The maximum input length a large language model (LLM) can handle depends on its architecture and configuration. Most models have a fixed “context window,” which defines the number of tokens (words or subwords) they can process in a single request. For example, OpenAI’s GPT-3.5-turbo supports up to 16,384 tokens, while GPT-4 offers variants with 8,192 or 32,768 tokens. Other models, like Anthropic’s Claude 2, extend this to 100,000 tokens. These limits include both input and output, so longer prompts reduce the space available for responses. Developers must check documentation for specific models, as exceeding the token limit typically results in truncation or errors.
Input length constraints directly impact how developers design applications. For instance, summarizing a lengthy document requires splitting it into chunks that fit within the model’s context window. APIs often provide tools to count tokens before sending requests. For example, OpenAI’s tiktoken library helps estimate token usage. If a model’s limit is 4,096 tokens, a 5,000-token query would need trimming—either by removing sections, shortening sentences, or prioritizing key content. Some models allow streaming or iterative processing, where outputs from one request feed into the next, but this adds complexity and latency.
Handling long inputs often involves trade-offs. While some models support fine-tuning to extend their effective context, this requires significant computational resources. Techniques like “sliding window” processing (re-analyzing overlapping text segments) or hierarchical summarization can mitigate limits but may lose coherence. For example, a developer building a chatbot for legal documents might split contracts into sections, summarize each, then combine results. Always test edge cases: a 32k-token model might still struggle with highly technical or dense text. Understanding these limitations ensures realistic system design and avoids unexpected failures in production.