What Are Tokens
In the context of artificial intelligence (AI), a token is a fundamental unit of data that algorithms process, particularly in natural language processing (NLP) and machine learning services. These AI tokens play a pivotal role in facilitating transactions and serving as the medium of exchange within AI-powered platforms.
In the context of AI, a token represents a distinct information unit. It can be:
- A word in a text document.
- A character in a string.
- A pixel in an image.
- Any other fundamental unit of data processed by the AI model.
We can thought token as a pieces of words. Before the any AI model/API processes the request, the input is broken down into tokens. These tokens are not cut up exactly where the words start or end, tokens can include trailing spaces and even sub-words.
Almost all Language models read and write text in chunks of characters called tokens. model processes input data in the form of tokens. In English, a token can be as short as one character or as long as one word (e.g., b or boy), and in some languages tokens can be even shorter than one character or even longer than one word.
The process of converting Text to token is called tokenization
Let's us explore tokenization, using a Open AI interactive Tokenizer tool, which allows you to calculate the number of tokens and see how text is broken into tokens. remember that the exact tokenization process varies between models. and may produce different tokens for the same input text. Let us discus here only using GPT. OpenAI's large language models process text using tokens, which are common sequences of characters found in a set of text. The models learn to understand the statistical relationships between these tokens.
Token Characteristics
The length of tokens varies based on the language and context. A helpful rule of thumb is that one token generally corresponds to 4 characters of text for common English text. This translates to roughly 3/4 of a word so 100 tokens is equal to 75 words.
- Approximately 1 token ≈ 4 characters in English.
- Approximately 1 token ≈ 3/4 words.
- 100 tokens ≈ 75 words or 1-2 sentences.
- 1 paragraph ≈ 100 tokens.
- 1,500 words ≈ 2048 tokens.
Contextual Considerations while generating token
The AI API treats words according to their context in the data. Identical words may be generated into different tokens based on their structure within the text. Remember that the exact tokenization process varies between models. Newer models like GPT-3.5 and GPT-4 use different tokenizers than previous models, resulting in different tokens for the same input text.
AI Models take the prompt, convert the input into a list of tokens, processes the prompt, and convert the predicted tokens back to the words we see in the response.
for understanding You can use the tool openAI's tokenizer as below.
Two identical words may be generated into different tokens depending on how they are structured within the text. Consider how the API generates token values for the word 'you' based on its context within the text.
In the example above the token '499' for 'you' (with a leading space and starting with a small letter)
in above example, The token '9514' generated for 'you' (don't have leading space) and is different from the token '499' for ‘ you’ (with a leading space and starting with a small letter).
Observations
The token generated for ‘you’ varies depending on its placement within the sentence.
- Lowercase in the middle of a sentence
- Uppercase in the middle of a sentence
- Uppercase at the beginning of a sentence
You can use the tool tokenizer to understand how a piece of text might be tokenized by a language model.
Higher token-to-char ratio effect
How words are split into tokens is also language-dependent. In some language we can write same sentence with less characters while in some language we have to write more characters for same sentence, and token are created using sequence of characters. ('ਮੈਂ ਠੀਕ ਹਾਂ' in Panjabi) contains 18 tokens (for 11 chars), while in English it contain only 3 token for 9 characters. higher token-to-char ratio can make it more expensive to implement the API for languages other than English. as mostly AI API are billed on basis of token input/output count with API.
18 token generated for 'I am fine' in Panjabi language
3 token generated for 'I am fine' in English language
Token Limits
Language models also have restriction for input and output tokens. Depending on the model used, requests can use up to 128,000 tokens shared between prompt and completion. Some models, like GPT-4 Turbo, have different limits on input and output tokens. But There are some ways to solve problems create query within the limit, for example breaking the text into smaller pieces, condensing your prompt, also prompt engineering can be beneficial here for get maximum from API. you can write same thing with concise text and get minimize you token count. If a conversation has too many tokens to fit within a model's maximum limit (e.g., more than 4097 tokens for gpt-3.5-turbo), you will have to truncate, or otherwise shrink your text until it fits. To see how many tokens are used by an API call, check the usage field in the API response (e.g., response['usage']['total_tokens']). But in Chat models because of their message-based formatting, it's more difficult to count how many tokens will be used by a conversation.
Token Pricing
Cost of AI is calculated based on the number of tokens processed during inference. Each token contributes to your bill, and the total cost depends on the complexity and length of your input. Let's say you provide a text prompt with 20 tokens, and the AI model generates a response using 30 tokens. Your total cost would be based on 50 tokens (20 input tokens + 30 output tokens). Note however that for some models the price per token is different for tokens in the input vs. the output. Prices can be viewed in units of either per 1M or 1K tokens. roughly You can think of tokens as pieces of words, where 1,000 tokens is about 750 words. for example GPT-4 Turbo is offered at 128K context with an April 2023 knowledge cutoff and basic support for vision.
(128k context window, which is equivalent to approximately 300 pages of text in a single prompt. This extended context allows for more coherent and contextually relevant responses)
Pricing as par Model and Token Limit
For more detail regarding pricing click here
Conclusion
- How much your API call costs, as you pay per token.
- How long your API call takes, as writing more tokens takes more time.
- Whether your API call works or not, as total tokens must be below the model's maximum limit.
- Both input and output tokens will be counted. if your API call used 5 tokens in the message input and you received 10 tokens in the message output, you would be billed for 15 tokens.
We can thought token as a pieces of words. AI model processes input data in the form of tokens. cost of AI is calculated based on the number of tokens processed during inference, Keep in mind that different AI services or platforms may have variations in their token-based pricing models. Always check the specific pricing details provided by the service you're using, To keep things simple and flexible, pay only for the resources you use.
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