Claude Sonnet 5.5 (effort xhigh · thinking on) on Effort dial sweep on one well-specified feature
AClaude Sonnet 5.5xhigh · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $1.07
- Wall time
- 14m
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
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: $1.066967. 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
- $1.07
- Wall time
- 14m
- Human step-ins
- 0
- Turns
- 20
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort xhigh · thinking on | 1,248,537 | 65,534 | $1.07 |
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 xhigh · 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?
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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"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 1.0669666000000002,
"usage": {
"input_tokens": 38,
"cache_creation_input_tokens": 73513,
"cache_read_input_tokens": 1174986,
"output_tokens": 65534,
"output_tokens_details": {
"thinking_tokens": 41665
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 73513,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 1158,
"cache_read_input_tokens": 91334,
"cache_creation_input_tokens": 942,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 942
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-sonnet-5-5": {
"inputTokens": 38,
"outputTokens": 65534,
"cacheReadInputTokens": 1174986,
"cacheCreationInputTokens": 73513,
"webSearchRequests": 0,
"costUSD": 1.0669666000000002,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 41665,
"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": 20,
"subtype": "success",
"api_error_status": null,
"result": "API-88 is done. `createApp({ rateLimit: { redis, capacity, refillPerSec, ... } })` now rate-limits every `/notes` route and leaves `/health` alone. `npm run lint` is clean and all 39 tests pass (the 4 existing plus 35 new). The suite was stable across 8 runs, including 3 under CPU load, and leaves no keys behind in Redis. I haven't committed anything.\n\n## What changed\n- **`src/middleware/rateLimit.js`** (new) exports `createRateLimiter(options)`.\n - **Atomicity:** the check-and-consume is a single Lua script (`EVALSHA`, falling back to `EVAL` after a script-cache flush). It uses Redis's own `TIME`, so app servers with skewed clocks still agree.\n - **Responses:**\n - Success calls `next()` and sets `X-RateLimit-Limit` and `X-RateLimit-Remaining`.\n - An empty bucket gets `429 {\"error\":\"rate_limited\"}` with `X-RateLimit-Remaining: 0` and a `Retry-After` in whole seconds, rounded up, at least 1. `next()` is not called.\n - **TTL:** each key expires once the bucket would be full again, since that state is the same as having no key.\n - **Fail open:** Redis errors, a closed connection and an unreachable server all let the request through.\n- **`src/app.js`:** mounts the limiter on `/notes`.\n- **`README.md`:** documents the limiter.\n\n## Decisions to check\n- **Order change in `app.js`:** the limiter runs before `express.json()`, so malformed or oversized bodies can't skip the limit. To give 429s an `x-request-id`, I moved `requestId` above `express.json()` too. A JSON parse-error 400 now also carries `x-request-id`.\n- **Two options you didn't list:**\n - `timeoutMs` (default 250). With ioredis defaults an unreachable Redis would hang each request for ~10s while commands sit in its offline queue, which isn't failing open in practice.\n - `onError(err, req)` (default: `console.error`, at most once per 10s). Without it, failing open would be invisible.\n- **A bad `keyFn` result is a 500.** A throwing `keyFn`, or one returning anything other than a non-empty string or number, goes to the error handler. Otherwise omitting an API-key header could bypass the limit. This applies to the limiter's own bugs, not to Redis failures.\n- **Config validation:** the limiter throws at construction for an invalid `capacity` or `refillPerSec`, and for a `refillPerSec` so small the key TTL would overflow. Without that last check, Redis would reject the TTL on every call and the limiter would silently stop limiting.\n\n## Not done\n- **`src/server.js` is unchanged, so the running server doesn't rate-limit yet.** It already creates a Redis client, but turning the limiter on means choosing production numbers for `capacity` and `refillPerSec`, and `trustProxy` if you're behind a load balancer. Tell me the numbers and I'll wire it, or I can make it opt-in via env vars.\n- **Behind a proxy you need `trustProxy`.** Without it, every client shares one bucket under the default `req.ip` key.\n\nI checked the tests against three deliberately broken limiters: non-atomic, no TTL, and fail-closed. Each was caught by the tests meant to catch it.",
"ttft_ms": 1759,
"type": "result",
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"uuid": "[REDACTED]",
"ttft_stream_ms": 1602,
"time_to_request_ms": 233,
"first_content_frame_ms": 1602,
"queued_turn_count": 0,
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}
]