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Buyers should expect lower operational costs and big savings at scale from a more token-efficient model, but those savings may come with reduced raw performance or capabilities compared with a higher-power model like Anthropic’s Fable 5. The article says enterprises are increasingly trading some model power for token efficiency to cut AI spending. Answered

They measure token usage by tracking the number of tokens processed and billed for a given task, since token efficiency is defined as the amount of data processed and billed. To compare providers in practice, run the same tasks on OpenAI, Meta, SpaceXAI, and Anthropic and compare the tokens consumed per task and resulting cost, with fewer tokens for the same output indicating better token efficiency. Answered

Hardware or on-prem hardware options can substantially lower total cost of ownership by cutting the raw running costs that token-efficient models still incur at scale. For example, a related article titled "Phison unveils solution to reduce $3m cost of upcoming 1T parameter AI model to $100K" shows hardware improvements can reduce operational costs dramatically, meaning token efficiency plus better hardware or on-prem deployments can together shrink TCO significantly. Answered