diff --git a/qdrant_client/async_qdrant_fastembed.py b/qdrant_client/async_qdrant_fastembed.py
index f9759d61..5035383d 100644
--- a/qdrant_client/async_qdrant_fastembed.py
+++ b/qdrant_client/async_qdrant_fastembed.py
@@ -791,7 +791,7 @@ def _resolve_query(
             Optional[models.Query]: query as it was, models.Query(nearest=query) or None
 
         Raises:
-            ValueError: if query is not of supported type or query is models.Document without `model` field
+            ValueError: if query is not of supported type
         """
         if isinstance(query, get_args(types.Query)) or isinstance(query, grpc.Query):
             return query
@@ -807,9 +807,6 @@ def _resolve_query(
             )
             return models.NearestQuery(nearest=query)
         if isinstance(query, INFERENCE_OBJECT_TYPES):
-            model_name = query.model
-            if model_name is None:
-                raise ValueError(f"`model` field has to be set explicitly in the {type(query)}")
             return models.NearestQuery(nearest=query)
         if query is None:
             return None
diff --git a/qdrant_client/conversions/conversion.py b/qdrant_client/conversions/conversion.py
index fff8283f..1a96b438 100644
--- a/qdrant_client/conversions/conversion.py
+++ b/qdrant_client/conversions/conversion.py
@@ -1036,7 +1036,7 @@ def convert_multi_dense_vector(cls, model: grpc.MultiDenseVector) -> List[List[f
     def convert_document(cls, model: grpc.Document) -> rest.Document:
         return rest.Document(
             text=model.text,
-            model=model.model if model.HasField("model") else None,
+            model=model.model,
             options=grpc_to_payload(model.options),
         )
 
@@ -1044,7 +1044,7 @@ def convert_document(cls, model: grpc.Document) -> rest.Document:
     def convert_image(cls, model: grpc.Image) -> rest.Image:
         return rest.Image(
             image=value_to_json(model.image),
-            model=model.model if model.HasField("model") else None,
+            model=model.model,
             options=grpc_to_payload(model.options),
         )
 
@@ -1052,7 +1052,7 @@ def convert_image(cls, model: grpc.Image) -> rest.Image:
     def convert_inference_object(cls, model: grpc.InferenceObject) -> rest.InferenceObject:
         return rest.InferenceObject(
             object=value_to_json(model.object),
-            model=model.model if model.HasField("model") else None,
+            model=model.model,
             options=grpc_to_payload(model.options),
         )
 
