diff --git a/src/api/models/bedrock.py b/src/api/models/bedrock.py
index 1d096ff..61bff54 100644
--- a/src/api/models/bedrock.py
+++ b/src/api/models/bedrock.py
@@ -210,7 +210,6 @@ class BedrockModel(BaseChatModel):
         """Default implementation for Chat Stream API"""
         response = self._invoke_bedrock(chat_request, stream=True)
         message_id = self.generate_message_id()
-
         stream = response.get("stream")
         for chunk in stream:
             stream_response = self._create_response_stream(
@@ -398,7 +397,6 @@ class BedrockModel(BaseChatModel):
 
         Ref: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html
         """
-
         messages = self._parse_messages(chat_request)
         system_prompts = self._parse_system_prompts(chat_request)
 
@@ -424,14 +422,17 @@ class BedrockModel(BaseChatModel):
         if chat_request.reasoning_effort:
             # From OpenAI api, the max_token is not supported in reasoning mode
             # Use max_completion_tokens if provided.
+
             max_tokens = chat_request.max_completion_tokens if chat_request.max_completion_tokens else chat_request.max_tokens
+            budget_tokens = self._calc_budget_tokens(max_tokens, chat_request.reasoning_effort)
             inference_config["maxTokens"] = max_tokens
             # unset topP - Not supported
             inference_config.pop("topP")
+
             args["additionalModelRequestFields"] = {
                 "reasoning_config": {
                     "type": "enabled",
-                    "budget_tokens": max_tokens - 1
+                    "budget_tokens": budget_tokens
                 }
             }
         # add tool config
@@ -493,7 +494,7 @@ class BedrockModel(BaseChatModel):
             for c in content:
                 if "reasoningContent" in c:
                     message.reasoning_content = c["reasoningContent"]["reasoningText"].get("text", "")
-                if "text" in c:
+                elif "text" in c:
                     message.content = c["text"]
                 else:
                     logger.warning("Unknown tag in message content " + ",".join(c.keys()))
@@ -564,6 +565,12 @@ class BedrockModel(BaseChatModel):
                 message = ChatResponseMessage(
                     content=delta["text"],
                 )
+            elif "reasoningContent" in delta:
+                # ignore "signature" in the delta.
+                if "text" in delta["reasoningContent"]:
+                    message = ChatResponseMessage(
+                        reasoning_content=delta["reasoningContent"]["text"],
+                    )
             else:
                 # tool use
                 index = chunk["contentBlockDelta"]["contentBlockIndex"] - 1
@@ -701,6 +708,18 @@ class BedrockModel(BaseChatModel):
             }
         }
 
+    def _calc_budget_tokens(self, max_tokens: int, reasoning_effort: Literal["low", "medium", "high"]) -> int:
+        # Helper function to calculate budget_tokens based on the max_tokens.
+        # Ratio for efforts:  Low - 30%, medium - 60%, High: Max token - 1
+        # Note that The minimum budget_tokens is 1,024 tokens so far.
+        # But it may be changed for different models in the future.
+        if reasoning_effort == "low":
+            return int(max_tokens * 0.3)
+        elif reasoning_effort == "medium":
+            return int(max_tokens * 0.6)
+        else:
+            return max_tokens - 1
+
     def _convert_finish_reason(self, finish_reason: str | None) -> str | None:
         """
         Below is a list of finish reason according to OpenAI doc: