diff --git a/pandas-stubs/_typing.pyi b/pandas-stubs/_typing.pyi
index baa358a8a..c4ee25c4b 100644
--- a/pandas-stubs/_typing.pyi
+++ b/pandas-stubs/_typing.pyi
@@ -9,6 +9,7 @@ from collections.abc import (
 )
 import datetime
 from os import PathLike
+import sys
 from typing import (
     Any,
     Literal,
@@ -738,7 +739,20 @@ JsonSeriesOrient: TypeAlias = Literal["split", "records", "index", "table"]
 TimestampConvention: TypeAlias = Literal["start", "end", "s", "e"]
 
 CSVEngine: TypeAlias = Literal["c", "python", "pyarrow", "python-fwf"]
-CSVQuoting: TypeAlias = Literal[0, 1, 2, 3]
+# [pandas-dev/pandas-stubs/991]
+# Ref: https://github.com/python/cpython/blob/5a4fb7ea1c96f67dbb3df5d4ccaf3f66a1e19731/Modules/_csv.c#L88-L91
+# QUOTE_MINIMAL = 0
+# QUOTE_ALL = 1
+# QUOTE_NONNUMERIC = 2
+# QUOTE_NONE = 3
+# Added in 3.12:
+# QUOTE_STRINGS = 4
+# QUOTE_NOTNULL = 5
+CSVQuotingCompat: TypeAlias = Literal[0, 1, 2, 3]
+if sys.version_info < (3, 12):
+    CSVQuoting: TypeAlias = CSVQuotingCompat
+else:
+    CSVQuoting: TypeAlias = CSVQuotingCompat | Literal[4, 5]
 
 HDFCompLib: TypeAlias = Literal["zlib", "lzo", "bzip2", "blosc"]
 ParquetEngine: TypeAlias = Literal["auto", "pyarrow", "fastparquet"]
diff --git a/tests/test_frame.py b/tests/test_frame.py
index 2ff0348c2..7ef2b201d 100644
--- a/tests/test_frame.py
+++ b/tests/test_frame.py
@@ -15,6 +15,7 @@
 import itertools
 from pathlib import Path
 import string
+import sys
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -172,6 +173,13 @@ def test_types_to_csv() -> None:
     # Testing support for binary file handles, added in 1.2.0 https://pandas.pydata.org/docs/whatsnew/v1.2.0.html
     df.to_csv(io.BytesIO(), quoting=csv.QUOTE_ALL, encoding="utf-8", compression="gzip")
 
+    if sys.version_info >= (3, 12):
+        with ensure_clean() as path:
+            df.to_csv(path, quoting=csv.QUOTE_STRINGS)
+
+        with ensure_clean() as path:
+            df.to_csv(path, quoting=csv.QUOTE_NOTNULL)
+
 
 def test_types_to_csv_when_path_passed() -> None:
     df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})