diff --git a/src/lighteval/models/litellm_model.py b/src/lighteval/models/litellm_model.py
index 840061788..6c37c0e68 100644
--- a/src/lighteval/models/litellm_model.py
+++ b/src/lighteval/models/litellm_model.py
@@ -22,6 +22,7 @@
 
 import logging
 import os
+import re
 import time
 from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass
@@ -93,12 +94,17 @@ def __init__(self, config, env_config) -> None:
         litellm.drop_params = True
         litellm.set_verbose = False
 
+    def is_reasoning_model(self):
+        return "o1" in self.model or "o3" in self.model or "R1" in self.model
+
     def _prepare_stop_sequence(self, stop_sequence):
         """Prepare and validate stop sequence."""
         if self.provider == "anthropic":
             # Filter out whitespace-only stop sequences
             if stop_sequence:
                 stop_sequence = [s for s in stop_sequence if s and s.strip()]
+        if not stop_sequence:  # If empty after filtering
+            stop_sequence = ["\n"]
         return stop_sequence
 
     def _prepare_max_new_tokens(self, max_new_tokens):
@@ -106,7 +112,7 @@ def _prepare_max_new_tokens(self, max_new_tokens):
         if not max_new_tokens or max_new_tokens <= 0:
             return None
 
-        if "o1" in self.model:
+        if self.is_reasoning_model():
             # We need to allow more tokens to include reasoning tokens
             max_new_tokens = min(max_new_tokens * 10, 32000)
         return max_new_tokens
@@ -132,8 +138,8 @@ def __call_api(self, prompt, return_logits, max_new_tokens, num_samples, stop_se
                     "n": num_samples,
                     "caching": True,
                 }
-                if "o1" in self.model:
-                    logger.warning("O1 models do not support temperature, top_p, stop sequence. Disabling.")
+                if self.is_reasoning_model():
+                    logger.warning("Reasoning models do not support temperature, top_p, stop sequence. Disabling.")
                 else:
                     kwargs["temperature"] = self.TEMPERATURE
                     kwargs["top_p"] = self.TOP_P
@@ -142,10 +148,17 @@ def __call_api(self, prompt, return_logits, max_new_tokens, num_samples, stop_se
                 response = litellm.completion(**kwargs)
 
                 # If response is empty, retry without caching (maybe the error is recoverable and solved with a retry)
-                if response.choices[0].message.content is None:
+                content = response.choices[0].message.content
+                if not content:
                     kwargs["caching"] = False
                     logger.info("Response is empty, retrying without caching")
                     response = litellm.completion(**kwargs)
+
+                if content is not None and "<think>" in content:
+                    logger.debug(f"Removing <think> tags from response: {content}")
+                    response.choices[0].message.content = re.sub(
+                        r"<think>.*?</think>", "", content, flags=re.DOTALL
+                    ).strip()
                 return response
             except litellm.BadRequestError as e:
                 if "message" in e.__dict__: