Context Window
“Larger context basically means you can write a larger text prompt, and get a larger and more detailed response back. So you could for example copy the text from multiple pages from a book (up to 300 pages, if the claims from the announcement are accurate), and then ask it to summarize the content, analyze, identify key points or themes, etc.” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
“It also means that the AI will remember more of your long conversations. For example, let’s say you ask it to give you ideas for a story and it says, “This is a character named Paul whose brother is named Lenny.”” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
“Then you keep asking for more and more details about the story, and it comes up with a story about Paul traveling to France and doing all of these interesting things. If you chat long enough and then ask it for the name of Paul’s brother, that first message could land outside of the context window, which means the AI will forget the answer it previously gave you. It might reply that Paul’s brother is named Dave, or it might even say that Paul doesn’t have a brother.” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
“A longer context window allows you to have much longer conversations before it starts to “forget” things.” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
The largest models, such as Google's Gemini 1.5, presented in February 2024, can have a context window sized up to 1 million (context window of 10 million was also “successfully tested”).
- Snippet from Wikipedia: Context window
The context window is the maximum length of input a large language model (LLM) can consider at once. In the development and maturation of LLM technology expanding the context window has been a major goal. The length of a context window is measured in tokens. In 2025, the Gemini LLM had the largest context window with two million tokens.
In some models the context length is limited by the size of inputs during the training runs. However, attention mechanisms can be adopted to allow LLMs to interpret sequences that are much longer than those observed at training time.