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Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Answer 8 business questions against a SQLite shop database

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

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.06
Wall time
16s
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-sonnet-5.5: $0.061121. 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
$0.06
Wall time
16s
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on78,2732,126$0.06

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. 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.
  3. 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.
  4. 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

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Evidence log
[
 {
  "duration_api_ms": 15224,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.061121499999999995,
  "usage": {
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   "cache_creation_input_tokens": 8210,
   "cache_read_input_tokens": 70055,
   "output_tokens": 2126,
   "output_tokens_details": {
    "thinking_tokens": 129
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 8210,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 366,
     "cache_read_input_tokens": 20447,
     "cache_creation_input_tokens": 302,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 302
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-sonnet-5-5": {
    "inputTokens": 8,
    "outputTokens": 2126,
    "cacheReadInputTokens": 70055,
    "cacheCreationInputTokens": 8210,
    "webSearchRequests": 0,
    "costUSD": 0.061121499999999995,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 129,
    "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,
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   "refused": {
    "depth_limit": 0,
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   "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` in the working directory. I ran every query against `shop.db` to get the answers.\n\n| Question | Answer |\n|---|---|\n| q1: total shipped revenue | 2520896.86 |\n| q2: top country by shipped revenue | DE |\n| q3: top category net of returns | input (about 722,611.71 net) |\n| q4: customers with zero shipped orders | 2 |\n| q5: returned units / shipped units | 0.0443 |\n| q6: top revenue month | 2026-09 |\n| q7: discontinued products in shipped orders from 2026-07-01 | 3 |\n| q8: average shipped order value | 994.44 |\n\n- **Returns (q3):** I summed returns per order and product before joining them to the order lines. That keeps a line with several return rows from being counted twice.\n- **Returns (q5):** I counted only returns on shipped orders, as the task specifies.\n- **Average order value (q8):** I divided total shipped revenue by the number of distinct shipped orders. Every shipped order has at least one line item, so none are missing from the count.",
  "ttft_ms": 2629,
  "type": "result",
  "duration_ms": 15598,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1155,
  "time_to_request_ms": 216,
  "first_content_frame_ms": 1156,
  "queued_turn_count": 0,
  "result_index": 0
 }
]

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