Context window size — how much text a model can consider at once — has grown dramatically, but larger isn’t automatically better; there are genuine, real cost and performance trade-offs worth understanding before defaulting to “use the biggest context available.”
The Genuine Cost Scaling Problem
Most transformer-based attention mechanisms scale computationally with the SQUARE of context length (though modern optimizations have meaningfully improved this in various ways) - meaning doubling context length can genuinely more than double the actual compute cost, not simply double it linearly. A request using 100,000 tokens of context costs GENUINELY, substantially more than a request using 5,000 tokens of context, even for otherwise functionally comparable underlying tasks.
The “Lost in the Middle” Problem
Research has genuinely, repeatedly shown that models often pay measurably less attention to information positioned in the genuine middle of a very long context, compared to information positioned at the clear beginning or end — meaning simply stuffing a huge amount of context into a prompt doesn’t automatically guarantee the model will actually, reliably use all of it equally well and equally reliably.
A More Deliberate Approach
Retrieval-Augmented Generation (RAG) — retrieving only the specific, genuinely most relevant chunks of a larger document collection rather than stuffing the model’s entire context with everything indiscriminately — frequently produces genuinely better results at meaningfully lower cost than simply maximizing context length by default. Large context windows are genuinely valuable for specific tasks (like analyzing one truly large, singular document as a coherent whole), but they’re not automatically a free, costless upgrade over more deliberate, targeted retrieval for every single use case.