If we have a set of things (e.g. language models) and some measures we care about (e.g. cost, speed, whether weights are open, scores for a few benchmarks, etc.), then some of those things will be "pareto optimal" (see below) and some won't. The "pareto front" is the subset that is pareto optimal.
Some thing is "pareto optimal" when there isn't another thing that's AT LEAST AS GOOD in ALL measures, and BETTER in at least one way. For example, if we say there are no ties (for simplicity), then the cheapest language model is pareto optimal; the fastest model is pareto optimal; those which score highest on each benchmark are pareto optimal; and so on.
Tradeoffs can also be pareto optimal: for example, if the cheapest model is also slow, then there will be more pareto optimal models which are "cheapest for their speed"; and so on for other tradeoffs (e.g. fastest that achieves a certain benchmark score; cheapest model with open weights; etc.).
If you're making a decision about which thing to choose, you only need to care about those in the pareto front (since, by definition, anything that's not pareto optimal is objectively worse on at least one measure).
Pareto optimality does not compare one measure against another: something that's 10000x slower can still be pareto optimal, if it's 1% cheaper than the alternatives. To pick a "best" thing, you could give a weight/importance to each measure, and combine them into an overall score: but that's subjective, and might vary between people and tasks. In contrast, focusing on the pareto front is a way to ignore those things that will never be the best, regardless of weighting.