How to price tokens for LLMs — a useful framework

Ben Evans lays out a way to think about token pricing that actually tracks what the business does. The key move: separate the cost of running tokens from the value they create, then price the gap. Most models charge by the token, but the token is just a unit of measure — the real question is whether the model earns back its compute cost plus a margin on the work it does.

He breaks it into three buckets. First, the raw compute cost per token, which drops as models get better. Second, the value bucket — how much the token is worth when it solves a real problem for a customer, which is where the margin lives. Third, the pricing strategy on top, which depends on whether you're selling to enterprises who need reliability or to developers who want cheap throughput. The framework is useful because it stops you from pricing by what the competition charges and starts pricing by what the model actually does.

Why this matters for us: la gente that builds small tools for the community — the side hustles, the apps for tías and primos — needs to understand how to price their AI work so it covers the compute and still leaves room to grow, instead of burning through cash on tokens that don't earn back their cost.