Five practical ways to cut what you spend on OpenAI, Anthropic, Google and xAI models, starting with measuring cost per finished task instead of cost per token.
Most advice on reducing LLM costs starts with the price list: move to a cheaper model, shorten prompts, cache what you can. Those steps help at the margin. The bigger saving comes from changing what you measure. Price per token tells you what a request cost. It does not tell you what the work cost, and the work is what you pay for.
Measure cost per finished task: every attempt the task took, plus the time a person spent catching and fixing a wrong answer. Once that number exists, the right moves become obvious.
Each of those rules is simple on its own. Applying all five on every step of every request, across every model a company has approved, with new models shipping every week, is not something people can do by hand. That is why most companies settle for one blunt policy, usually "use the cheap model", and lose the savings the policy was meant to produce.
CompletionPrism™ makes this decision before every request is sent. It prices the cost to finish each step on every approved model across Anthropic, OpenAI, Google and xAI, counting tokens across every attempt, rework, switching, hidden thinking and context re-reading. It then routes down, routes up, stays or stops, whichever has the lowest total cost that still holds quality.
Every call is billed to your own provider account, so the saving shows up on an invoice you can check. Every answer arrives with a receipt showing which model ran and what it saved.
Start by measuring. A two-week baseline on one team's real traffic, with nothing rerouted, tells you your actual cost per finished task and how much of it is rework. Most teams find the rework is larger than the invoice.