After years of fixating on raw model prowess, the AI industry is shifting to a more pragmatic metric: how much reliable work a model delivers per dollar. Companies are increasingly routing tasks to “good enough” systems and reserving premium models for only the most demanding jobs, as seen in Amazon’s internal push to steer Alexa queries away from costlier third-party options. Executives say the new battleground is efficiency—evaluated by real task completion, input/output pricing, the ability to reuse prior computation, and the number of steps required to finish a job. While rankings remain fluid, practitioners argue that headlines may favor the smartest models, but budgets will favor the ones that convert spend into dependable output.
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