• A baseline is usually a lower complexity model. New research leads tend to compare against them to justify increased complexity and other requirements. Often, a random baseline is used, based on either random performance from resampling or a theoretical random performance. This is usually done in applications (case study?), not in development of new methods.

• A benchmark is a comparison of different competitors. In development, it's often not enough to beat the baseline, the model must be competitive facing other similarly complex models.

Ref: https://stats.stackexchange.com/questions/486134/what-is-the-difference-between-baseline-and-benchmark-in-terms-of-machine-learni