Estimate your token consumption

Advanced language models process text using tokens, which are common sequences of characters in text. These models learn the statistical relationships between tokens to predict the next one in a sequence.

Tokenization is crucial for how these models interpret and generate text. It breaks down input text into smaller units (tokens) that the model can process.

The tokenization process can vary between different models. Newer models may use different tokenizers than older ones, potentially producing different tokens for the same input text. This can affect how the model processes text and impact token counts.

Understanding tokenization is helpful when working with these models, especially when considering input length limitations or optimizing text processing efficiency.