What is a token in AI?

Tokens have become a crucial aspect of AI models, serving as the primary unit for usage limits and billing, particularly in large language models (LLMs) like ChatGPT and Claude Code. These models interpret inputs as strings of tokens—units that can range from single characters to whole words—and generate outputs by predicting the next token in a sequence. The tokenization process, influenced by a model's training data, varies across languages and media, with more common patterns typically represented by fewer tokens. The context window, which limits the number of tokens processed at one time, is critical for tasks such as coding and reasoning, where complex thought processes consume more tokens. Recent shifts in AI economics have moved from fixed pricing to per-token billing, significantly impacting costs, especially for sophisticated reasoning models used in coding tasks. To manage these costs, strategies like combining AI with deterministic automation, such as Zapier, can optimize token usage by matching tasks to appropriate models, thus ensuring efficiency without excessive token consumption.

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