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Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Pick the most valuable set of jobs for one crew when moving between sites costs time

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-21 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.11
Wall time
17s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Build the most valuable day schedule for one crew Inputs: `jobs.csv` (job_id, site, start, end, value_usd; times are HH:MM on the same day) and `rules.json` (`changeover_minutes`). Rules: - The crew does one job at a time and must do a job from its start to its end. - Between two consecutive jobs at different sites the crew needs at least `changeover_minutes` from the end of one to the start of the next. Consecutive jobs at the same site only need the next start to be at or after the previous end. - Choose the set of jobs with the highest total value. The data has exactly one best set. - what_if_value = the best total value if changeover_minutes were doubled. Write `out/schedule.json`: `{"jobs": ["<job_id>", ... in start order], "total_value": <usd>, "job_count": <int>, "what_if_value": <usd>}`. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.

Choose jobs for one crew from 22-28 timed offers across four sites. Moving between different sites needs a changeover gap; staying on one site does not. Return the unique best set and the best value if the gap doubled. Every seed is drawn so that earliest-finish-first and highest-value-first greedy rules are strictly worse than the optimum, and ignoring changeovers gives a different set. 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). Folder-restore note: this run's output files under work/out/ were lost in a 1:22 AM ET restore on Oct 21, 2026. The trace, step-in exchanges and output-hash list were checked against the recorded SHA-256 values and match; the hashes of the individual output files inside that list can no longer be recomputed. Measured data unchanged.

Success criteria: optimal_jobs optimal_value changeover_respected what_if_doubled_gap

Setup notes: Batch 18 case t05-crew-day-schedule (offline, synthetic inputs, seed 3186719906). Autonomous case: no stand-in user and no step-ins expected; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths); pass = every check true and no ask/ files. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.16 (8 cores). Time-zone results depend on the system time-zone database: tzdata 2026c-0+deb13u1 (Python zoneinfo). Claude Code's own reported cost for claude-opus-5.5: $0.109777. Score: 4/4. 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.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.11
Wall time
17s
Human step-ins
0
Turns
4
Model usage
ModelInput tokOutput tokCost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on75,0391,677$0.11

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. It counts as a success only if it meets every success criterion: optimal_jobs optimal_value changeover_respected what_if_doubled_gap
  4. 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.

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Automated checks

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Evidence log
[
 {
  "duration_api_ms": 16346,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.109777,
  "usage": {
   "input_tokens": 8,
   "cache_creation_input_tokens": 7846,
   "cache_read_input_tokens": 67185,
   "output_tokens": 1677,
   "output_tokens_details": {
    "thinking_tokens": 264
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 7846,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 566,
     "cache_read_input_tokens": 18959,
     "cache_creation_input_tokens": 1033,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 1033
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-opus-5-5": {
    "inputTokens": 8,
    "outputTokens": 1677,
    "cacheReadInputTokens": 67185,
    "cacheCreationInputTokens": 7846,
    "webSearchRequests": 0,
    "costUSD": 0.109777,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 264,
    "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 `out/schedule.json`. The best schedule is worth $4,861 across 5 jobs, and no other set of jobs reaches that total.\n\n| Job | Site | Time | Value |\n|---|---|---|---|\n| J22 | Mill St | 06:30\u201309:30 | $1,044 |\n| J14 | Mill St | 09:30\u201311:00 | $666 |\n| J15 | Elm Park | 13:00\u201313:45 | $893 |\n| J24 | Elm Park | 15:30\u201316:15 | $1,229 |\n| J06 | Depot | 17:15\u201318:15 | $1,029 |\n\nI checked each step against the rules by hand:\n- **J22 to J14:** same site, so J14 can start right at 09:30 when J22 ends.\n- **J14 to J15:** different sites, with a 120-minute gap.\n- **J24 to J06:** different sites, with a gap of exactly 60 minutes. That meets the \"at least 60 minutes\" rule, so the schedule depends on reading it as \"at least\".\n\n**What-if:** with the changeover doubled to 120 minutes, the best total drops to $4,031. That schedule is J22, J14, J20, J12, J06, and it is also the only best set.\n\nI used a short Python script that tries every valid sequence of jobs and counts how many sets tie for the top value. It's in `/tmp/s/solve.py`, outside `out/`.",
  "ttft_ms": 1958,
  "type": "result",
  "duration_ms": 16699,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 920,
  "time_to_request_ms": 245,
  "first_content_frame_ms": 920,
  "queued_turn_count": 0,
  "result_index": 0
 }
]

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