Input vs Output vs Reasoning Tokens Cost - LLM Pricing Explained

If you have ever checked the pricing page for OpenAI, Anthropic, or Google and wondered why there are three different token prices listed, you are not alone. The distinction between input tokens, output tokens, and reasoning tokens is one of the most misunderstood aspects of LLM pricing - and getting it wrong can mean overspending by 5-10x on your AI workloads. This guide breaks down exactly what each token type is, why they cost different amounts, and how to optimize your spending - whether you are building an AI application, running code reviews, or just trying to understand your API bill. What Are Tokens in LLMs? Before diving into pricing, let's clarify what tokens actually are. A token is the fundamental unit of text that large language models process. It is not a word, not a character, but something in between. On average, one token equals roughly 4 characters or 0.75 words in English. The word "understanding" is two tokens. A line of Python code like def calculate_total(items):