diff --git a/qdrant_client/grpc/points_pb2.py b/qdrant_client/grpc/points_pb2.py
index 1f1ad2f9..3dd2bba1 100644
--- a/qdrant_client/grpc/points_pb2.py
+++ b/qdrant_client/grpc/points_pb2.py
@@ -18,7 +18,7 @@
 from . import json_with_int_pb2 as json__with__int__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0cpoints.proto\x12\x06qdrant\x1a\x11\x63ollections.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x13json_with_int.proto\"8\n\rWriteOrdering\x12\'\n\x04type\x18\x01 \x01(\x0e\x32\x19.qdrant.WriteOrderingType\"Y\n\x0fReadConsistency\x12+\n\x04type\x18\x01 \x01(\x0e\x32\x1b.qdrant.ReadConsistencyTypeH\x00\x12\x10\n\x06\x66\x61\x63tor\x18\x02 \x01(\x04H\x00\x42\x07\n\x05value\"<\n\x07PointId\x12\r\n\x03num\x18\x01 \x01(\x04H\x00\x12\x0e\n\x04uuid\x18\x02 \x01(\tH\x00\x42\x12\n\x10point_id_options\"\x1d\n\rSparseIndices\x12\x0c\n\x04\x64\x61ta\x18\x01 \x03(\r\"\xa5\x01\n\x08\x44ocument\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\x12\n\x05model\x18\x03 \x01(\tH\x00\x88\x01\x01\x12.\n\x07options\x18\x04 \x03(\x0b\x32\x1d.qdrant.Document.OptionsEntry\x1a=\n\x0cOptionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x1c\n\x05value\x18\x02 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
 _WRITEORDERINGTYPE = DESCRIPTOR.enum_types_by_name['WriteOrderingType']
 WriteOrderingType = enum_type_wrapper.EnumTypeWrapper(_WRITEORDERINGTYPE)
@@ -1232,22 +1232,22 @@
   _RETRIEVEDPOINT_PAYLOADENTRY._serialized_options = b'8\001'
   _POINTSTRUCT_PAYLOADENTRY._options = None
   _POINTSTRUCT_PAYLOADENTRY._serialized_options = b'8\001'
-  _WRITEORDERINGTYPE._serialized_start=24265
-  _WRITEORDERINGTYPE._serialized_end=24318
-  _READCONSISTENCYTYPE._serialized_start=24320
-  _READCONSISTENCYTYPE._serialized_end=24376
-  _FIELDTYPE._serialized_start=24379
-  _FIELDTYPE._serialized_end=24552
-  _DIRECTION._serialized_start=24554
-  _DIRECTION._serialized_end=24584
-  _RECOMMENDSTRATEGY._serialized_start=24586
-  _RECOMMENDSTRATEGY._serialized_end=24639
-  _FUSION._serialized_start=24641
-  _FUSION._serialized_end=24668
-  _SAMPLE._serialized_start=24670
-  _SAMPLE._serialized_end=24690
-  _UPDATESTATUS._serialized_start=24692
-  _UPDATESTATUS._serialized_end=24783
+  _WRITEORDERINGTYPE._serialized_start=24220
+  _WRITEORDERINGTYPE._serialized_end=24273
+  _READCONSISTENCYTYPE._serialized_start=24275
+  _READCONSISTENCYTYPE._serialized_end=24331
+  _FIELDTYPE._serialized_start=24334
+  _FIELDTYPE._serialized_end=24507
+  _DIRECTION._serialized_start=24509
+  _DIRECTION._serialized_end=24539
+  _RECOMMENDSTRATEGY._serialized_start=24541
+  _RECOMMENDSTRATEGY._serialized_end=24594
+  _FUSION._serialized_start=24596
+  _FUSION._serialized_end=24623
+  _SAMPLE._serialized_start=24625
+  _SAMPLE._serialized_end=24645
+  _UPDATESTATUS._serialized_start=24647
+  _UPDATESTATUS._serialized_end=24738
   _WRITEORDERING._serialized_start=97
   _WRITEORDERING._serialized_end=153
   _READCONSISTENCY._serialized_start=155
@@ -1257,275 +1257,275 @@
   _SPARSEINDICES._serialized_start=308
   _SPARSEINDICES._serialized_end=337
   _DOCUMENT._serialized_start=340
-  _DOCUMENT._serialized_end=505
-  _DOCUMENT_OPTIONSENTRY._serialized_start=434
-  _DOCUMENT_OPTIONSENTRY._serialized_end=495
-  _IMAGE._serialized_start=508
-  _IMAGE._serialized_end=683
-  _IMAGE_OPTIONSENTRY._serialized_start=434
-  _IMAGE_OPTIONSENTRY._serialized_end=495
-  _INFERENCEOBJECT._serialized_start=686
-  _INFERENCEOBJECT._serialized_end=882
-  _INFERENCEOBJECT_OPTIONSENTRY._serialized_start=434
-  _INFERENCEOBJECT_OPTIONSENTRY._serialized_end=495
-  _VECTOR._serialized_start=885
-  _VECTOR._serialized_end=1260
-  _VECTOROUTPUT._serialized_start=1263
-  _VECTOROUTPUT._serialized_end=1531
-  _DENSEVECTOR._serialized_start=1533
-  _DENSEVECTOR._serialized_end=1560
-  _SPARSEVECTOR._serialized_start=1562
-  _SPARSEVECTOR._serialized_end=1609
-  _MULTIDENSEVECTOR._serialized_start=1611
-  _MULTIDENSEVECTOR._serialized_end=1667
-  _VECTORINPUT._serialized_start=1670
-  _VECTORINPUT._serialized_end=1965
-  _SHARDKEYSELECTOR._serialized_start=1967
-  _SHARDKEYSELECTOR._serialized_end=2023
-  _UPSERTPOINTS._serialized_start=2026
-  _UPSERTPOINTS._serialized_end=2271
