Token Limit
Maximum number of tokens (text units) an AI model can process in a single request.
Definition
Token limit is the maximum amount of text an AI model can process in one interaction, measured in tokens (roughly 4 characters or 0.75 words). Models like GPT-4 have token limits ranging from 8,000 to 128,000 tokens, which determines how much context they can consider when generating responses.
Token limits include both the input prompt and the generated output. A model with an 8,000 token limit might use 6,000 tokens for your prompt, leaving only 2,000 tokens for the response. Understanding token limits is crucial for applications that need to process long documents, maintain conversation history, or work with extensive context.
Why It Matters
Token limits determine what's possible with AI applications. A customer service chatbot needs to include conversation history, knowledge base articles, and system instructions in every request—quickly consuming thousands of tokens. Hitting the token limit means losing context or truncating important information.
For businesses building AI features, token limits directly impact cost and capability. Models with larger context windows cost more per API call but enable richer applications. A 128K token model can process an entire book as context, while an 8K model requires chunking and sophisticated retrieval strategies.
Token management strategies like semantic search, compression, and summarization help work within limits. Instead of sending an entire knowledge base to the model, RAG (Retrieval Augmented Generation) systems retrieve only relevant sections—staying within token limits while maintaining access to vast information.
Examples in Practice
A legal tech company builds an AI contract analyzer that needs to process 50-page agreements. With an 8K token limit, the entire contract exceeds the limit. They implement chunking—analyzing the contract in sections and combining results—enabling comprehensive analysis within token constraints.
A customer support chatbot maintains conversation context by including the last 10 messages in each API call. As conversations grow longer, they approach the token limit. The system implements smart truncation, keeping recent messages and system instructions while summarizing older context, maintaining coherent conversations without hitting limits.
A content marketing team wants AI to analyze competitor blog posts and suggest topics. Individual posts fit within token limits, but analyzing 50 competitors at once exceeds them. They batch process 5 competitors per request, then have the model synthesize findings—working within limits to handle large-scale analysis.