What generally distinguishes a newer generation of GPT models from an older one?

A new generation is a new base model rather than a new coat of paint, and the differences are printed on the model reference pages, where you can check them before committing to anything.

The first is how much the model can hold at once. GPT-4o shipped with a 128,000-token context window; the GPT-5.6 family lists 1,050,000. That is the gap between handing it a long file and handing it a whole project.

The second is reasoning. Current-generation models spend internal reasoning tokens before they answer, which OpenAI says helps the model "plan, use tools effectively, inspect alternatives, recover from ambiguity, and solve harder multi-step tasks". The GPT-4o page lists no such capability at all.

The third is simply the knowledge cutoff moving forward.

Every wrong answer here dies on the same reference pages: newer generations are not smaller and weaker, they still accept text, and the differences are specifications rather than claims. The durable lesson is not the numbers, which change every few months, but the habit of reading them off the documentation instead of guessing.