Claude Sonnet 5.5 (effort high · thinking on) on Effort dial sweep on one well-specified feature

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

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.22
Wall time: 2m
Human step-ins: 0
Outcome: Pass

Compare with other models on this task →

Exact prompt

You are working in the notes-api repository (Express 4, Node 20, node:test + supertest, ESLint). A Redis server is running for you at the URL in the REDIS_URL environment variable (db 0 is scratch space you may flush). Ticket API-88: Add a rate-limiter middleware. Create src/middleware/rateLimit.js exporting createRateLimiter(options) that returns Express middleware implementing a per-key token bucket stored in Redis, so that several app instances sharing one Redis share the same limits. Options: - redis: an ioredis client (required) - capacity: bucket size (max burst), integer - refillPerSec: tokens added per second (may be fractional) - keyFn(req): returns the bucket key; default is req.ip - prefix: Redis key prefix, default 'rl:' Behaviour: - Each request consumes one token. If a token is available, call next() and set X-RateLimit-Limit (capacity) and X-RateLimit-Remaining (tokens left, integer, after this request). - If the bucket is empty, respond 429 with JSON {"error":"rate_limited"}, a Retry-After header (whole seconds, rounded up, at least 1) and X-RateLimit-Remaining: 0. Do not call next(). - The check-and-consume must be atomic under concurrent requests, including across app instances. - Bucket keys must not live forever in Redis: give them a TTL. - If Redis fails (error or unavailable), fail open: let the request through and do not crash. Wire it into the app so that createApp({ rateLimit: { redis, capacity, refillPerSec, ... } }) applies the limiter to all /notes routes (not /health). Add thorough tests (at least 8), keep the existing tests passing, and make sure `npm run lint` is clean.

The same feature ticket, a Redis-backed token-bucket rate limiter for an Express API, run at a range of reasoning-effort levels on models of different sizes. Nine hidden acceptance tests check burst limits, refill, atomicity under concurrency, sharing across app instances, key TTLs and fail-open behaviour, alongside lint and the agent's own tests. It shows what extra effort buys in tokens, cost and quality. 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: All provided acceptance tests pass; agent-written tests ≥ 8; no lint errors.

Setup notes: Synthetic Express 4 notes API (batch2/p02/repo) with a local redis-server 7 on the box. Same prompt for every run; only --model/--effort differ. Scored with 9 hidden acceptance tests (batch2/p02/hidden/acceptance.test.js, run after the agent finished), the agent's own test count (test()/it() calls outside the original notes tests), npm test and eslint. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Claude Code's own reported cost for claude-sonnet-5.5: $0.222118. Score: 9/9 acceptance. 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. Comparison on this use case: the same ticket at every Claude Code effort level (low, medium, high, xhigh, max) for Opus 5.5 and Sonnet 5.5, plus Haiku 5.5 at the default effort.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.22
Wall time: 2m
Human step-ins: 0
Turns: 10

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort high · thinking on	212,202	12,029	$0.22

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 high · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: All provided acceptance tests pass; agent-written tests ≥ 8; no lint errors.
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": 86653,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.22211849999999997,
  "usage": {
   "input_tokens": 16,
   "cache_creation_input_tokens": 20661,
   "cache_read_input_tokens": 191525,
   "output_tokens": 12029,
   "output_tokens_details": {
    "thinking_tokens": 2295
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 20661,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 777,
     "cache_read_input_tokens": 32520,
     "cache_creation_input_tokens": 680,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 680
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-sonnet-5-5": {
    "inputTokens": 16,
    "outputTokens": 12029,
    "cacheReadInputTokens": 191525,
    "cacheCreationInputTokens": 20661,
    "webSearchRequests": 0,
    "costUSD": 0.22211849999999997,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 2295,
    "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,
    "system": 0
   },
   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 10,
  "subtype": "success",
  "api_error_status": null,
  "result": "The rate limiter is in and wired into `createApp({ rateLimit })` for all `/notes` routes, but not `/health`. All 21 tests pass (4 existing, 17 new), and I ran the suite three times with no flakes. `npm run lint` is clean.\n\n- **Atomicity:** the refill, check and consume steps run in one Lua script, so concurrent requests and multiple app instances all see the same bucket. The script reads the time from Redis, so clock skew between instances doesn't matter.\n- **Concurrency tests:** 40 parallel requests against a capacity-5 bucket let exactly 5 through. A second test splits 30 requests across two apps, each with its own Redis client, and exactly 6 get through against a capacity of 6.\n- **TTL:** after every request the key expires once the bucket would be full again, plus a 1 s margin. An expired bucket behaves like a fresh one.\n- **Headers:** a 429 sends `{\"error\":\"rate_limited\"}`, `Retry-After` in whole seconds (rounded up, minimum 1) and `X-RateLimit-Remaining: 0`.\n- **Fail open:** a Redis error, an unreachable Redis, a throwing `keyFn` and a throwing `onError` hook all let the request through.\n\nA few things go beyond the ticket:\n- **Extra options:** `timeoutMs` (default 500) and `onError`.\n  - **`timeoutMs`:** ioredis queues commands while Redis is down, so without a timeout requests would hang instead of failing open.\n  - **`onError`:** without it, failures are logged to the console at most once every 10 s.\n- **Option validation:** `createRateLimiter` throws at setup if `redis` is missing, `capacity` isn't a positive integer, or `refillPerSec` isn't greater than 0.\n- **Missing key:** if `keyFn` returns `undefined` or `null`, I skip limiting for that request rather than putting everyone in one shared bucket.\n- **README:** I added a short section on the limiter.\n\nI didn't enable the limiter in `src/server.js`, since the ticket only asked for the `createApp` wiring. Turning it on in production means passing a `rateLimit` option there. If the app runs behind a proxy, set `trustProxy`, or every client will share the proxy's IP.\n\nNothing is committed.",
  "ttft_ms": 2451,
  "type": "result",
  "duration_ms": 114434,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1663,
  "time_to_request_ms": 232,
  "first_content_frame_ms": 1663,
  "queued_turn_count": 0,
  "result_index": 0
 }
]