Input Tokents

Input Tokens are discrete units of text, code, or data that represent the fundamental processing elements fed into large language models and AI systems for analysis, understanding, and response generation. These tokens serve as the standardized format through which AI models receive and interpret human language, converting raw text into numerical representations that neural networks can process effectively. Input tokenization involves breaking down text into smaller components such as words, subwords, characters, or byte-pair encodings using algorithms like SentencePiece, WordPiece, or Byte-Pair Encoding (BPE) that optimize vocabulary size and semantic representation. Token limits define the maximum amount of input context that models can process simultaneously, with different models supporting varying context windows ranging from thousands to millions of tokens. Input token counting directly impacts API costs, processing time, and model performance, making efficient tokenization strategies essential for production AI applications. Advanced tokenization techniques include multi-modal tokens that represent images, audio, or structured data alongside text, enabling models to process diverse input types within unified frameworks. Understanding input tokens is crucial for prompt engineering, cost optimization, and designing AI applications that effectively utilize model capabilities while respecting computational constraints and processing limitations inherent in current language model architectures.