What is the token limit in OpenAI models?

OpenAI models process text in chunks called tokens, which represent units of text (like words or word parts). The token limit varies by model and determines how much text the AI can handle in a single request. For example, GPT-3.5 Turbo has a default limit of 4,096 tokens, while GPT-4 supports up to 8,192 tokens in its standard version, with extended options reaching 32,768 tokens. These limits apply to the combined input and output—if your input uses 3,000 tokens, the model can generate up to 1,096 tokens in response when using GPT-3.5 Turbo. Exceeding the limit causes the request to fail or truncate the text, so developers must track token counts carefully.

Token limits directly affect how developers design applications. For instance, summarizing a 10-page document (roughly 5,000 tokens) with GPT-3.5 Turbo would require splitting the text into smaller chunks or using a model with a higher limit, like GPT-4. Similarly, building a chatbot requires keeping conversation history within the token window. Developers often use strategies like truncation, omitting older messages, or summarizing past interactions to stay under the limit. Tools like OpenAI’s tiktoken library help count tokens programmatically, ensuring inputs fit the model’s constraints. For code-generation tasks, where long context is common, selecting a model with a higher token capacity becomes critical to avoid incomplete outputs.

Understanding token limits also involves trade-offs between cost, latency, and functionality. Models with higher limits, like GPT-4-32k, are more expensive and slower but enable complex tasks such as analyzing legal contracts or generating lengthy reports. Conversely, smaller limits force developers to optimize inputs but reduce costs. For example, a support ticket system using GPT-3.5 Turbo might need to preprocess user queries to remove irrelevant details before sending them to the API. Always check OpenAI’s documentation for the latest limits, as they vary by model version and can change over time. Balancing these factors is key to building efficient, scalable applications.