No LLM is real time, and in fact, even a 2025 cut off isn't entirely realistic. Without guidance to say, a new version of a framework it will frequently "reference" documentation from old versions and use that.

It's somewhat real time when it searches the web, of course that data is getting populated into context rather than in training.

You can query Grok on recent news and it will deliver the very recent updates (1 day old or probably even less)

That's the web version (which has tools like search plugged in), other models in their official frontends (Gemini on gemini.google.com, GPT/o models on chatgpt.com) are also "real time". But when served over API, most of those models are just static.

Not at all. The model weights and training data remain the same, it's just RAG'ing real-time twitter data into its context window when returning results. It's like a worse version of Perplexity.

Why worse? Doesn't Grok also search the web along with Twitter?