Approximately how many tokens would an LLM take to process 6,000 input words?

To determine the approximate number of tokens an LLM (large language model) would process for 6,000 input words, it's essential to understand the relationship between words and tokens. Typically, a token can represent various types of linguistic units, including individual characters, words, or subwords.

The general rule of thumb is that, on average, a single word is roughly equivalent to about 1.33 tokens, when considering language such as English, which can have many shorter words that combine into longer or more complex tokens. This means that for 6,000 words, one could estimate the total token count by multiplying the number of words by the average conversion rate.

Calculating this gives:

6,000 words x 1.33 tokens/word ≈ 8,000 tokens.

This approximation aligns with the concept that models often tokenize input in a way that includes both full words and subwords, leading to a higher token count compared to the word count alone. Thus, the correct answer reflects the estimated token usage based on typical tokenization practices seen in LLMs.