What is a Token?
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- Basic Definition: A token can be thought of as roughly equivalent to a word, but it’s a bit more complex than that. In natural language processing (NLP), a token is the smallest unit that a model processes.
- Characteristics:
- Not Always a Word: A token can be a word, but it can also be a part of a word or even punctuation. For example, the word “don’t” might be split into two tokens: “don” and “‘t”.
- Variable Length: Tokens do not have a fixed length in terms of characters. A token could be a single character like “a” or a longer word like “wonderful”.
How Tokens are Counted
- Processing Text: When OpenAI’s AI processes text, it first breaks down the input into these tokens.
- Charging Model: The pricing is based on the number of tokens processed. This includes both the tokens that make up the input you provide and the tokens in the output generated by the AI.
Example
- If you ask a question that is 50 tokens long and receive an answer that is 150 tokens long, that would total 200 tokens.
Pricing Tiers
- OpenAI has different pricing for different versions of GPT-4. For instance currently, “gpt-4-1106-preview” might be priced at $0.01 per 1,000 tokens for certain requests and $0.03 per 1,000 tokens for others.
Practical Implications
- Cost Management: Understanding tokens is crucial for managing costs, especially when developing applications or services that use OpenAI’s API extensively.
- Optimizing Usage: By understanding how tokens are counted, you can optimize the way you frame queries or instructions to the AI to use fewer tokens, thereby reducing costs.
In summary, tokens are the basic units of text that OpenAI’s models, like GPT-4, process, and they play a central role in how usage is calculated and charged. Understanding tokens helps in effectively managing and optimizing the use of OpenAI’s services.
Curious as to what counts as an “Input” token? Please click here to read this article: “In OpenAI’s ChatGPT AI Assistant, Does the Knowledge Base Count Toward the “Input” Cost?”