diff --git a/src/terratorch/models/prithvi_model_factory.py b/src/terratorch/models/prithvi_model_factory.py
index ad424084..a84a35a8 100644
--- a/src/terratorch/models/prithvi_model_factory.py
+++ b/src/terratorch/models/prithvi_model_factory.py
@@ -32,7 +32,7 @@ def build_model(
         backbone: str | nn.Module,
         decoder: str | nn.Module,
         bands: list[HLSBands | int],
-        in_channels: int = int | None,  # this should be removed, can be derived from bands. But it is a breaking change
+        in_channels: int | None = None,  # this should be removed, can be derived from bands. But it is a breaking change
         num_classes: int | None = None,
         pretrained: bool = True,  # noqa: FBT001, FBT002
         num_frames: int = 1,
diff --git a/tests/test_prithvi_model_factory.py b/tests/test_prithvi_model_factory.py
index 6d76fa44..ad7f53a5 100644
--- a/tests/test_prithvi_model_factory.py
+++ b/tests/test_prithvi_model_factory.py
@@ -13,6 +13,7 @@
 NUM_CLASSES = 2
 EXPECTED_SEGMENTATION_OUTPUT_SHAPE = (1, NUM_CLASSES, 224, 224)
 EXPECTED_REGRESSION_OUTPUT_SHAPE = (1, 224, 224)
+EXPECTED_CLASSIFICATION_OUTPUT_SHAPE = (1, NUM_CLASSES)
 
 
 @pytest.fixture(scope="session")
@@ -25,6 +26,37 @@ def model_input() -> torch.Tensor:
     return torch.ones((1, NUM_CHANNELS, 224, 224))
 
 @pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
+def test_create_classification_model(backbone, model_factory: PrithviModelFactory, model_input):
+    model = model_factory.build_model(
+        "classification",
+        backbone=backbone,
+        decoder="IdentityDecoder",
+        in_channels=NUM_CHANNELS,
+        bands=PRETRAINED_BANDS,
+        pretrained=False,
+        num_classes=NUM_CLASSES,
+    )
+    model.eval()
+
+    with torch.no_grad():
+        assert model(model_input).output.shape == EXPECTED_CLASSIFICATION_OUTPUT_SHAPE
+
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
+def test_create_classification_model_no_in_channels(backbone, model_factory: PrithviModelFactory, model_input):
+    model = model_factory.build_model(
+        "classification",
+        backbone=backbone,
+        decoder="IdentityDecoder",
+        bands=PRETRAINED_BANDS,
+        pretrained=False,
+        num_classes=NUM_CLASSES,
+    )
+    model.eval()
+
+    with torch.no_grad():
+        assert model(model_input).output.shape == EXPECTED_CLASSIFICATION_OUTPUT_SHAPE
+
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
 @pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
 def test_create_segmentation_model(backbone, decoder, model_factory: PrithviModelFactory, model_input):
     model = model_factory.build_model(
@@ -41,8 +73,24 @@ def test_create_segmentation_model(backbone, decoder, model_factory: PrithviMode
     with torch.no_grad():
         assert model(model_input).output.shape == EXPECTED_SEGMENTATION_OUTPUT_SHAPE
 
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
+@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
+def test_create_segmentation_model_no_in_channels(backbone, decoder, model_factory: PrithviModelFactory, model_input):
+    model = model_factory.build_model(
+        "segmentation",
+        backbone=backbone,
+        decoder=decoder,
+        bands=PRETRAINED_BANDS,
+        pretrained=False,
+        num_classes=NUM_CLASSES,
+    )
+    model.eval()
 
-@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
+    with torch.no_grad():
+        assert model(model_input).output.shape == EXPECTED_SEGMENTATION_OUTPUT_SHAPE
+
+
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
 @pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
 def test_create_segmentation_model_with_aux_heads(backbone, decoder, model_factory: PrithviModelFactory, model_input):
     aux_heads_name = ["first_aux", "second_aux"]
@@ -67,7 +115,7 @@ def test_create_segmentation_model_with_aux_heads(backbone, decoder, model_facto
             assert output.shape == EXPECTED_SEGMENTATION_OUTPUT_SHAPE
 
 
-@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
 @pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
 def test_create_regression_model(backbone, decoder, model_factory: PrithviModelFactory, model_input):
     model = model_factory.build_model(
@@ -83,8 +131,22 @@ def test_create_regression_model(backbone, decoder, model_factory: PrithviModelF
     with torch.no_grad():
         assert model(model_input).output.shape == EXPECTED_REGRESSION_OUTPUT_SHAPE
 
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
+@pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
+def test_create_regression_model_no_in_channels(backbone, decoder, model_factory: PrithviModelFactory, model_input):
+    model = model_factory.build_model(
+        "regression",
+        backbone=backbone,
+        decoder=decoder,
+        bands=PRETRAINED_BANDS,
+        pretrained=False,
+    )
+    model.eval()
+
+    with torch.no_grad():
+        assert model(model_input).output.shape == EXPECTED_REGRESSION_OUTPUT_SHAPE
 
-@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
 @pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
 def test_create_regression_model_with_aux_heads(backbone, decoder, model_factory: PrithviModelFactory, model_input):
     aux_heads_name = ["first_aux", "second_aux"]
@@ -108,14 +170,14 @@ def test_create_regression_model_with_aux_heads(backbone, decoder, model_factory
             assert output.shape == EXPECTED_REGRESSION_OUTPUT_SHAPE
 
 
-@pytest.mark.parametrize("backbone", ["prithvi_vit_100", "prithvi_vit_300"])
+@pytest.mark.parametrize("backbone", ["prithvi_vit_100"])
 @pytest.mark.parametrize("decoder", ["FCNDecoder", "UperNetDecoder", "IdentityDecoder"])
 def test_create_model_with_extra_bands(backbone, decoder, model_factory: PrithviModelFactory):
     model = model_factory.build_model(
         "segmentation",
         backbone=backbone,
         decoder=decoder,
-        in_channels=NUM_CHANNELS,
+        in_channels=NUM_CHANNELS + 1,
         bands=[*PRETRAINED_BANDS, 7],  # add an extra band
         pretrained=False,
         num_classes=NUM_CLASSES,