Claude Haiku 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 Haiku 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: $0.01
Wall time: 2.0h
Human step-ins: 0
Outcome: Pass

Compare with other models on this task →

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: 0.40. Claude Code's own reported cost for claude-haiku-5.5: $0.011185. 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: $0.01
Wall time: 2.0h
Human step-ins: 0
Turns: 8

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	197,010	9,171	$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. 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.

Was this useful?

⇄ Rerun on another model

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": 235083,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.01118525,
  "usage": {
   "input_tokens": 16,
   "cache_creation_input_tokens": 24359,
   "cache_read_input_tokens": 172635,
   "output_tokens": 9171,
   "output_tokens_details": {
    "thinking_tokens": 4600
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 24359,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 678,
     "cache_read_input_tokens": 28735,
     "cache_creation_input_tokens": 797,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 797
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 16,
    "outputTokens": 9171,
    "cacheReadInputTokens": 172635,
    "cacheCreationInputTokens": 24359,
    "webSearchRequests": 0,
    "costUSD": 0.01118525,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 4600,
    "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": 8,
  "subtype": "success",
  "api_error_status": null,
  "result": "`price_orders` in `pricing.py` now runs in about 0.55s on 200k orders, 100k prices and 20k promos, down from hours. The new correctness test passes against the original implementation.\n\n**What changed**\n- **Price lookup:** prices are grouped per SKU and sorted by `(effective_at, list index)`. A `bisect_right` on the timestamps then picks the latest row with `effective_at <= ts`, and among equal timestamps the row that appears last in the list, as the docstring requires.\n- **Promos:** for each SKU, orders are swept in timestamp order. Promos are activated once their `start` has passed and kept in a max-heap keyed by `pct`. Expired tops are popped, since timestamps only increase. The window stays `[start, end)`, and the `max(0, \u2026)` floor from the original is kept.\n- **Rounding:** the arithmetic expressions are unchanged, so the floats and `round` results are identical.\n- Function name and signature are unchanged. Only stdlib is used (`bisect`, `heapq`).\n\n**Test:** `test_pricing.py` keeps the original implementation as `price_orders_reference` and checks the new version against it on:\n- 300 small random cases with many timestamp ties, overlapping or touching promos, unknown SKUs, and negative and float pcts;\n- `make_data.make` output with three seeds;\n- hand-written edge cases: empty inputs, equal `effective_at`, and the `[start, end)` boundaries.\n\nTo confirm the test has teeth, I ran it against two broken variants. Switching to `bisect_left` failed 239 subtests, and making the promo `end` inclusive failed 184. The real version passes.\n\n**Not verified:** I did not run the original on the full 200k-order size, since it would take hours. Equality is established by the random and seeded comparisons at smaller sizes, not at production scale.",
  "ttft_ms": 1119,
  "type": "result",
  "duration_ms": 239337,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1011,
  "time_to_request_ms": 253,
  "first_content_frame_ms": 1011,
  "queued_turn_count": 0,
  "result_index": 0
 }
]