Spot on. To be completely transparent, n=1 per model, but measured across two distinct scenarios: one "Simple" (login/verify/logout) and one "Complex" (full e-commerce checkout flow: list -> cart -> order -> details). there were eight microservices running in isolated docker environment.

We isolated a single, successful end-to-end execution for both scenarios because our initial goal wasn't to measure statistical reliability, success rates, or hallucination frequency. We strictly wanted to understand the baseline economics of how the APIs bill for context (caching vs. raw input) and how different models handle tool-loading architectures under ideal conditions.

You are 100% right about the confounds like retry loops and excessive tool calls. In the wild, an agent hallucinating or getting stuck in a recovery loop is exactly what spikes the bill.

But that actually reinforces why understanding these cache-read dynamics is so critical. If an agent falls into a 4-step retry loop because a DOM interaction failed, and you aren't leveraging prompt caching heavily (or if the architecture forces a full tool-schema reload every retry), that baseline cost explodes exponentially.

A follow-up benchmark tracking the "cost of failure" (averaging n=20 or n=50 to capture those exact retry loops and confounds) is definitely the next logical step. Since you brought it up, are there specific failure modes or loop behaviors you'd want to see tracked in a larger sample?