Most AI cost tools only route down to cheaper models. Why routing up, staying and stopping matter just as much for the lowest cost to finish.
Before any AI request is sent, there is a decision to make about which model should handle it. Most tools treat that decision as a search for the cheapest acceptable model. That covers one of four possible moves, and often not the most valuable one.
Routing up feels wrong to anyone watching the invoice, because it spends more on tokens. But one good answer is cheaper than four bad ones and an hour of someone fixing the result. A system that can only route down cannot capture that saving, and on hard work it makes costs worse.
Retry loops are one of the quietest sources of AI waste, especially in agents. A job that has failed several times on the same problem rarely succeeds on the next attempt. Knowing when to stop saves both the tokens and the time someone spends reviewing yet another failed result.
CompletionPrism™ makes one of these four moves before every step is sent, and the receipt says which one it made. When it routes up, the extra token spend appears as a negative line next to the rework it avoided, so the trade is visible. The four moves are patent pending.
Inside SaaS products, the same decision runs as PrismRoute in the Embedded License, before each step of every request a vendor's AI features make.