Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Implement a spec exactly, with nothing extra
AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener
- Estimated cost
- $0.0029
- Wall time
- 9s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
Implement SPEC.md exactly. Then stop. Do not add anything the spec does not ask for.
Task prompt given to the agent (headless, offline, synthetic fixture inputs): Implement SPEC.md exactly. Then stop. Do not add anything the spec does not ask for. Inspired by https://x.com/mave99a/status/2096480295692460432 (unverified; not checked with the author). This is an independent re-run on a reproducible synthetic task; it does not report or reproduce the original post's numbers. Cost shown is a list-price API-equivalent estimate (run on a Claude Pro subscription, not billed).
Success criteria: All automated rubric checks pass: hidden test test_flow; hidden test test_overwrite; under 60 non-blank lines; no classes; no extra files; stdlib only (no non-stdlib imports).
Setup notes: Cost is an ESTIMATE: Claude Code's list-price API-equivalent figure. The run used a Claude Pro subscription, so nothing was billed. input_tokens = all input incl. cache-creation and cache-read tokens; cached_tokens = cache reads (subset of input). Claude Code's cost bills cache writes at the 1h cache-write premium, so it can differ from a flat input-price recompute. Web disabled; tools Read/Write/Edit/Glob/Grep/Bash; single run; scored automatically (6/6 checks).
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.0029
- Wall time
- 9s
- Human step-ins
- 0
- Turns
- 4
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on | 77,888 | 1,359 | $0.0029 |
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 automated rubric checks pass: hidden test test_flow; hidden test test_overwrite; under 60 non-blank lines; no classes; no extra files; stdlib only (no non-stdlib imports).
- 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": 7825,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.0029270400000000005,
"usage": {
"input_tokens": 8,
"cache_creation_input_tokens": 7726,
"cache_read_input_tokens": 70154,
"output_tokens": 1359,
"output_tokens_details": {
"thinking_tokens": 111
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 7726,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 266,
"cache_read_input_tokens": 19852,
"cache_creation_input_tokens": 413,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 413
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-haiku-5-5": {
"inputTokens": 8,
"outputTokens": 1359,
"cacheReadInputTokens": 70154,
"cacheCreationInputTokens": 7726,
"webSearchRequests": 0,
"costUSD": 0.0029270400000000005,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 111,
"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": 4,
"subtype": "success",
"api_error_status": null,
"result": "I implemented `SPEC.md` as `kv.py` in the work directory and stopped there.\n\nIt's a single file using only the Python 3 standard library, with 43 non-blank lines. It supports `set`, `get`, `del` and `list` against `kv.json` in the current directory, with the output and exit codes the spec gives.\n\nI smoke-tested it in a temporary directory: set, get, list, delete, and the `NOT FOUND` exit-1 cases all behaved as specified. The temporary directory was removed afterwards.\n\nThe spec doesn't cover a few cases, so I made these choices:\n- A missing `kv.json` is treated as an empty store.\n- Wrong arguments or an unknown command print a one-line usage message and exit 1.\n- `NOT FOUND` goes to stdout, like the other results.",
"ttft_ms": 1374,
"type": "result",
"duration_ms": 8295,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1219,
"time_to_request_ms": 214,
"first_content_frame_ms": 1220,
"queued_turn_count": 0,
"result_index": 0
}