Token

A token is the basic unit of input and output for a language model — typically a subword fragment, occasionally a whole word, sometimes a single character, depending on the tokenizer. In modern LLMs like GPT-4, Claude, Gemini, and Llama, common English words become single tokens while rare words split into multiple subword pieces; for example, "unbelievable" might tokenize as ["un", "believ", "able"] or similar. Tokens are the unit in which LLM APIs measure context window size, charge billing, and report rate limits, making accurate token counting essential for cost forecasting and capacity planning. A useful rule of thumb is that English text averages about 0.75 tokens per word or 4 characters per token in the most common tokenizers, but actual rates vary by content type — code, JSON, and non-English text are typically denser. Each model family uses a different tokenizer producing different token counts for identical text, so token-based comparisons across providers require careful normalization. AI governance teams use token counting as a primary cost-control lever and AI compliance reporting axis.

Token metering in Centralpoint: Centralpoint meters every token across every model in its stack — OpenAI, Claude, Gemini, Llama, embedded models — producing accurate per-skill, per-audience, and per-tenant cost reports. The model-agnostic platform keeps prompts on-premise, supports both generative and embedded models, and embeds chatbots through one line of JavaScript.


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