The maximum input length an LLM can handle depends on its architecture and implementation. Most transformer-based LLMs are constrained by a fixed token limit, typically ranging from a few hundred to several thousand tokens. For example, OpenAI's GPT-4 can handle up to 32,000 tokens in some configurations, while earlier models like GPT-3 are limited to 4,096 tokens.
This token limit includes both the input and the generated output, so longer prompts reduce the space available for responses. If the input exceeds the token limit, it must be truncated, which can result in a loss of context or incomplete processing of the text.
Developers can address this limitation by preprocessing the input to include only the most relevant information or by using specialized architectures, such as sparse attention, to extend the effective context length. For extremely long documents, chunking the input and processing it in smaller sections can also be an effective strategy.