Claude Opus 5.5 (effort medium · thinking on) on Effort dial sweep on one well-specified feature
AClaude Opus 5.5medium · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $0.36
- Wall time
- 2m
- 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-opus-5.5: $0.36226. 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
- $0.36
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 10
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5effort medium · thinking on | 172,518 | 9,364 | $0.36 |
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 medium · 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
[
{
"duration_api_ms": 83535,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.3622602000000001,
"usage": {
"input_tokens": 14,
"cache_creation_input_tokens": 18003,
"cache_read_input_tokens": 154501,
"output_tokens": 9364,
"output_tokens_details": {
"thinking_tokens": 1871
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 18003,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 855,
"cache_read_input_tokens": 29769,
"cache_creation_input_tokens": 380,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 380
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-opus-5-5": {
"inputTokens": 14,
"outputTokens": 9364,
"cacheReadInputTokens": 154501,
"cacheCreationInputTokens": 18003,
"webSearchRequests": 0,
"costUSD": 0.3622602000000001,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 1871,
"canonicalModel": "claude-opus-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 for API-88 is in place and applies to the `/notes` routes. All 19 tests pass (4 existing, 15 new), I ran the suite 5 times with no flaky failures, and `npm run lint` is clean. Nothing is committed yet.\n\n**What I added**\n- **`src/middleware/rateLimit.js`**: `createRateLimiter(options)` checks and takes a token in a single Redis script, so it's atomic even when several app instances share one Redis. The script uses Redis's own clock, so clock differences between servers don't matter. Each bucket key expires once its bucket would be full again, plus one second. The headers, the 429 response and the `Retry-After` value (whole seconds, rounded up, at least 1) follow the ticket.\n- **`src/app.js`**: `createApp({ rateLimit: {...} })` puts the limiter in front of the `/notes` router only. `/health` isn't limited, and without the option the app behaves exactly as before.\n- **`test/rateLimit.test.js`**: covers the headers, the 429 response, `Retry-After` rounding and its minimum of 1, refill over time capped at capacity, per-key buckets and prefixes, the default `req.ip` key, the TTL, `/health` exemption, option checks, and the fail-open cases. The atomicity test sends 40 requests at once through two app instances with separate Redis clients and checks that exactly 5 get through.\n- **README**: a short section on the option.\n\n**Choices beyond the ticket**\n- **Two extra options:**\n - `timeoutMs` (default 1000): by default ioredis queues commands while disconnected and retries them, so a Redis outage would make requests hang instead of failing open. After the timeout the request is let through.\n - `onError`: a hook for logging Redis errors. By default it writes to `console.error`.\n- **Input checks:** the limiter throws at startup if `redis` is missing, `capacity` isn't a positive integer, or `refillPerSec` isn't a positive number.\n- **Bad keys fail open:** if `keyFn` throws, the request is let through, the same as a Redis failure.\n- **Proxies:** the default key `req.ip` only shows the real client address if `trustProxy` is set correctly. Behind a load balancer without it, all clients share one bucket.\n\n`src/server.js` doesn't turn on rate limiting, because the ticket didn't give production limits. If you give me the values, I can set it up there, for example from environment variables.",
"ttft_ms": 3766,
"type": "result",
"duration_ms": 112483,
"uuid": "[REDACTED]",
"ttft_stream_ms": 2458,
"time_to_request_ms": 294,
"first_content_frame_ms": 2459,
"queued_turn_count": 0,
"result_index": 0
}
]