Split a campaign budget across channels whose response only kicks in above a minimum spend
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
Best model for this task so far: DeepSeek V4.1 Flash (Venice) (thinking on) (success, $0.0032, 10s).
All runs (5)
100% pass rate across 5 eligible runs: 5 pass, 0 partial, 0 fail. Partial and failed runs are listed below with everything else.ⓘ
| Model | Cost | Time | Step-insⓘ | Autonomy | Outcome | Trustⓘ | Date | Compare |
|---|---|---|---|---|---|---|---|---|
| DDeepSeek V4.1 Flash (Venice)thinking on | $0.0032 | 10s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-28 | |
| ZGLM 4.7 (Venice)thinking on | $0.04 | 8m | 0 | Fully autonomous | pass | Trust 80% | 2026-10-28 | |
| AClaude Opus 5.5default · thinking on | $0.09 | 13s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-21 | |
| AClaude Haiku 5.5default · thinking on | $0.0034 | 11s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-21 | |
| AClaude Sonnet 5.5default · thinking on | $0.04 | 9s | 0 | Fully autonomous | pass | Trust 80% | 2026-10-21 |
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