Claude Haiku 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 Haiku 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: $0.02
Wall time: 57s
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-haiku-5.5: $0.015862. 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: $0.02
Wall time: 57s
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	230,114	12,196	$0.02

Reproduce this run

Use the use case prompt on the use case page.
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.
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": 56026,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.015862090000000002,
  "usage": {
   "input_tokens": 12,
   "cache_creation_input_tokens": 39273,
   "cache_read_input_tokens": 190829,
   "output_tokens": 12196,
   "output_tokens_details": {
    "thinking_tokens": 7743
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 39273,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 555,
     "cache_read_input_tokens": 50356,
     "cache_creation_input_tokens": 1456,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 1456
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 12,
    "outputTokens": 12196,
    "cacheReadInputTokens": 190829,
    "cacheCreationInputTokens": 39273,
    "webSearchRequests": 0,
    "costUSD": 0.015862090000000002,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 7743,
    "canonicalModel": "claude-haiku-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": 6,
  "subtype": "success",
  "api_error_status": null,
  "result": "`parse.py` is written and has run: `parsed.json` now holds 300 records, one per log line, in the same order.\n\nEach line is one transaction in one of six formats, and every line matched a format, so nothing was skipped. Spot-checks against the source lines came out right: 12-hour times (e.g. `8:33 PM` \u2192 `20:33`), European amounts (`1.048,69` \u2192 `1048.69`), `amt_minor` divided by 100 except JPY, and `Last, First` names turned into `First Last`. Across the output there are 40 negative amounts, and no names still contain a comma.\n\nChoices I made that you may want to change:\n- **Timestamps are not converted.** The `Z` and `UTC` stamps are written as-is, so `2026-10-25T00:07` means UTC wall-clock, because the spec gave no target timezone.\n- **Refunds:** lines saying `REFUND`, `refunded` or `Erstattung` are made negative. A value that is already negative in the source (e.g. line 4, `-19909.75`) is kept negative even without a refund word.\n- **Reference:** in the note lines, the reference is taken from `reference X`, and the `Ignore old ref Z000-0000` text is skipped.",
  "ttft_ms": 1669,
  "type": "result",
  "duration_ms": 56430,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1423,
  "time_to_request_ms": 208,
  "first_content_frame_ms": 1423,
  "queued_turn_count": 0,
  "result_index": 0
 }
]