diff --git a/libs/standard-tests/langchain_tests/integration_tests/chat_models.py b/libs/standard-tests/langchain_tests/integration_tests/chat_models.py
index d1c3ab3e6e3d3..6fef3158930a7 100644
--- a/libs/standard-tests/langchain_tests/integration_tests/chat_models.py
+++ b/libs/standard-tests/langchain_tests/integration_tests/chat_models.py
@@ -1,6 +1,6 @@
 import base64
 import json
-from typing import List, Optional, cast
+from typing import Any, List, Literal, Optional, cast
 
 import httpx
 import pytest
@@ -29,7 +29,9 @@
 from langchain_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION
 
 
-def _get_joke_class() -> type[BaseModel]:
+def _get_joke_class(
+    schema_type: Literal["pydantic", "typeddict", "json_schema"],
+) -> Any:
     """
     :private:
     """
@@ -40,7 +42,28 @@ class Joke(BaseModel):
         setup: str = Field(description="question to set up a joke")
         punchline: str = Field(description="answer to resolve the joke")
 
-    return Joke
+    def validate_joke(result: Any) -> bool:
+        return isinstance(result, Joke)
+
+    class JokeDict(TypedDict):
+        """Joke to tell user."""
+
+        setup: Annotated[str, ..., "question to set up a joke"]
+        punchline: Annotated[str, ..., "answer to resolve the joke"]
+
+    def validate_joke_dict(result: Any) -> bool:
+        return all(key in ["setup", "punchline"] for key in result.keys())
+
+    if schema_type == "pydantic":
+        return Joke, validate_joke
+
+    elif schema_type == "typeddict":
+        return JokeDict, validate_joke_dict
+
+    elif schema_type == "json_schema":
+        return Joke.model_json_schema(), validate_joke_dict
+    else:
+        raise ValueError("Invalid schema type")
 
 
 class _MagicFunctionSchema(BaseModel):
@@ -1151,7 +1174,8 @@ def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
         assert tool_call["args"].get("answer_style")
         assert tool_call["type"] == "tool_call"
 
-    def test_structured_output(self, model: BaseChatModel) -> None:
+    @pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"])
+    def test_structured_output(self, model: BaseChatModel, schema_type: str) -> None:
         """Test to verify structured output is generated both on invoke and stream.
 
         This test is optional and should be skipped if the model does not support
@@ -1181,29 +1205,19 @@ def has_tool_calling(self) -> bool:
         if not self.has_tool_calling:
             pytest.skip("Test requires tool calling.")
 
-        Joke = _get_joke_class()
-        # Pydantic class
-        chat = model.with_structured_output(Joke, **self.structured_output_kwargs)
+        schema, validation_function = _get_joke_class(schema_type)  # type: ignore[arg-type]
+        chat = model.with_structured_output(schema, **self.structured_output_kwargs)
         result = chat.invoke("Tell me a joke about cats.")
-        assert isinstance(result, Joke)
+        validation_function(result)
 
         for chunk in chat.stream("Tell me a joke about cats."):
-            assert isinstance(chunk, Joke)
+            validation_function(chunk)
+        assert chunk
 
-        # Schema
-        chat = model.with_structured_output(
-            Joke.model_json_schema(), **self.structured_output_kwargs
-        )
-        result = chat.invoke("Tell me a joke about cats.")
-        assert isinstance(result, dict)
-        assert set(result.keys()) == {"setup", "punchline"}
-
-        for chunk in chat.stream("Tell me a joke about cats."):
-            assert isinstance(chunk, dict)
-        assert isinstance(chunk, dict)  # for mypy
-        assert set(chunk.keys()) == {"setup", "punchline"}
-
-    async def test_structured_output_async(self, model: BaseChatModel) -> None:
+    @pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"])
+    async def test_structured_output_async(
+        self, model: BaseChatModel, schema_type: str
+    ) -> None:
         """Test to verify structured output is generated both on invoke and stream.
 
         This test is optional and should be skipped if the model does not support
@@ -1233,28 +1247,14 @@ def has_tool_calling(self) -> bool:
         if not self.has_tool_calling:
             pytest.skip("Test requires tool calling.")
 
-        Joke = _get_joke_class()
-
-        # Pydantic class
-        chat = model.with_structured_output(Joke, **self.structured_output_kwargs)
+        schema, validation_function = _get_joke_class(schema_type)  # type: ignore[arg-type]
+        chat = model.with_structured_output(schema, **self.structured_output_kwargs)
         result = await chat.ainvoke("Tell me a joke about cats.")
-        assert isinstance(result, Joke)
+        validation_function(result)
 
         async for chunk in chat.astream("Tell me a joke about cats."):
-            assert isinstance(chunk, Joke)
-
-        # Schema
-        chat = model.with_structured_output(
-            Joke.model_json_schema(), **self.structured_output_kwargs
-        )
-        result = await chat.ainvoke("Tell me a joke about cats.")
-        assert isinstance(result, dict)
-        assert set(result.keys()) == {"setup", "punchline"}
-
-        async for chunk in chat.astream("Tell me a joke about cats."):
-            assert isinstance(chunk, dict)
-        assert isinstance(chunk, dict)  # for mypy
-        assert set(chunk.keys()) == {"setup", "punchline"}
+            validation_function(chunk)
+        assert chunk
 
     @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Test requires pydantic 2.")
     def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None: