Your modeled scenario
What this could mean
The takeaway
Where it goes
Modeled monthly spend allocation
Total annualized spend
Useful spend
Wasted spend
Assumptions (adjustable)
The varied 26% starting mix is illustrative, not a typical-waste benchmark. Categories are modeled as separate slices so this scenario does not double-count spend.
View all modeled values
| Allocation | Share of current spend | Monthly amount | Annual amount |
|---|
What you won’t see in the bill
The model call is the visible line item. Cost per completed task is shaped by everything around it.
Retries, repeated calls, growing context, cache behavior, model choice, idle cadence, and failed outcomes can multiply the runtime spend required to produce useful work.
- Plan
- Tool
- Finish
- Plan
- Tool
- Retry
- Repeated tool
- Growing context
- Finish / fail
Every extra model step is still metered, whether or not it advances the outcome.
A 2026 study found identical-task coding-agent runs varied by up to 30× in total tokens, while “higher token usage does not translate into higher accuracy.” Read the study →
Xerg replaces this model with measured cost impact, confidence, detector coverage, and prioritized fixes from your runtime evidence.
Replace the scenario with evidence
Find the recoverable spend.
Xerg audits the runtime data you already have, attributes the costly patterns, and shows what to fix first.