diff --git a/langchain/llms/openai.py b/langchain/llms/openai.py
index f5dbafec..b98a7f5d 100644
--- a/langchain/llms/openai.py
+++ b/langchain/llms/openai.py
@@ -5,11 +5,14 @@ import logging
 import sys
 import warnings
 from typing import (
+    AbstractSet,
     Any,
     Callable,
+    Collection,
     Dict,
     Generator,
     List,
+    Literal,
     Mapping,
     Optional,
     Set,
@@ -150,6 +153,10 @@ class BaseOpenAI(BaseLLM):
     """Maximum number of retries to make when generating."""
     streaming: bool = False
     """Whether to stream the results or not."""
+    allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
+    """Set of special tokens that are allowed。"""
+    disallowed_special: Union[Literal["all"], Collection[str]] = "all"
+    """Set of special tokens that are not allowed。"""
 
     def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]:  # type: ignore
         """Initialize the OpenAI object."""
@@ -449,7 +456,11 @@ class BaseOpenAI(BaseLLM):
 
         enc = tiktoken.encoding_for_model(self.model_name)
 
-        tokenized_text = enc.encode(text)
+        tokenized_text = enc.encode(
+            text,
+            allowed_special=self.allowed_special,
+            disallowed_special=self.disallowed_special,
+        )
 
         # calculate the number of tokens in the encoded text
         return len(tokenized_text)
@@ -602,6 +613,10 @@ class OpenAIChat(BaseLLM):
     """Series of messages for Chat input."""
     streaming: bool = False
     """Whether to stream the results or not."""
+    allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
+    """Set of special tokens that are allowed。"""
+    disallowed_special: Union[Literal["all"], Collection[str]] = "all"
+    """Set of special tokens that are not allowed。"""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -785,7 +800,11 @@ class OpenAIChat(BaseLLM):
         enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
 
         # encode the text using the GPT-3.5-Turbo encoder
-        tokenized_text = enc.encode(text)
+        tokenized_text = enc.encode(
+            text,
+            allowed_special=self.allowed_special,
+            disallowed_special=self.disallowed_special,
+        )
 
         # calculate the number of tokens in the encoded text
         return len(tokenized_text)