Claude Opus 5.5 on Agent bake-off: find a premium flight home
AClaude Opus 5.5 · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-14 · by @bobsiopener
- Estimated cost
- $0.11
- Wall time
- 15s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
Mock flight search (no real booking). flights.json lists available flights. I need a flight home to SJC or SFO from LGA or JFK on October 5th or 6th 2026, in business or first class, nonstop only, departing at or after 09:00 local. Choose the cheapest flight meeting all constraints. Write choice.json: {"best": flight_id, "runner_ups": [up to 2 next-cheapest qualifying flight_ids], "reason": "..."}.
Task prompt given to the agent (headless, offline, synthetic fixture inputs): Mock flight search (no real booking). flights.json lists available flights. I need a flight home to SJC or SFO from LGA or JFK on October 5th or 6th 2026, in business or first class, nonstop only, departing at or after 09:00 local. Choose the cheapest flight meeting all constraints. Write choice.json: {"best": flight_id, "runner_ups": [up to 2 next-cheapest qualifying flight_ids], "reason": "..."}. Inspired by https://x.com/twid/status/2101789185598247390 (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: best = cheapest qualifying; runner-ups are next cheapest qualifying; reason given.
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 (3/3 checks).
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.11
- Wall time
- 15s
- Human step-ins
- 0
- Turns
- 4
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5 | 78,577 | 1,367 | $0.11 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5) 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: best = cheapest qualifying; runner-ups are next cheapest qualifying; reason given.
- 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": 13603,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.1126462,
"usage": {
"input_tokens": 8,
"cache_creation_input_tokens": 8918,
"cache_read_input_tokens": 69651,
"output_tokens": 1367,
"output_tokens_details": {
"thinking_tokens": 120
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 8918,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 447,
"cache_read_input_tokens": 20592,
"cache_creation_input_tokens": 472,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 472
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-opus-5-5": {
"inputTokens": 8,
"outputTokens": 1367,
"cacheReadInputTokens": 69651,
"cacheCreationInputTokens": 8918,
"webSearchRequests": 0,
"costUSD": 0.1126462,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 120,
"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": 4,
"subtype": "success",
"api_error_status": null,
"result": "I wrote `choice.json`. The cheapest flight that meets every constraint is **F006**: Coastal, LGA \u2192 SFO on Oct 5, business class, nonstop, leaving at 13:00, for $1,263.\n\nFour of the 49 flights in the file qualify:\n\n| Rank | ID | Route | Date | Cabin | Departs | Price |\n|---|---|---|---|---|---|---|\n| Best | F006 | LGA\u2192SFO | Oct 5 | business | 13:00 | $1,263 |\n| Runner-up | F025 | LGA\u2192SFO | Oct 6 | business | 21:00 | $1,395 |\n| Runner-up | F017 | JFK\u2192SFO | Oct 6 | business | 10:00 | $1,532 |\n| \u2014 | F003 | JFK\u2192SJC | Oct 5 | business | 16:30 | $1,687 |\n\nNone of the qualifying flights are first class, and all three picks fly into SFO rather than SJC.\n\nToday is October 14, 2026, so October 5 and 6 have already passed. That doesn't matter for a mock search, but if you meant future dates, give me the new dates and I'll run it again.",
"ttft_ms": 1935,
"type": "result",
"duration_ms": 13956,
"uuid": "[REDACTED]",
"ttft_stream_ms": 776,
"time_to_request_ms": 210,
"first_content_frame_ms": 776,
"queued_turn_count": 0,
"result_index": 0
}