diff --git a/libs/partners/deepseek/tests/integration_tests/test_chat_models.py b/libs/partners/deepseek/tests/integration_tests/test_chat_models.py
new file mode 100644
index 00000000000..43dc2de4047
--- /dev/null
+++ b/libs/partners/deepseek/tests/integration_tests/test_chat_models.py
@@ -0,0 +1,56 @@
+"""Test ChatDeepSeek chat model."""
+
+from typing import Optional, Type
+
+import pytest
+from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessageChunk, BaseMessageChunk
+from langchain_core.tools import BaseTool
+from langchain_tests.integration_tests import ChatModelIntegrationTests
+
+from langchain_deepseek.chat_models import ChatDeepSeek
+
+
+class TestChatDeepSeek(ChatModelIntegrationTests):
+    @property
+    def chat_model_class(self) -> Type[ChatDeepSeek]:
+        return ChatDeepSeek
+
+    @property
+    def chat_model_params(self) -> dict:
+        # These should be parameters used to initialize your integration for testing
+        return {
+            "model": "deepseek-chat",
+            "temperature": 0,
+        }
+
+    @property
+    def supports_json_mode(self) -> bool:
+        """(bool) whether the chat model supports JSON mode."""
+        return True
+
+    @pytest.mark.xfail(reason="Not yet supported.")
+    def test_tool_message_histories_list_content(
+        self, model: BaseChatModel, my_adder_tool: BaseTool
+    ) -> None:
+        super().test_tool_message_histories_list_content(model, my_adder_tool)
+
+
+@pytest.mark.xfail(reason="Takes > 30s to run.")
+def test_reasoning_content() -> None:
+    """Test reasoning content."""
+    chat_model = ChatDeepSeek(model="deepseek-reasoner")
+    response = chat_model.invoke("What is 3^3?")
+    assert response.content
+    assert response.additional_kwargs["reasoning_content"]
+    raise ValueError()
+
+
+@pytest.mark.xfail(reason="Takes > 30s to run.")
+def test_reasoning_content_streaming() -> None:
+    chat_model = ChatDeepSeek(model="deepseek-reasoner")
+    full: Optional[BaseMessageChunk] = None
+    for chunk in chat_model.stream("What is 3^3?"):
+        full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessageChunk)
+    assert full.additional_kwargs["reasoning_content"]