-  _DELETEPOINTS._serialized_start=2274
-  _DELETEPOINTS._serialized_end=2522
-  _GETPOINTS._serialized_start=2525
-  _GETPOINTS._serialized_end=2914
-  _UPDATEPOINTVECTORS._serialized_start=2917
-  _UPDATEPOINTVECTORS._serialized_end=3169
-  _POINTVECTORS._serialized_start=3171
-  _POINTVECTORS._serialized_end=3248
-  _DELETEPOINTVECTORS._serialized_start=3251
-  _DELETEPOINTVECTORS._serialized_end=3556
-  _SETPAYLOADPOINTS._serialized_start=3559
-  _SETPAYLOADPOINTS._serialized_end=3996
-  _SETPAYLOADPOINTS_PAYLOADENTRY._serialized_start=3856
-  _SETPAYLOADPOINTS_PAYLOADENTRY._serialized_end=3917
-  _DELETEPAYLOADPOINTS._serialized_start=3999
-  _DELETEPAYLOADPOINTS._serialized_end=4308
-  _CLEARPAYLOADPOINTS._serialized_start=4311
-  _CLEARPAYLOADPOINTS._serialized_end=4565
-  _CREATEFIELDINDEXCOLLECTION._serialized_start=4568
-  _CREATEFIELDINDEXCOLLECTION._serialized_end=4871
-  _DELETEFIELDINDEXCOLLECTION._serialized_start=4874
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+  _RETRIEVEDPOINT_PAYLOADENTRY._serialized_start=3811
+  _RETRIEVEDPOINT_PAYLOADENTRY._serialized_end=3872
+  _GETRESPONSE._serialized_start=20893
+  _GETRESPONSE._serialized_end=20960
+  _RECOMMENDRESPONSE._serialized_start=20962
+  _RECOMMENDRESPONSE._serialized_end=21032
+  _RECOMMENDBATCHRESPONSE._serialized_start=21034
+  _RECOMMENDBATCHRESPONSE._serialized_end=21109
+  _DISCOVERRESPONSE._serialized_start=21111
+  _DISCOVERRESPONSE._serialized_end=21180
+  _DISCOVERBATCHRESPONSE._serialized_start=21182
+  _DISCOVERBATCHRESPONSE._serialized_end=21256
+  _RECOMMENDGROUPSRESPONSE._serialized_start=21258
+  _RECOMMENDGROUPSRESPONSE._serialized_end=21335
+  _UPDATEBATCHRESPONSE._serialized_start=21337
+  _UPDATEBATCHRESPONSE._serialized_end=21410
+  _FACETRESPONSE._serialized_start=21412
+  _FACETRESPONSE._serialized_end=21473
+  _SEARCHMATRIXPAIRSRESPONSE._serialized_start=21475
+  _SEARCHMATRIXPAIRSRESPONSE._serialized_end=21559
+  _SEARCHMATRIXOFFSETSRESPONSE._serialized_start=21561
+  _SEARCHMATRIXOFFSETSRESPONSE._serialized_end=21649
+  _FILTER._serialized_start=21652
+  _FILTER._serialized_end=21824
+  _MINSHOULD._serialized_start=21826
+  _MINSHOULD._serialized_end=21895
+  _CONDITION._serialized_start=21898
+  _CONDITION._serialized_end=22179
+  _ISEMPTYCONDITION._serialized_start=22181
+  _ISEMPTYCONDITION._serialized_end=22212
+  _ISNULLCONDITION._serialized_start=22214
+  _ISNULLCONDITION._serialized_end=22244
+  _HASIDCONDITION._serialized_start=22246
+  _HASIDCONDITION._serialized_end=22295
+  _NESTEDCONDITION._serialized_start=22297
+  _NESTEDCONDITION._serialized_end=22359
+  _FIELDCONDITION._serialized_start=22362
+  _FIELDCONDITION._serialized_end=22671
+  _MATCH._serialized_start=22674
+  _MATCH._serialized_end=22965
+  _REPEATEDSTRINGS._serialized_start=22967
+  _REPEATEDSTRINGS._serialized_end=23001
+  _REPEATEDINTEGERS._serialized_start=23003
+  _REPEATEDINTEGERS._serialized_end=23039
+  _RANGE._serialized_start=23041
+  _RANGE._serialized_end=23148
+  _DATETIMERANGE._serialized_start=23151
+  _DATETIMERANGE._serialized_end=23378
+  _GEOBOUNDINGBOX._serialized_start=23380
+  _GEOBOUNDINGBOX._serialized_end=23472
+  _GEORADIUS._serialized_start=23474
+  _GEORADIUS._serialized_end=23535
+  _GEOLINESTRING._serialized_start=23537
+  _GEOLINESTRING._serialized_end=23586
+  _GEOPOLYGON._serialized_start=23588
+  _GEOPOLYGON._serialized_end=23683
+  _VALUESCOUNT._serialized_start=23685
+  _VALUESCOUNT._serialized_end=23798
+  _POINTSSELECTOR._serialized_start=23800
+  _POINTSSELECTOR._serialized_end=23917
+  _POINTSIDSLIST._serialized_start=23919
+  _POINTSIDSLIST._serialized_end=23964
+  _POINTSTRUCT._serialized_start=23967
+  _POINTSTRUCT._serialized_end=24180
+  _POINTSTRUCT_PAYLOADENTRY._serialized_start=3811
+  _POINTSTRUCT_PAYLOADENTRY._serialized_end=3872
+  _GEOPOINT._serialized_start=24182
+  _GEOPOINT._serialized_end=24218
 # @@protoc_insertion_point(module_scope)
diff --git a/qdrant_client/http/models/models.py b/qdrant_client/http/models/models.py
index 4e52f071..63907e0b 100644
--- a/qdrant_client/http/models/models.py
+++ b/qdrant_client/http/models/models.py
@@ -607,9 +607,8 @@ class Document(BaseModel, extra="forbid"):
     """
 
