diff --git a/neon_data_models/models/api/mq/brainforge.py b/neon_data_models/models/api/mq/brainforge.py
index 5f484a7..437cbad 100644
--- a/neon_data_models/models/api/mq/brainforge.py
+++ b/neon_data_models/models/api/mq/brainforge.py
@@ -28,6 +28,7 @@
 from pydantic import Field
 
 from neon_data_models.models.base.contexts import MQContext
+from neon_data_models.models.base.mq import MQResponse
 from neon_data_models.models.api.llm import BrainForgeLLM, LLMRequest, LLMResponse, LLMPersona
 from neon_data_models.models.api.http.brainforge import LLMGetModelsHttpResponse, LLMGetPersonasHttpRequest
 
@@ -37,7 +38,7 @@ class LLMGetModels(MQContext):
         description="ID of user to get models for")
 
 
-class LLMGetModelsResponse(MQContext, LLMGetModelsHttpResponse):
+class LLMGetModelsResponse(MQResponse, LLMGetModelsHttpResponse):
     pass
 
 
@@ -51,7 +52,7 @@ def model_version(self):
         return self.model_id.split("@", 1)[1]
 
 
-class LLMGetPersonasResponse(MQContext):
+class LLMGetPersonasResponse(MQResponse):
     model: Optional[BrainForgeLLM] = Field(
         "Full configuration of requested model if model is loaded and access "
         "is allowed, else None.")
@@ -64,7 +65,7 @@ def personas(self) -> List[LLMPersona]:
         return self.model.personas if self.model else []
 
 
-class LLMGetInference(LLMRequest, MQContext):
+class LLMGetInference(LLMRequest, MQResponse):
     user_id: str = Field("ID of user making the request")
 
     def as_llm_request(self):
@@ -80,7 +81,7 @@ class LLMGetCompletion(LLMGetModels):
         description="Dictionary of kwargs to pass to OpenAI completion request")
 
 
-class LLMGetCompletionResponse(MQContext):
+class LLMGetCompletionResponse(MQResponse):
     openai_response: dict = Field(
         description="OpenAI ChatCompletion model")
 
@@ -98,12 +99,12 @@ class LLMGetTokenizerChatTemplatedString(LLMGetModels):
                     "else a single string")
 
 
-class LLMGetTokenizerChatTemplatedStringResponse(MQContext):
+class LLMGetTokenizerChatTemplatedStringResponse(MQResponse):
     prompt: Union[List[str], str] = Field(
         description="Prompt generated by the tokenizer")
 
 
-class LLMGetInferenceResponse(LLMResponse, MQContext):
+class LLMGetInferenceResponse(LLMResponse, MQResponse):
     pass