The AI race is shifting from bigger models to cheaper, smarter systems

Companies are increasingly selecting AI models based on specific task requirements, cost efficiency, and data control rather than relying solely on benchmark rankings. Major technology firms have begun deploying smaller, specialized models for particular applications alongside their larger systems. This shift reflects trade-offs between model size, computational expense, and performance gains for individual use cases.

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