     text: str = Field(..., description="Text of the document This field will be used as input for the embedding model")
-    model: Optional[str] = Field(
-        default=None,
-        description="Name of the model used to generate the vector List of available models depends on a provider",
+    model: str = Field(
+        ..., description="Name of the model used to generate the vector List of available models depends on a provider"
     )
     options: Optional[Dict[str, Any]] = Field(
         default=None, description="Parameters for the model Values of the parameters are model-specific"
@@ -873,9 +872,8 @@ class Image(BaseModel, extra="forbid"):
     """
 
     image: Any = Field(..., description="Image data: base64 encoded image or an URL")
-    model: Optional[str] = Field(
-        default=None,
-        description="Name of the model used to generate the vector List of available models depends on a provider",
+    model: str = Field(
+        ..., description="Name of the model used to generate the vector List of available models depends on a provider"
     )
     options: Optional[Dict[str, Any]] = Field(
         default=None, description="Parameters for the model Values of the parameters are model-specific"
@@ -921,9 +919,8 @@ class InferenceObject(BaseModel, extra="forbid"):
         ...,
         description="Arbitrary data, used as input for the embedding model Used if the model requires more than one input or a custom input",
     )
-    model: Optional[str] = Field(
-        default=None,
-        description="Name of the model used to generate the vector List of available models depends on a provider",
+    model: str = Field(
+        ..., description="Name of the model used to generate the vector List of available models depends on a provider"
     )
     options: Optional[Dict[str, Any]] = Field(
         default=None, description="Parameters for the model Values of the parameters are model-specific"
diff --git a/qdrant_client/proto/points.proto b/qdrant_client/proto/points.proto
index 978eec7a..baca3150 100644
--- a/qdrant_client/proto/points.proto
+++ b/qdrant_client/proto/points.proto
@@ -48,19 +48,19 @@ message SparseIndices {
 
 message Document {
   string text = 1; // Text of the document
-  optional string model = 3; // Model name
+  string model = 3; // Model name
   map<string, Value> options = 4; // Model options
 }
 
 message Image {
   Value image = 1; // Image data, either base64 encoded or URL
-  optional string model = 2; // Model name
+  string model = 2; // Model name
   map<string, Value> options = 3; // Model options
 }
 
 message InferenceObject {
   Value object = 1; // Object to infer
-  optional string model = 2; // Model name
+  string model = 2; // Model name
   map<string, Value> options = 3; // Model options
 }
 
diff --git a/qdrant_client/qdrant_fastembed.py b/qdrant_client/qdrant_fastembed.py
index abc25699..5e25c4bd 100644
--- a/qdrant_client/qdrant_fastembed.py
+++ b/qdrant_client/qdrant_fastembed.py
@@ -872,7 +872,7 @@ def _resolve_query(
             Optional[models.Query]: query as it was, models.Query(nearest=query) or None
 
