Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Effort dial sweep on one well-specified feature
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $0.02
- 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-haiku-5.5: $0.019465. 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
- $0.02
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 13
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 394,072 | 19,931 | $0.02 |
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: 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": 91563,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.019464550000000004,
"usage": {
"input_tokens": 24,
"cache_creation_input_tokens": 29243,
"cache_read_input_tokens": 364805,
"output_tokens": 19931,
"output_tokens_details": {
"thinking_tokens": 10430
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 29243,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 1108,
"cache_read_input_tokens": 41564,
"cache_creation_input_tokens": 218,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 218
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 24,
"outputTokens": 19931,
"cacheReadInputTokens": 364805,
"cacheCreationInputTokens": 29243,
"webSearchRequests": 0,
"costUSD": 0.019464550000000004,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 10430,
"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": 13,
"subtype": "success",
"api_error_status": null,
"result": "API-88 is done: the rate limiter is in `src/middleware/rateLimit.js`, wired into `createApp`, and covered by 16 new tests. `npm test` passes all 20 tests (the 4 existing plus 16 new), `npm run lint` is clean, and the suite passed on repeated runs. I haven't committed anything.\n\n**How it works**\n- Each request runs one Lua script in Redis that refills the bucket, takes a token if one is available, and sets the key's TTL. Redis runs a script atomically, so concurrent requests on any instance can't both take the last token.\n- The script reads time from Redis `TIME`, so clock skew between app instances doesn't affect refill.\n- Buckets expire after the time it takes an empty bucket to refill (at least 1 s), so keys don't live forever.\n- Allowed requests get `X-RateLimit-Limit` and `X-RateLimit-Remaining`. Denied requests get 429 with `{\"error\":\"rate_limited\"}`, `Retry-After` (whole seconds, rounded up, minimum 1), and `Remaining: 0`.\n- `createApp({ rateLimit: {...} })` mounts the limiter on `/notes` only, so `/health` is never limited.\n\n**Choices I made beyond the spec**\n- **Fail-open timeout:** Redis can hang without erroring, so each check has a `timeoutMs` (default 200 ms). If it doesn't answer in time, the request goes through.\n- **Known-down short-circuit:** if the ioredis client is `reconnecting`, `close`, or `end`, requests are let through immediately rather than waiting for the timeout.\n- **`onError` option:** fail-open errors are logged to `console.error` by default, and you can pass your own handler.\n- **`keyFn` errors** are passed to `next(err)`, not treated as a Redis failure.\n- **429 also sets `X-RateLimit-Limit`**, for consistency with allowed responses.\n- **Option validation:** `createRateLimiter` throws on a missing `redis`, a non-positive-integer `capacity`, or a non-positive `refillPerSec`.\n- **README** updated to describe the limiter; it previously said Redis wasn't used.\n\n**Not changed**\n- `src/server.js` still calls `createApp({ redis })`, so the limiter is off in the running server until you pass `rateLimit` there. I didn't add env-based config because the ticket didn't ask for it.\n\n**Tests** (`test/rateLimit.test.js`, using `REDIS_URL` with a unique key prefix per test) cover: the 429 path and handler skipping, header values, refill over time, `Retry-After` rounding, per-key buckets, the default `req.ip` key and `rl:` prefix, TTLs, sharing across two app instances, 40 concurrent requests across two instances producing exactly 10 allowed, fail-open on errors, a closed client, and a timeout, `/notes` vs `/health` wiring, option validation, and `keyFn` errors.",
"ttft_ms": 1849,
"type": "result",
"duration_ms": 101422,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1756,
"time_to_request_ms": 252,
"first_content_frame_ms": 1756,
"queued_turn_count": 0,
"result_index": 0
}
]