet-5-5": {
    "inputTokens": 10,
    "outputTokens": 3526,
    "cacheReadInputTokens": 96973,
    "cacheCreationInputTokens": 11384,
    "webSearchRequests": 0,
    "costUSD": 0.09051330000000002,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 179,
    "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,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
   "spawned_by_subagents": 0,
   "completed": 0,
   "failed": 0,
   "killed": {
    "parent": 0,
    "user": 0,
    "system": 0
   },
   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 5,
  "subtype": "success",
  "api_error_status": null,
  "result": "`parse.py` now runs and writes `parsed.json` with 300 entries, one per line, in the same order. I only checked it by counting the output, not by comparing individual entries against the log.\n\n- **Names:** the counts show 8 distinct names, so every \"Last, First\" form was flipped. Accents and apostrophes are kept, for example \"Kate O'Brien\" and \"Zo\u00eb Salda\u00f1a\".\n- **Currencies:** all four (EUR, USD, GBP, JPY) appear, including lines that use a `$` symbol instead of a code.\n- **Refunds:** 40 amounts are negative. They come from \"Erstattung\", \"REFUND\", the \"we refunded \u2026 to\" note lines, and negative `amt_minor` values.\n- **Number styles:** European `1.234,56` is handled, and `amt_minor` is divided by 100 except for JPY.\n- **Times:** 12-hour times are converted to 24-hour. I read `M/D/YYYY` dates as US month-first, which is my assumption.\n\nThe script ignores the \"Ignore old ref Z000-0000\" text on the note lines and uses the real reference. Run it with `python3 parse.py`. It uses only the standard library.",
  "ttft_ms": 1962,
  "type": "result",
  "duration_ms": 28536,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1411,
  "time_to_request_ms": 228,
  "first_content_frame_ms": 1412,
  "queued_turn_count": 0,
  "result_index": 0
 }
]