Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Optimise an O(n·m) price lookup to <4s on 200k orders with byte-identical output

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-17 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.06
Wall time: 35s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: make price_orders fast `pricing.py::price_orders` is correct but O(orders × prices). Production runs it on 200k orders, 100k price rows and 20k promos and it takes hours. Make it fast: it must produce EXACTLY the same output (same list, same order, same rounding, same tie-breaking described in the docstring) and run on 200k/100k/20k in under ~3 seconds on this machine. `make_data.py` generates test data. Keep the function name/signature; stdlib only; no network. Add a small correctness test comparing against the original on random data.

Needs sorting+bisect while preserving subtle tie-breaking (last row wins on equal timestamps) and promo max-pct; a tie-heavy dataset catches near-misses. Timing threshold is wall-clock. 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. Output must be exactly equal (==) to the original implementation on 5 seeded datasets, including one tie-heavy dataset with many equal timestamps and short promo windows. 2. On 200k orders / 100k prices / 20k promos, a single run must take < 4.0 s wall-clock (run in a subprocess capped at 30 s; timeout = fail).

Setup notes: Use Case Scout batch 4 case d03-perf-price-lookup (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`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring: 0.65. Claude Code's own reported cost for claude-sonnet-5.5: $0.063301. Score: 1/1 checks. 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: 35s
Human step-ins: 0
Turns: 3

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	57,842	2,487	$0.06

Reproduce this run

Use the use case prompt on the use case page.
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.
It counts as a success only if it meets every success criterion: 1. Output must be exactly equal (==) to the original implementation on 5 seeded datasets, including one tie-heavy dataset with many equal timestamps and short promo windows. 2. On 200k orders / 100k prices / 20k promos, a single run must take < 4.0 s wall-clock (run in a subprocess capped at 30 s; timeout = fail).
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

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  "session_id": "[REDACTED]",
  "total_cost_usd": 0.0633008,
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   "output_tokens_details": {
    "thinking_tokens": 480
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   "service_tier": "standard",
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     "cache_creation_input_tokens": 2008,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 2008
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
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  "modelUsage": {
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    "inputTokens": 6,
    "outputTokens": 2487,
    "cacheReadInputTokens": 49468,
    "cacheCreationInputTokens": 8368,
    "webSearchRequests": 0,
    "costUSD": 0.0633008,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 480,
    "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": {
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   "completed": 0,
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   "killed": {
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   "refused": {
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  "safety_stops": 0,
  "is_error": false,
  "num_turns": 3,
  "subtype": "success",
  "api_error_status": null,
  "result": "`price_orders` in `pricing.py` now runs the 200k orders / 100k prices / 20k promos case in about 0.65 s, well under your 3 s target. Its output matches the original function on every check I ran.\n\n- **Price lookup:** prices are grouped by SKU and stably sorted by `effective_at`. Each order then does one binary search. Ties go to the row that appears last in the list, as the docstring says. Orders before any price for their SKU still get `None`.\n- **Promo discount:** orders and promos are grouped by SKU and swept in time order. A heap tracks the active promos, so each order takes the largest `pct` among promos whose window `[start, end)` contains its timestamp. I kept the original code's rule (largest `pct` wins, floor of 0) rather than the docstring's \"latest promo\" wording, since the two disagree.\n- **Output:** the rounding and the order of the output list are unchanged.\n\nI saved the original as `reference_pricing.py` so the test has something to compare against. `test_pricing.py` compares the two on 200 small random datasets with timestamps squeezed together, so ties and window-boundary hits are common. It passes. I also compared the two directly on 3,000 orders with the full 100k prices and 20k promos, and they matched. I did not compare them on the full 200k orders, because the original would take hours.\n\nI ran the test with `python3 test_pricing.py`.",
  "ttft_ms": 1719,
  "type": "result",
  "duration_ms": 34243,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1022,
  "time_to_request_ms": 282,
  "first_content_frame_ms": 1023,
  "queued_turn_count": 0,
  "result_index": 0
 }
]