Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Answer 8 business questions against a SQLite shop database
AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $0.15
- Wall time
- 35s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Task: answer business questions from a SQLite database `shop.db` is a SQLite database (use python's sqlite3; inspect the schema yourself). Only orders with status 'shipped' count as sales. Revenue = qty * unit_price from order_items (NOT list_price). Write `answer.json` with keys q1..q8: - q1: total shipped revenue (round 2) - q2: customer country with the highest shipped revenue (2-letter code) - q3: product category with highest shipped revenue NET of returns (subtract returned qty * that line's unit_price) - q4: number of customers with zero shipped orders - q5: returned units / shipped units, over shipped orders only (round 4) - q6: month (YYYY-MM) with highest shipped revenue - q7: number of distinct discontinued products that appear in shipped orders dated on/after 2026-07-01 - q8: average shipped order value (round 2) Also write `queries.sql` with the SQL you used. No network.
Answer eight business questions from a small SQLite shop database. Traps include using the order line's unit price instead of the list price, counting only shipped orders, netting out returns per line, and finding customers with no shipped orders. 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. All 8 answers (q1–q8) must be correct: numeric answers within 0.01 (q5 within 0.0005); text answers exact match, case-insensitive. 2. Pass requires 8/8.
Setup notes: Use Case Scout batch 3 case c12-sqlite-business-questions (offline fixture). Prepared with batch3/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/answer.json 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: $0.153444. Score: 8/8. 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
- $0.15
- Wall time
- 35s
- Human step-ins
- 0
- Turns
- 4
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on | 78,210 | 3,065 | $0.15 |
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. All 8 answers (q1–q8) must be correct: numeric answers within 0.01 (q5 within 0.0005); text answers exact match, case-insensitive. 2. Pass requires 8/8.
- 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": 33535,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.1534436,
"usage": {
"input_tokens": 8,
"cache_creation_input_tokens": 9804,
"cache_read_input_tokens": 68398,
"output_tokens": 3065,
"output_tokens_details": {
"thinking_tokens": 133
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 9804,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 473,
"cache_read_input_tokens": 19882,
"cache_creation_input_tokens": 2068,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 2068
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-opus-5-5": {
"inputTokens": 8,
"outputTokens": 3065,
"cacheReadInputTokens": 68398,
"cacheCreationInputTokens": 9804,
"webSearchRequests": 0,
"costUSD": 0.1534436,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 133,
"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": 4,
"subtype": "success",
"api_error_status": null,
"result": "I wrote `answer.json` and `queries.sql`. I produced `answer.json` by running the statements in `queries.sql` directly, so the two files match.\n\n| Key | Answer |\n|---|---|\n| q1 total shipped revenue | 2,520,896.86 |\n| q2 top country | DE |\n| q3 top category, net of returns | input |\n| q4 customers with zero shipped orders | 2 |\n| q5 returned / shipped units | 0.0443 |\n| q6 top month | 2026-09 |\n| q7 discontinued products shipped on/after 2026-07-01 | 3 |\n| q8 average shipped order value | 994.44 |\n\nHow I handled the parts that could have gone either way:\n- **Returns:** 78 of the 453 return rows belong to cancelled or pending orders. I left them out of q3 and q5, so only returns on shipped orders count.\n- **Data checks:** none of these turned up a problem. There are no duplicate order or return lines, no returns without a matching order line, no return larger than the quantity ordered, no orphan orders, and no variations in status spelling or date format.\n- **Close calls:** q6 is near: September has 315,245 in revenue against January's 312,782. q2 and q3 win by clearer margins: DE has 472,008 against ES's 450,677, and \"input\" has 722,612 net against \"cables\" at 485,314.",
"ttft_ms": 4702,
"type": "result",
"duration_ms": 34368,
"uuid": "[REDACTED]",
"ttft_stream_ms": 2872,
"time_to_request_ms": 222,
"first_content_frame_ms": 2873,
"queued_turn_count": 0,
"result_index": 0
}
]