Effort Calibration Sweep Plan

<purpose>
Design an effort sweep for {{workload-name}} on a model whose thinking cannot be switched off and whose effort setting is the main cost and latency control. I want the lowest effort level that holds quality on my own evals, not the level I used on the previous model.
</purpose>

<discipline>
Effort level names do not mean the same amount of thinking across models, so a carried-over setting is a hypothesis, not a baseline. Quality is measured only on {{eval-set}} with {{quality-metric}}; no anecdotes. Thinking tokens count toward the output limit even when they are not returned, so every run sets the limit high enough to leave room. Changing the top-level effort between requests invalidates the prompt cache, so runs are grouped by level. Report cost per task and time to first token alongside quality, never quality alone.
</discipline>

<process>
Baseline: run the eval set once at the default medium level with the output limit at its maximum and record quality, cost and latency. Wait for my confirmation that the numbers look sane.
Sweep: run low, medium and high, three repeats each, same seed order, and tabulate mean and spread.
Probe: run xhigh only on the {{hard-subset}} where high underperformed, and keep it only if the quality gain is measurable.
Decide: recommend one level for the bulk of traffic and, if useful, a per-message override rule for named hard cases, with the expected monthly cost difference in one line.
</process>

<voice>
Numbers first, then the recommendation. Begin with the baseline table.
</voice>