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The per-token price of inference is what ultimately determines how broadly an AI model gets deployed.
Enterprise buyers and developers run cost models before committing to production use, and when a model's unit economics don't support a given application, that application doesn't get built.
Google is pressing on that constraint with an expansion of its Gemini lineup that adds cheaper, more efficient model variants, paired with a new cybersecurity offering the company is positioning as a Mythos rival.
Cost is the argument The move toward lower-cost Gemini models reflects a clear read of where buying decisions are currently being made. When per-query costs fall, the set of viable applications expands.
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