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LLM token counts vary by tokenizer, not word count

By PulseAugur Editorial · [1 sources] ·

Token counts for large language models (LLMs) do not directly correlate with word counts, as the process is dependent on the specific tokenizer used by the model. Factors such as text formatting, punctuation, and even language can influence how text is segmented into tokens. For accurate token counting, it is essential to use the tokenizer associated with the target LLM, with tools like OpenAI's tiktoken or provider-specific APIs being recommended. AI

IMPACT Understanding tokenization is crucial for optimizing LLM prompt costs and performance.

RANK_REASON The item explains a technical concept related to LLM infrastructure rather than announcing a new release or significant event.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM token counts vary by tokenizer, not word count

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12 / 100
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Commentary
The item explains a technical concept related to LLM infrastructure rather than announcing a new release or significant event.
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High
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Muhammad Azlaan Zubair ·

    Why Token Counts Differ from Word Counts

    <p>A word count cannot tell you exactly how many tokens an LLM will process. Token counts depend on the tokenizer or model family, the spelling and formatting of the text, and sometimes the structure of the API request around it. Rules such as “one token equals three-quarters of …

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