How Claude "Thinks": A Simple Breakdown of Its Reasoning Style

Modern large language models are often described as "next-token predictors," but that description is increasingly incomplete. Systems like Claude, developed by Anthropic, have evolved beyond naive generation into compute-aware reasoning systems that dynamically trade off latency for accuracy. To understand how Claude "thinks," we need to move past metaphors and look at the underlying mechanics: token-level inference, latent reasoning traces, and adaptive compute allocation. Transformer Foundations and Latent Computation At its core, Claude is still a Transformer-based autoregressive model. Like models derived from the Transformer architecture introduced in Attention Is All You Need, it operates by predicting the probability distribution of the next token given a sequence. However, what differentiates modern reasoning-oriented models is not the architecture itself, but how inference is used. Instead of a single forward pass producing a direct answer, Claude leverages latent multi-step c