diff --git a/tests/models/kosmos2_5/test_processor_kosmos2_5.py b/tests/models/kosmos2_5/test_processor_kosmos2_5.py
index 21be585131bb91..bbb308cba36aac 100644
--- a/tests/models/kosmos2_5/test_processor_kosmos2_5.py
+++ b/tests/models/kosmos2_5/test_processor_kosmos2_5.py
@@ -152,6 +152,59 @@ def test_model_input_names(self):
         with pytest.raises(ValueError):
             processor()
 
+    @require_torch
+    @require_vision
+    def test_unstructured_kwargs(self):
+        # Rewrite as KOSMOS-2.5 processor doesn't use `rescale_factor`
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs()
+        image_input = self.prepare_image_inputs()
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            max_patches=1024,
+            padding="max_length",
+            max_length=76,
+        )
+
+        self.assertEqual(inputs["flattened_patches"].shape[1], 1024)
+        self.assertEqual(len(inputs["input_ids"][0]), 76)
+
+    @require_torch
+    @require_vision
+    def test_unstructured_kwargs_batched(self):
+        # Rewrite as KOSMOS-2.5 processor doesn't use `rescale_factor`
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs(batch_size=2)
+        image_input = self.prepare_image_inputs(batch_size=2)
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            max_patches=1024,
+            padding="longest",
+            max_length=76,
+        )
+
+        self.assertEqual(inputs["flattened_patches"].shape[1], 1024)
+
+        self.assertEqual(len(inputs["input_ids"][0]), 76)
+
     @require_torch
     @require_vision
     def test_structured_kwargs_nested(self):