Skip to content

Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Split a campaign budget across channels whose response only kicks in above a minimum spend

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
$0.09
Wall time
13s
Human step-ins
0
Outcome
Pass

Compare with other models on this task →

Summary

Exact prompt

# Allocate the campaign budget across channels Inputs: `channels.csv` (channel, units, expected_signups: the expected signups when the channel gets that many budget units; a channel with 0 units gives 0) and `budget.json` (`units`: how many whole units to allocate, `max_units`: per-channel cap). Rules: - Every unit is a whole unit; a channel can get 0 units up to its cap. Units not listed for a channel are not allowed for that channel. - Spend at most the budget. Choose the allocation with the highest total expected signups. The data has exactly one best allocation. - Response is not linear: some channels give little or nothing until they reach a minimum spend. - what_if_signups = the best total if the budget had 2 more units (same caps). Write `out/allocation.json`: `{"allocation": {"<channel>": <units>, ...}, "total_signups": <int>, "units_used": <int>, "what_if_signups": <int>}`. List every channel, including those with 0. ## 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.

Allocate 10-14 whole budget units across five channels with per-channel caps. Response tables are S-shaped or threshold-gated, so adding units one at a time to the best next-unit gain gets stuck. Return the unique best allocation and a +2 unit what-if. Every seed is drawn so that marginal greedy and an equal split are strictly worse, and ignoring the caps changes the answer. 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_allocation optimal_total caps_respected what_if_budget

Setup notes: Batch 18 case t07-threshold-allocation (offline, synthetic inputs, seed 3073822060). 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 0.20 (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: $0.085566. 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
$0.09
Wall time
13s
Human step-ins
0
Turns
3
Model usage
ModelInput tokOutput tokCost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on55,663948$0.09

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_allocation optimal_total caps_respected what_if_budget
  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.

Was this useful?

⇄ Rerun on another model

Automated checks

Trust 80% comes from these checks (how Trust % works).

Evidence log
[
 {
  "duration_api_ms": 11966,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.0855656,
  "usage": {
   "input_tokens": 6,
   "cache_creation_input_tokens": 7109,
   "cache_read_input_tokens": 48548,
   "output_tokens": 948,
   "output_tokens_details": {
    "thinking_tokens": 18
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 7109,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 334,
     "cache_read_input_tokens": 18485,
     "cache_creation_input_tokens": 770,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 770
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-opus-5-5": {
    "inputTokens": 6,
    "outputTokens": 948,
    "cacheReadInputTokens": 48548,
    "cacheCreationInputTokens": 7109,
    "webSearchRequests": 0,
    "costUSD": 0.0855656,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 18,
    "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": 3,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote the allocation to `out/allocation.json`. With 11 units, the best split gives **883 expected signups**:\n\n| Channel | Units |\n|---|---|\n| Search | 1 |\n| Affiliates | 1 |\n| Social | 2 |\n| Podcast | 7 (its cap) |\n| Email | 0 |\n\nAll 11 units are used. I checked every possible allocation within the budget and caps, so this is the true maximum. The next best gives 882, so the best is unique as the task says.\n\n**With 2 more units (13), the best total is 977.** That extra budget changes the plan rather than just adding to it: Email goes from 0 to 3 units and Social drops to 1, giving Search 1, Affiliates 1, Social 1, Podcast 7, Email 3. This happens because Email returns almost nothing below 3 units (1 signup at 1 unit, 4 at 2) and then jumps to 133 at 3 units. Podcast works the same way, which is why it takes 7 of the 11 units.",
  "ttft_ms": 1983,
  "type": "result",
  "duration_ms": 12351,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1016,
  "time_to_request_ms": 259,
  "first_content_frame_ms": 1016,
  "queued_turn_count": 0,
  "result_index": 0
 }
]

Get new runs and ranking changes weekly