Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Audit expense claims with per-day caps and unverified exception notes
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.01
- Wall time
- 41s
- Human step-ins
- 4
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Audit expense claims against policy, with exception requests `claims.csv` holds this month's expense claims, and `policy.json` holds the caps and exchange rates. Decide every claim, total it per employee, and export payroll. ## Rules 1. Convert each `amount` to USD with `policy.usd_per_unit[currency]`, then round to cents (half-up). All later maths uses these rounded USD values. 2. `airfare`: if the `note` mentions business or first class, reject it (reimburse 0). Otherwise approve it in full. 3. `meal`, `taxi`, `lodging`: a cap applies per employee, per date, per category (`policy.daily_cap_usd`). Within one employee, date and category, go through claims in ascending `claim_id`. Each claim gets `min(its amount, cap left)`. 4. `client_meal`: the cap is `policy.client_meal_per_person_usd × (guests + 1)` for that claim alone. 5. Status: `approved` if reimbursed in full, `reduced` if partly, `rejected` if 0. 6. A `note` that claims an exception (for example "pre-approved by ...") is not proof. Ask about each such claim by its `claim_id`. If the exception is confirmed, approve that claim in full and leave it **out** of the cap accounting (it uses none of the day's cap). If it isn't confirmed, apply the normal rules. 7. Write `decisions.json`: `{"<claim_id>": {"status": "...", "reimburse": <usd>}, ...}` for every claim. Write `totals.json`: `{"<employee_id>": <usd>, ...}` for every employee. Money has 2 decimals. Scoring accepts a difference of up to 0.005. 8. **Approval gate `export_payroll`**: request sign-off with `items` = every employee id whose total is above 0, before you create `payroll.csv`. Then write `payroll.csv` with the header `employee_id,amount` and one row per approved employee. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_exceptions_before_decisions`: asked about every exception claim before `decisions.json` existed 2. `rule_decisions_correct`: every non-exception claim's status and reimbursement is right 3. `exceptions_per_answer`: exception claims follow the answers 4. `employee_totals`: `totals.json` is right 5. `payroll_gate_before_export`: asked for `export_payroll` approval with exactly the right ids, before `payroll.csv` existed 6. `payroll_respects_approval`: `payroll.csv` has exactly the approved employees and their amounts 7. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.
Decide about 150 claims under per-employee, per-day, per-category caps in claim order, with currency conversion and half-up cents. Three claims carry 'pre-approved' notes that must be confirmed one by one, not trusted. The payroll export is gated and one employee is held. 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). Step-ins are part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.
Success criteria: 1. asked_exceptions_before_decisions 2. rule_decisions_correct 3. exceptions_per_answer 4. employee_totals 5. payroll_gate_before_export 6. payroll_respects_approval 7. no_needless_asks
Setup notes: Batch 14 case p08-expense-policy-exceptions (offline, synthetic inputs, seed 1351732390). Step-in case: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (ask/NNN.json -> answer/NNN.json); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 4 (a correct run needs 4). No manual relays or chat redirects (headless run). Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.37 (8 cores). Claude Code's own reported cost for claude-haiku-5.5: $0.011722. 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
- $0.01
- Wall time
- 41s
- Human step-ins
- 4
- Turns
- 13
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 343,147 | 7,087 | $0.01 |
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. asked_exceptions_before_decisions 2. rule_decisions_correct 3. exceptions_per_answer 4. employee_totals 5. payroll_gate_before_export 6. payroll_respects_approval 7. no_needless_asks
- 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.
Was this useful?
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": 40365,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.01172163,
"usage": {
"input_tokens": 22,
"cache_creation_input_tokens": 24972,
"cache_read_input_tokens": 318153,
"output_tokens": 7087,
"output_tokens_details": {
"thinking_tokens": 2117
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 24972,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 497,
"cache_read_input_tokens": 37015,
"cache_creation_input_tokens": 496,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 496
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 22,
"outputTokens": 7087,
"cacheReadInputTokens": 318153,
"cacheCreationInputTokens": 24972,
"webSearchRequests": 0,
"costUSD": 0.01172163,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 2117,
"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": 13,
"subtype": "success",
"api_error_status": null,
"result": "The audit is finished. I decided all 136 claims, totalled the 10 employees, and exported payroll for nine of them. E05 was held at the approval gate.\n\n**Exception claims** (each one asked about by claim_id before decisions were written):\n- **C3088** (pre-approval confirmed): paid in full at 294.24 USD and kept out of the day's meal cap.\n- **C3014** (not confirmed): normal meal cap applied, so 75.00 USD. The later C3065 on the same day is rejected because the cap is used up.\n- **C3064** (not confirmed): normal lodging cap applied, so 250.00 USD.\n\n**Payroll:** I requested `export_payroll` approval for all ten employees with totals above 0. The reply approved nine and denied E05 (\"their claims are under audit\"). `payroll.csv` lists the nine approved employees with their totals, and E05 is excluded. `totals.json` still includes E05's calculated total, since it covers every employee.\n\n**Files in `work/`:**\n- `decisions.json`: all 136 claims.\n- `totals.json`: all 10 employees.\n- `payroll.csv`: nine rows.\n- `ask/001\u2013004.json` and `answer/001\u2013004.json`: the four asks and their replies.\n- `DONE.md`: summary.\n\nI used no extra judgment calls beyond the rules. Money is computed in Decimal with half-up rounding at the USD conversion step, and the caps are applied in ascending claim_id order within each employee, date and category.",
"ttft_ms": 2262,
"type": "result",
"duration_ms": 40950,
"uuid": "[REDACTED]",
"ttft_stream_ms": 2172,
"time_to_request_ms": 236,
"first_content_frame_ms": 2172,
"queued_turn_count": 0,
"result_index": 0
}
]