diff --git a/clarifai/client/model.py b/clarifai/client/model.py
index 58dd8ce..3d41475 100644
--- a/clarifai/client/model.py
+++ b/clarifai/client/model.py
@@ -490,7 +490,7 @@ def predict_by_filepath(self,
 
     Args:
         filepath (str): The filepath to predict.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -529,7 +529,7 @@ def predict_by_bytes(self,
 
     Args:
         input_bytes (bytes): File Bytes to predict on.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -596,7 +596,7 @@ def predict_by_url(self,
 
     Args:
         url (str): The URL to predict.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio'.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio'.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -709,7 +709,7 @@ def generate(self,
 
   def generate_by_filepath(self,
                            filepath: str,
-                           input_type: str,
+                           input_type: str = None,
                            compute_cluster_id: str = None,
                            nodepool_id: str = None,
                            deployment_id: str = None,
@@ -719,7 +719,7 @@ def generate_by_filepath(self,
 
     Args:
         filepath (str): The filepath to predict.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -754,7 +754,7 @@ def generate_by_filepath(self,
 
   def generate_by_bytes(self,
                         input_bytes: bytes,
-                        input_type: str,
+                        input_type: str = None,
                         compute_cluster_id: str = None,
                         nodepool_id: str = None,
                         deployment_id: str = None,
@@ -764,7 +764,7 @@ def generate_by_bytes(self,
 
     Args:
         input_bytes (bytes): File Bytes to predict on.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -778,11 +778,18 @@ def generate_by_bytes(self,
         >>> from clarifai.client.model import Model
         >>> model = Model("https://clarifai.com/openai/chat-completion/models/GPT-4")
         >>> stream_response = model.generate_by_bytes(b'Write a tweet on future of AI',
-                                                      input_type='text',
                                                       deployment_id='deployment_id',
                                                       inference_params=dict(temperature=str(0.7), max_tokens=30)))
         >>> list_stream_response = [response for response in stream_response]
     """
+    if not input_type:
+      model_input_types = self.get_model_input_types()
+      if len(model_input_types) > 1:
+        raise UserError(
+            "Model has multiple input types. Please use model.predict() for this multi-modal model."
+        )
+      input_type = model_input_types[0]
+
     if input_type not in {'image', 'text', 'video', 'audio'}:
       raise UserError(
           f"Got input type {input_type} but expected one of image, text, video, audio.")
@@ -818,7 +825,7 @@ def generate_by_bytes(self,
 
   def generate_by_url(self,
                       url: str,
-                      input_type: str,
+                      input_type: str = None,
                       compute_cluster_id: str = None,
                       nodepool_id: str = None,
                       deployment_id: str = None,
@@ -828,7 +835,7 @@ def generate_by_url(self,
 
     Args:
         url (str): The URL to predict.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -843,9 +850,17 @@ def generate_by_url(self,
         >>> model = Model("url") # Example URL: https://clarifai.com/clarifai/main/models/general-image-recognition
                     or
         >>> model = Model(model_id='model_id', user_id='user_id', app_id='app_id')
-        >>> stream_response = model.generate_by_url('url', 'image', deployment_id='deployment_id')
+        >>> stream_response = model.generate_by_url('url', deployment_id='deployment_id')
         >>> list_stream_response = [response for response in stream_response]
     """
+    if not input_type:
+      model_input_types = self.get_model_input_types()
+      if len(model_input_types) > 1:
+        raise UserError(
+            "Model has multiple input types. Please use model.predict() for this multi-modal model."
+        )
+      input_type = model_input_types[0]
+
     if input_type not in {'image', 'text', 'video', 'audio'}:
       raise UserError(
           f"Got input type {input_type} but expected one of image, text, video, audio.")
@@ -934,7 +949,7 @@ def stream(self,
 
   def stream_by_filepath(self,
                          filepath: str,
-                         input_type: str,
+                         input_type: str = None,
                          compute_cluster_id: str = None,
                          nodepool_id: str = None,
                          deployment_id: str = None,
@@ -944,7 +959,7 @@ def stream_by_filepath(self,
 
