Large language models (LLMs) can solve many mathematical, logical, and physical problems with striking competence. This essay argues that LLMs are neither mere statistical pattern matchers nor fully human-like minds. They internalize statistically learned procedures for symbolic processing through training on vast datasets. They can emulate algorithmic behavior through learned inference-like computations and chain-of-thought generation, yet they do not obviously possess stable semantics, an explicitly inspectable world model, or intrinsic understanding. Drawing on computational theory, probabilistic perspectives, and recent empirical evidence-including chain-of-thought prompting, the GSM-Symbolic benchmark, and test-time compute reasoning models-the essay suggests that LLMs can produce behavior functionally equivalent to reasoning without necessarily sharing its classical foundations. The conclusion is not that the question is settled, but that LLMs deserve a distinct category in how we think about intelligence, explanation, and design.