Logit model and logistics regress, what the diffrence?

In economics and business school, we talks about logit models a lot. In general, we mean the conditional logit model (McFadden (1973). But in statistics, they use logistics regression. I have been puzzling for years and could not find the answers.

For logistics regression, you model log(odds) as dependent variable and logit models, you model "utility function". Both models are about binary variable, at least for their simplest forms.
Except that the name and operation a bit different. What is the difference between them?

When you interpret the results, for logistic regression, the interpretation varies with "prob., odds, and log odds" and I guess, logit model, results mainly deals with the utility function. I am just very confused with the terminology. In some papers, I suspect they just run logistic regression but refer them as logit model, which makes me even more confusing.