     Args:
         filepath (str): The filepath to predict.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -957,7 +972,7 @@ def stream_by_filepath(self,
     Example:
         >>> from clarifai.client.model import Model
         >>> model = Model("url")
-        >>> stream_response = model.stream_by_filepath('/path/to/image.jpg', 'image', deployment_id='deployment_id')
+        >>> stream_response = model.stream_by_filepath('/path/to/image.jpg', deployment_id='deployment_id')
         >>> list_stream_response = [response for response in stream_response]
     """
     if not os.path.isfile(filepath):
@@ -977,7 +992,7 @@ def stream_by_filepath(self,
 
   def stream_by_bytes(self,
                       input_bytes_iterator: Iterator[bytes],
-                      input_type: str,
+                      input_type: str = None,
                       compute_cluster_id: str = None,
                       nodepool_id: str = None,
                       deployment_id: str = None,
@@ -987,7 +1002,7 @@ def stream_by_bytes(self,
 
     Args:
         input_bytes_iterator (Iterator[bytes]): Iterator of file bytes to predict on.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -1001,11 +1016,18 @@ def stream_by_bytes(self,
         >>> from clarifai.client.model import Model
         >>> model = Model("https://clarifai.com/openai/chat-completion/models/GPT-4")
         >>> stream_response = model.stream_by_bytes(iter([b'Write a tweet on future of AI']),
-                                                    input_type='text',
                                                     deployment_id='deployment_id',
                                                     inference_params=dict(temperature=str(0.7), max_tokens=30)))
         >>> list_stream_response = [response for response in stream_response]
     """
+    if not input_type:
+      model_input_types = self.get_model_input_types()
+      if len(model_input_types) > 1:
+        raise UserError(
+            "Model has multiple input types. Please use model.predict() for this multi-modal model."
+        )
+      input_type = model_input_types[0]
+
     if input_type not in {'image', 'text', 'video', 'audio'}:
       raise UserError(
           f"Got input type {input_type} but expected one of image, text, video, audio.")
@@ -1041,7 +1063,7 @@ def input_generator():
 
   def stream_by_url(self,
                     url_iterator: Iterator[str],
-                    input_type: str,
+                    input_type: str = None,
                     compute_cluster_id: str = None,
                     nodepool_id: str = None,
                     deployment_id: str = None,
@@ -1051,7 +1073,7 @@ def stream_by_url(self,
 
     Args:
         url_iterator (Iterator[str]): Iterator of URLs to predict.
-        input_type (str): The type of input. Can be 'image', 'text', 'video' or 'audio.
+        input_type (str, optional): The type of input. Can be 'image', 'text', 'video' or 'audio.
         compute_cluster_id (str): The compute cluster ID to use for the model.
         nodepool_id (str): The nodepool ID to use for the model.
         deployment_id (str): The deployment ID to use for the model.
@@ -1064,9 +1086,17 @@ def stream_by_url(self,
     Example:
         >>> from clarifai.client.model import Model
         >>> model = Model("url")
-        >>> stream_response = model.stream_by_url(iter(['url']), 'image', deployment_id='deployment_id')
+        >>> stream_response = model.stream_by_url(iter(['url']), deployment_id='deployment_id')
         >>> list_stream_response = [response for response in stream_response]
     """
+    if not input_type:
+      model_input_types = self.get_model_input_types()
+      if len(model_input_types) > 1:
+        raise UserError(
+            "Model has multiple input types. Please use model.predict() for this multi-modal model."
+        )
+      input_type = model_input_types[0]
+
     if input_type not in {'image', 'text', 'video', 'audio'}:
       raise UserError(
           f"Got input type {input_type} but expected one of image, text, video, audio.")