Compare scores of models
I'm going to assume your using python and scikit-learn mostly because it has a method for providing model metrics.

from sklearn.metrics import classification_report

# I presume that you've already trained a model and it's saved as xgb
# X_test is your testing X data (NOT THE DATA YOU TRAINED ON!!!!)
# Y_test is the corresponding correct values
print('Accuracy score is: ',accuracy_score(Y_test, xgb.predict(X_test)) * 100)
print(classification_report(Y_test, xgb.predict(X_test)))
&gt;&gt;&gt; Accuracy score is:  61.13989637305699
&gt;&gt;&gt;           precision    recall  f1-score   support

           0       0.38      0.98      0.55        47
           1       0.99      0.49      0.66       146

   micro avg       0.61      0.61      0.61       193
   macro avg       0.68      0.74      0.60       193
weighted avg       0.84      0.61      0.63       193


As you can see there is lots of info available.
The accuracy score is the % of correct predictions overall.
And the classification report goes into more depth about how good the model is predicting each class more info here crossvalidated SE explanation.