diff --git a/src/khoj/processor/conversation/openai/utils.py b/src/khoj/processor/conversation/openai/utils.py
index fd3d0fd7..20e632d0 100644
--- a/src/khoj/processor/conversation/openai/utils.py
+++ b/src/khoj/processor/conversation/openai/utils.py
@@ -14,6 +14,7 @@ from openai.lib.streaming.chat import (
     ChatCompletionStreamEvent,
     ContentDeltaEvent,
 )
+from openai.types.chat.chat_completion import ChatCompletion
 from openai.types.chat.chat_completion_chunk import (
     ChatCompletionChunk,
     Choice,
@@ -78,7 +79,11 @@ def completion_with_backoff(
         client = get_openai_client(openai_api_key, api_base_url)
         openai_clients[client_key] = client
 
+    stream = not is_non_streaming_model(model_name, api_base_url)
     stream_processor = default_stream_processor
+    if stream:
+        model_kwargs["stream_options"] = {"include_usage": True}
+
     formatted_messages = format_message_for_api(messages, api_base_url)
 
     # Tune reasoning models arguments
@@ -109,23 +114,33 @@ def completion_with_backoff(
             add_qwen_no_think_tag(formatted_messages)
 
     read_timeout = 300 if is_local_api(api_base_url) else 60
-    model_kwargs["stream_options"] = {"include_usage": True}
     if os.getenv("KHOJ_LLM_SEED"):
         model_kwargs["seed"] = int(os.getenv("KHOJ_LLM_SEED"))
 
     aggregated_response = ""
-    with client.beta.chat.completions.stream(
-        messages=formatted_messages,  # type: ignore
-        model=model_name,
-        temperature=temperature,
-        timeout=httpx.Timeout(30, read=read_timeout),
-        **model_kwargs,
-    ) as chat:
-        for chunk in stream_processor(chat):
-            if chunk.type == "content.delta":
-                aggregated_response += chunk.delta
-            elif chunk.type == "thought.delta":
-                pass
+    if stream:
+        with client.beta.chat.completions.stream(
+            messages=formatted_messages,  # type: ignore
+            model=model_name,
+            temperature=temperature,
+            timeout=httpx.Timeout(30, read=read_timeout),
+            **model_kwargs,
+        ) as chat:
+            for chunk in stream_processor(chat):
+                if chunk.type == "content.delta":
+                    aggregated_response += chunk.delta
+                elif chunk.type == "thought.delta":
+                    pass
+    else:
+        # Non-streaming chat completion
+        chunk = client.beta.chat.completions.parse(
+            messages=formatted_messages,  # type: ignore
+            model=model_name,
+            temperature=temperature,
+            timeout=httpx.Timeout(30, read=read_timeout),
+            **model_kwargs,
+        )
+        aggregated_response = chunk.choices[0].message.content
 
     # Calculate cost of chat
     input_tokens = chunk.usage.prompt_tokens if hasattr(chunk, "usage") and chunk.usage else 0
@@ -182,7 +197,11 @@ async def chat_completion_with_backoff(
         client = get_openai_async_client(openai_api_key, api_base_url)
         openai_async_clients[client_key] = client
 
+    stream = not is_non_streaming_model(model_name, api_base_url)
     stream_processor = adefault_stream_processor
+    if stream:
+        model_kwargs["stream_options"] = {"include_usage": True}
+
     formatted_messages = format_message_for_api(messages, api_base_url)
 
     # Configure thinking for openai reasoning models
@@ -228,9 +247,7 @@ async def chat_completion_with_backoff(
         if not deepthought:
             add_qwen_no_think_tag(formatted_messages)
 
-    stream = True
     read_timeout = 300 if is_local_api(api_base_url) else 60
-    model_kwargs["stream_options"] = {"include_usage": True}
     if os.getenv("KHOJ_LLM_SEED"):
         model_kwargs["seed"] = int(os.getenv("KHOJ_LLM_SEED"))
 
@@ -238,7 +255,7 @@ async def chat_completion_with_backoff(
     final_chunk = None
     response_started = False
     start_time = perf_counter()
-    chat_stream: openai.AsyncStream[ChatCompletionChunk] = await client.chat.completions.create(
+    response: openai.AsyncStream[ChatCompletionChunk] | ChatCompletion = await client.chat.completions.create(
         messages=formatted_messages,  # type: ignore
         model=model_name,
         stream=stream,
@@ -246,26 +263,34 @@ async def chat_completion_with_backoff(
         timeout=httpx.Timeout(30, read=read_timeout),
         **model_kwargs,
     )
-    async for chunk in stream_processor(chat_stream):
-        # Log the time taken to start response
-        if not response_started:
-            response_started = True
-            logger.info(f"First response took: {perf_counter() - start_time:.3f} seconds")
-        # Keep track of the last chunk for usage data
-        final_chunk = chunk
-        # Skip empty chunks
-        if len(chunk.choices) == 0:
-            continue
-        # Handle streamed response chunk
-        response_chunk: ResponseWithThought = None
-        response_delta = chunk.choices[0].delta
-        if response_delta.content:
-            response_chunk = ResponseWithThought(response=response_delta.content)
-            aggregated_response += response_chunk.response
-        elif response_delta.thought:
-            response_chunk = ResponseWithThought(thought=response_delta.thought)
-        if response_chunk:
-            yield response_chunk
+    if not stream:
+        # If not streaming, we can return the response directly
+        if len(response.choices) == 0 or not response.choices[0].message:
+            raise ValueError("No response by model.")
+        aggregated_response = response.choices[0].message.content
+        final_chunk = response
+        yield ResponseWithThought(response=aggregated_response)
+    else:
+        async for chunk in stream_processor(response):
+            # Log the time taken to start response
+            if not response_started:
+                response_started = True
+                logger.info(f"First response took: {perf_counter() - start_time:.3f} seconds")
+            # Keep track of the last chunk for usage data
+            final_chunk = chunk
+            # Skip empty chunks
+            if len(chunk.choices) == 0:
+                continue
+            # Handle streamed response chunk
+            response_chunk: ResponseWithThought = None
+            response_delta = chunk.choices[0].delta
+            if response_delta.content:
+                response_chunk = ResponseWithThought(response=response_delta.content)
+                aggregated_response += response_chunk.response
+            elif response_delta.thought:
+                response_chunk = ResponseWithThought(thought=response_delta.thought)
+            if response_chunk:
+                yield response_chunk
 
     # Calculate cost of chat after stream finishes
     input_tokens, output_tokens, cost = 0, 0, 0
@@ -354,6 +379,14 @@ def is_openai_reasoning_model(model_name: str, api_base_url: str = None) -> bool
     return model_name.startswith("o") and is_openai_api(api_base_url)
 
 
+def is_non_streaming_model(model_name: str, api_base_url: str = None) -> bool:
+    """
+    Check if model response should not be streamed.
+    """
+    # Some OpenAI models requires biometrics to stream. Avoid streaming their responses.
+    return model_name in ["o3", "o3-pro"] and is_openai_api(api_base_url)
+
+
 def is_twitter_reasoning_model(model_name: str, api_base_url: str = None) -> bool:
     """
     Check if the model is a Twitter reasoning model