Does the AI Know What Day It Is? Cutoff Dates, Web Search, and Why Models Differ

Large language models are very advanced at many things and very basic at others. Two of those basic gaps, the knowledge cutoff and not knowing the current date, trip up a lot of people who assume the model is always up to date.

The cutoff date

Each model has a cutoff date. It gets trained on the data available on the internet up to that point, for example up to January 2026. Everything in its training data is what existed until the cutoff, and anything newer, it does not know about.

You may have noticed this yourself. Ask a model to research startup grants you can still apply for, and it gives you results from 2022, because that is the data it was trained on.

Models do not know today's date

On top of that, the models do not actually know the current date. Newer models have implemented a way for the model to know which day it is today, but it is a workaround. Without it, the model only knows what it was trained on up to the cutoff.

The practical fix is to give very concrete instructions. If you want up-to-date information, tell it explicitly to search the web and not just rely on what it was trained on.

Why web search costs more

Giving the model web access is not free. When it searches, it goes to the website, scrapes all the content, and treats that as a message, as if you had copy-pasted the page in as input. Then it processes that information to answer your question. That is why web search is still quite expensive, with one model listed at ten dollars per thousand web searches.

The three things that make models different

Beyond the cutoff date, models differ along two more dimensions.

One is the training data. Of the hundreds of models available, some were trained more on images and are much better at identifying things in images. One model was trained on a lot more medical data and understands the medical domain much better. Claude Opus is very good at coding tasks, likely because it was trained on a lot more coding repositories, including proprietary code that is not publicly available.

The other is the actual algorithm behind the model, the machine learning implementation. The frontier models from Anthropic and OpenAI have also improved their thinking and reasoning capabilities at the algorithm level, not just the data, which is part of why you get different and sometimes better results. For open-source models you can look at that implementation; for Anthropic and OpenAI the algorithms are proprietary.

So when a model gives you a stale or wrong answer, it is worth remembering it is not a single system that knows everything. It is a model with a cutoff, a particular set of training data, and a particular algorithm, and you often have to tell it explicitly to go and check.