diff --git a/app/llm.py b/app/llm.py
index 5b599e8..3314062 100644
--- a/app/llm.py
+++ b/app/llm.py
@@ -14,6 +14,7 @@ from app.config import LLMSettings, config
 from app.logger import logger  # Assuming a logger is set up in your app
 from app.schema import Message, TOOL_CHOICE_TYPE, ROLE_VALUES, TOOL_CHOICE_VALUES, ToolChoice
 
+REASONING_MODELS = ["o1", "o3-mini"]
 
 class LLM:
     _instances: Dict[str, "LLM"] = {}
@@ -133,27 +134,30 @@ class LLM:
             else:
                 messages = self.format_messages(messages)
 
+            params = {
+                "model": self.model,
+                "messages": messages,
+            }
+
+            if self.model in REASONING_MODELS:
+                    params["max_completion_tokens"] = self.max_tokens
+            else:
+                params["max_tokens"] = self.max_tokens
+                params["temperature"] = temperature or self.temperature
+
             if not stream:
                 # Non-streaming request
-                response = await self.client.chat.completions.create(
-                    model=self.model,
-                    messages=messages,
-                    max_tokens=self.max_tokens,
-                    temperature=temperature or self.temperature,
-                    stream=False,
-                )
+                params["stream"] = False
+
+                response = await self.client.chat.completions.create(**params)
+
                 if not response.choices or not response.choices[0].message.content:
                     raise ValueError("Empty or invalid response from LLM")
                 return response.choices[0].message.content
 
             # Streaming request
-            response = await self.client.chat.completions.create(
-                model=self.model,
-                messages=messages,
-                max_tokens=self.max_tokens,
-                temperature=temperature or self.temperature,
-                stream=True,
-            )
+            params["stream"] = True
+            response = await self.client.chat.completions.create(**params)
 
             collected_messages = []
             async for chunk in response:
@@ -230,16 +234,22 @@ class LLM:
                         raise ValueError("Each tool must be a dict with 'type' field")
 
             # Set up the completion request
-            response = await self.client.chat.completions.create(
-                model=self.model,
-                messages=messages,
-                temperature=temperature or self.temperature,
-                max_tokens=self.max_tokens,
-                tools=tools,
-                tool_choice=tool_choice,
-                timeout=timeout,
+            params = {
+                "model": self.model,
+                "messages": messages,
+                "tools": tools,
+                "tool_choice": tool_choice,
+                "timeout": timeout,
                 **kwargs,
-            )
+            }
+
+            if self.model in REASONING_MODELS:
+                params["max_completion_tokens"] = self.max_tokens
+            else:
+                params["max_tokens"] = self.max_tokens
+                params["temperature"] = temperature or self.temperature
+
+            response = await self.client.chat.completions.create(**params)
 
             # Check if response is valid
             if not response.choices or not response.choices[0].message: