The maximum number of tokens a model can read at once — the total working memory available for a prompt, grounding documents, conversation history, and the generated response combined.
The context window is the maximum number of tokens a language model can attend to in a single forward pass. Everything the model "knows" about a conversation — the system prompt, previous messages, any documents provided for grounding, and the response it is generating — must fit within this limit. Tokens outside the window are simply not visible to the model; they might as well not exist.
A larger context window means more information can be fed to the model at once. This directly affects grounding: the more pages a model can read in a single request, the richer its answer can be. Gemini 1.5 Pro has a 1 million token context window, which can hold approximately 750,000 words — roughly 700 average web pages. GPT-4o supports 128,000 tokens. Earlier models were limited to 4,096 tokens, making deep document grounding impossible.
When AI search systems ground a query in web results, the retrieved grounding snippets are placed into the model's context window alongside the user's question. The model only sees what fits. DEJAN research has established that Google's AI Mode operates with a fixed per-query word budget for grounding content — a practical ceiling imposed by context window economics and latency constraints, not by the theoretical window size of the underlying model.
Having a large context window doesn't mean the model uses all of it equally well. Research has shown that LLMs tend to attend more strongly to the beginning and end of long contexts, with information in the middle receiving less weight. For AI SEO, this means position within a grounding document — not just presence — may affect how reliably a model incorporates a brand mention.