         Raises:
-            ValueError: if query is not of supported type or query is models.Document without `model` field
+            ValueError: if query is not of supported type
         """
         if isinstance(query, get_args(types.Query)) or isinstance(query, grpc.Query):
             return query
@@ -892,9 +892,6 @@ def _resolve_query(
             return models.NearestQuery(nearest=query)
 
         if isinstance(query, INFERENCE_OBJECT_TYPES):
-            model_name = query.model
-            if model_name is None:
-                raise ValueError(f"`model` field has to be set explicitly in the {type(query)}")
             return models.NearestQuery(nearest=query)
 
         if query is None:
diff --git a/tests/conversions/fixtures.py b/tests/conversions/fixtures.py
index cb4683d9..436de31e 100644
--- a/tests/conversions/fixtures.py
+++ b/tests/conversions/fixtures.py
@@ -312,21 +312,18 @@
     text="random text", model="bert", options=payload_to_grpc({"a": 2, "b": [1, 2], "c": "useful"})
 )
 document_without_options = grpc.Document(text="random text", model="bert")
-document_only_text = grpc.Document(text="random text")
 image_with_options = grpc.Image(
     image=json_to_value("path_to_image"),
     model="resnet",
     options=payload_to_grpc({"a": 2, "b": [1, 2], "c": "useful"}),
 )
 image_without_options = grpc.Image(image=json_to_value("path_to_image"), model="resnet")
-image_only_image = grpc.Image(image=json_to_value("path_to_image"))
 inference_object_with_options = grpc.InferenceObject(
     object=json_to_value("path_to_image"),
     model="bert",
     options=payload_to_grpc({"a": 2, "b": [1, 2], "c": "useful"}),
 )
 inference_object_without_options = grpc.InferenceObject(object=json_to_value("text"), model="bert")
-inference_object_only_object = grpc.InferenceObject(object=json_to_value("text"))
 order_value_int = grpc.OrderValue(int=42)
 order_value_float = grpc.OrderValue(float=42.0)
 single_vector_output = grpc.VectorsOutput(vector=grpc.VectorOutput(data=[1.0, 2.0, 3.0, 4.0]))
@@ -1254,13 +1251,10 @@
 vector_input_multi = grpc.VectorInput(multi_dense=multi_dense_vector)
 vector_input_doc_with_options = grpc.VectorInput(document=document_with_options)
 vector_input_doc_without_options = grpc.VectorInput(document=document_without_options)
-vector_input_doc_only_text = grpc.VectorInput(document=document_only_text)
 vector_input_image_with_options = grpc.VectorInput(image=image_with_options)
 vector_input_image_without_options = grpc.VectorInput(image=image_without_options)
-vector_input_image_only_image = grpc.VectorInput(image=image_only_image)
 vector_input_inference_with_options = grpc.VectorInput(object=inference_object_with_options)
 vector_input_inference_without_options = grpc.VectorInput(object=inference_object_without_options)
-vector_input_inference_only_object = grpc.VectorInput(object=inference_object_only_object)
 
 recommend_input = grpc.RecommendInput(
     positive=[
@@ -1272,15 +1266,12 @@
     positive=[
         vector_input_doc_with_options,
         vector_input_doc_without_options,
-        vector_input_doc_only_text,
     ],
     negative=[
         vector_input_image_with_options,
         vector_input_image_without_options,
-        vector_input_image_only_image,
         vector_input_inference_with_options,
         vector_input_inference_without_options,
-        vector_input_inference_only_object,
     ],
 )
 recommend_input_strategy = grpc.RecommendInput(