I worked with catboost, lightgbm, and xgboost when doing the zillow kaggle competition a long time ago, 2016 I think. I used what is called blending, where you give a weight to the models such as:

.5 * xgboost + .25 * catboost + .25 * lightgbm

Interesting - did this work better than a single model? In general, do meta-ensembles work better? My sense was that just xgboost was the main winner of kaggle competitions.

Meta-ensembles are the new trend for tabular data on Kaggle

XGBoost, LightGBM, and Catboost are all used quite frequently in competitions. LightGBM is actually marginally more popular than the other two now, but it's pretty close. In the M5 forecasting competition a few years back, many of the top solutions used primarily LightGBM.

If you're clever about how you blend models you can pretty much ensure that the performance of the (weighted) averaged model is strictly better than the (weighted) average performance of the models.

And increasing the space of possible models pretty much guarantees the performance will improve, provided you can still find a good enough optimum.