Preface:
In 2023-2024, we were accustomed to asking: "How many billions of parameters does this model have?"
By 2025, the question has become: "How many books can this model digest?" and "How many cents does it cost to infer 1 million tokens?"

This shift in questioning marks the transition of Large Language Models (LLMs) from the stage of "Brute Force Aesthetics" to "Precision Engineering". The marginal utility of parameter size is diminishing, while architectural efficiency, context length, and inference costs have become the new battlegrounds. This article deeply analyzes the three core trends of the AI model technology stack in 2025 from first principles.