Claude Opus 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 Opus 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: $0.19
Wall time: 48s
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: 0.54. Claude Code's own reported cost for claude-opus-5.5: $0.191382. 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: $0.19
Wall time: 48s
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	100,670	4,407	$0.19

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. 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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    "canonicalModel": "claude-opus-5-5",
    "provider": "firstParty",
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  "result": "`price_orders` now takes about **0.53 s** on the full 200k orders / 100k prices / 20k promos, against the 3 s target. Its output matches the original exactly in every test I ran.\n\n**How it works now** (`pricing.py`):\n- **Prices:** rows are grouped by SKU and stably sorted by `effective_at`, then each order is looked up with a binary search (`bisect_right`). Because the sort is stable, when several rows share the same `effective_at`, the one that came later in the list is found, so the \"last row wins\" rule still holds.\n- **Promos:** for each SKU, orders are processed in time order while a max-heap tracks the promos currently running. Promos with `end <= ts` are thrown away. This gives the same result as the original's largest-pct-of-all-matching-promos (never below 0).\n- **Unchanged:** the final arithmetic and rounding are the same expressions as before, so the numbers come out identical. The function name and signature are the same, and it uses only the standard library.\n\n**Tests** (`test_pricing.py`, run with `python3 test_pricing.py`, both pass):\n- 500 random small cases built to hit the edge cases: duplicate `effective_at` values, orders exactly on a promo's start or end, overlapping promos, empty promo windows, orders before any price, and unknown SKUs.\n- 3 cases generated by `make_data.py` at 2000/1000/400.\n\nI kept the original code as `pricing_reference.py` so the tests have something to compare against. I didn't run the original on the full 200k dataset because it takes hours. Instead, I checked 200 random orders priced against the full price and promo tables, and they matched.\n\n**Docstring mismatch:** the docstring says \"apply the latest promo \u2026 (if several, the largest pct wins)\", but the original code ignores which promo is latest. It just takes the largest pct among all promos that cover the order time. I copied what the code does, since you asked for identical output. You may want to reword the docstring.",
  "ttft_ms": 2559,
  "type": "result",
  "duration_ms": 47107,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 2089,
  "time_to_request_ms": 196,
  "first_content_frame_ms": 2089,
  "queued_turn_count": 0,
  "result_index": 0
 }
]