“Reasoning” models — and their offshoots, such as “deep research” — have emerged as a major trend in generative AI, helping improve the accuracy and reliability of responses to complex problems.
However, this approach significantly increases the computational costs of generating responses. The imitation of reasoning means a higher consumption of server capacity, requiring additional data center space, power and cooling — ultimately driving up operating costs for the model owners.
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