How much text an AI model can consider at one time, sometimes called its context, has grown substantially. In 2026 many systems can take in the equivalent of long reports, whole books or large collections of code in a single request, then answer questions that draw on all of it.
This reduces the need to chop material into pieces and stitch the answers together. The catch is that a model given a great deal of text can still overlook details buried in the middle, so results improve when the most relevant material is placed where the model attends most. Even so, the ability to reason over long documents opens up practical uses in research and analysis.