AI Tokens: What They Are, How They're Counted, and Why It Costs You

AI token costs depend on provider-specific tokenizers rather than a universal measure of words or characters, so identical content can generate substantially different bills and context-window usage across OpenAI, Anthropic, and Google models. Benchmarks cited in the text show that differences widen for structured inputs such as JSON, YAML, and tool definitions, with Claude Opus using up to 2.65 times as many tokens as GPT-5.4 for tool schemas, making effective costs diverge far more than list prices suggest and causing the cheapest model to vary by workload. Input format has an even larger effect: a 15-page test found raw HTML consumed about 4.1 million GPT tokens versus about 191,000 for cleaned Markdown, a 21.5-fold reduction that can determine whether content fits within a model’s context window. Technical, structured, emoji-rich, numeric, and non-English content may tokenize inefficiently, with some languages reportedly costing more than twelve times as much as English for equivalent material. The recommended approach is to measure representative traffic with each provider’s official token-counting tools, compare effective rather than advertised prices, monitor production usage, and reduce unnecessary input by converting HTML to cleaned Markdown, retrieving excerpts instead of full pages, and avoiding the accumulation of irrelevant context.

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