One of the biggest misconceptions in modern AI is the belief that larger models inherently produce better intelligence, higher reasoning ability, and lower hallucination rates.

Language models are categorized by their total parameter count. Parameter count measures a model’s raw storage capacity for defining the internal weights and connections that store knowledge and reasoning rules. In neural networks like Transformer models (e.g., Mistral), parameters ($ heta$) are the trainable floating-point numbers (weights and biases) adjusted during training. There are three different categories of language model, like

Small Language Model with 1 Billion to 15 Billion parameters. Mid-Tier Models with 15 Billion to 70 Billion parameters. Large Language Model with 100 Billion to 1+ Trillion parameters. Architecturally, the parameter count is defined by the width, depth, and structural configuration of the network layers. The following diagram shows how most of the models focused on number of parameters and how it grown over the years.