diff --git a/botorch/models/model.py b/botorch/models/model.py
index 67f10fc9dc..8b20ff0edd 100644
--- a/botorch/models/model.py
+++ b/botorch/models/model.py
@@ -576,14 +576,13 @@ def fantasize(
             The constructed fantasy model.
         """
         if evaluation_mask is not None:
-            if (
-                evaluation_mask.ndim != 2
-                and evaluation_mask.shape[0] != X.shape[-2]
-                and evaluation_mask.shape[1] != self.num_outputs
+            if evaluation_mask.ndim != 2 or evaluation_mask.shape != torch.Size(
+                [X.shape[-2], self.num_outputs]
             ):
                 raise BotorchTensorDimensionError(
-                    f"Expected evaluation_mask of shape {X.shape[0]} "
-                    f"x {self.num_outputs}, but got {evaluation_mask.shape}."
+                    f"Expected evaluation_mask of shape `{X.shape[0]} "
+                    f"x {self.num_outputs}`, but got `"
+                    f"{' x '.join(str(i) for i in evaluation_mask.shape)}`."
                 )
             if not isinstance(sampler, ListSampler):
                 raise ValueError("Decoupled fantasization requires a list of samplers.")
diff --git a/test/models/test_model_list_gp_regression.py b/test/models/test_model_list_gp_regression.py
index 1547f2a212..09b058a521 100644
--- a/test/models/test_model_list_gp_regression.py
+++ b/test/models/test_model_list_gp_regression.py
@@ -555,7 +555,32 @@ def test_fantasize_with_outcome_transform_fixed_noise(self) -> None:
                     FixedNoiseGP(X, Y, yvar, outcome_transform=Standardize(m=1)),
                     FixedNoiseGP(X, Y2, yvar2, outcome_transform=Standardize(m=1)),
                 )
+                # test exceptions
+                eval_mask = torch.zeros(
+                    3, 2, 2, dtype=torch.bool, device=tkwargs["device"]
+                )
+                msg = (
+                    f"Expected evaluation_mask of shape `{X.shape[0]} x "
+                    f"{model.num_outputs}`, but got `"
+                    f"{' x '.join(str(i) for i in eval_mask.shape)}`."
+                )
+                with self.assertRaisesRegex(BotorchTensorDimensionError, msg):
 
+                    model.fantasize(
+                        X,
+                        evaluation_mask=eval_mask,
+                        sampler=ListSampler(
+                            IIDNormalSampler(n_fants, seed=0),
+                            IIDNormalSampler(n_fants, seed=0),
+                        ),
+                    )
+                msg = "Decoupled fantasization requires a list of samplers."
+                with self.assertRaisesRegex(ValueError, msg):
+                    model.fantasize(
+                        X,
+                        evaluation_mask=eval_mask[0],
+                        sampler=IIDNormalSampler(n_fants, seed=0),
+                    )
                 model.posterior(torch.zeros((1, 1), **tkwargs))
                 for decoupled in (False, True):
                     if decoupled: