Claude Opus 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 Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $0.26
- Wall time
- 39s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
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-opus-5.5: $0.260338. 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
- $0.26
- Wall time
- 39s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on | 148,761 | 4,406 | $0.26 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Claude Opus 5.5 (anthropic/claude-opus-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": 38350,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.260338,
"usage": {
"input_tokens": 12,
"cache_creation_input_tokens": 18259,
"cache_read_input_tokens": 130490,
"output_tokens": 4406,
"output_tokens_details": {
"thinking_tokens": 369
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 18259,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 561,
"cache_read_input_tokens": 28511,
"cache_creation_input_tokens": 1894,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 1894
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-opus-5-5": {
"inputTokens": 12,
"outputTokens": 4406,
"cacheReadInputTokens": 130490,
"cacheCreationInputTokens": 18259,
"webSearchRequests": 0,
"costUSD": 0.260338,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 369,
"canonicalModel": "claude-opus-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": "I wrote `parse.py` and ran it. `parsed.json` now has all 300 records, one per line, in order. Every line matched one of the six formats in the log, and the script stops with an error if any line doesn't match, so nothing gets skipped silently.\n\nI spot-checked a sample of lines against the source rather than all 300. Specific checks that passed:\n- Every ref has the `X123-4567` form, and the decoy `Z000-0000` in the \"Note:\" lines was never picked up.\n- No \"Last, First\" names remain. The first run left five `O'Brien, Kate` entries from one format unconverted; I fixed that, and they now read `Kate O'Brien` with the apostrophe kept. Accented names like `Zo\u00eb Salda\u00f1a` and `J\u00f6rg M\u00fcller` come through intact.\n- 12-hour times convert correctly, including the tricky ones: `12:53 PM` becomes `12:53`.\n- European amounts parse correctly: `16.253,45` becomes `16253.45`.\n- `amt_minor` values are divided by 100, except for JPY, which is used as-is.\n- 40 records are negative refunds. They come from:\n - \"REFUND\" and \"Erstattung\" entries\n - \"we refunded\" notes\n - amounts already negative in the JSON lines and the semicolon-separated lines\n\nTwo assumptions to be aware of:\n- Dates like `1/9/2026` are read as month/day (US style). Lines such as `2/27/2026` confirm that order.\n- Amounts are rounded to 2 decimal places.\n\nTo run it again: `python3 parse.py`. You can optionally pass an input file and an output file as arguments.",
"ttft_ms": 2505,
"type": "result",
"duration_ms": 38923,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1140,
"time_to_request_ms": 291,
"first_content_frame_ms": 1140,
"queued_turn_count": 0,
"result_index": 0
}
]