diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py b/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py
index 62324d79575..e9321d9c1c8 100644
--- a/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py
@@ -1,5 +1,6 @@
 """Test Responses API usage."""
 
+import base64
 import json
 import os
 from typing import Annotated, Any, Literal, cast
@@ -1200,3 +1201,69 @@ def test_compaction_streaming(output_version: Literal["responses/v1", "v1"]) ->
     messages.append(third_message)
     third_response = llm.invoke(messages)
     assert third_response.text
+
+
+def test_csv_input() -> None:
+    """Test CSV file input with both LangChain standard and OpenAI native formats."""
+    # Create sample CSV content
+    csv_content = (
+        "name,age,city\nAlice,30,New York\nBob,25,Los Angeles\nCarol,35,Chicago"
+    )
+    csv_bytes = csv_content.encode("utf-8")
+    base64_string = base64.b64encode(csv_bytes).decode("utf-8")
+
+    llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
+
+    # Test LangChain standard format
+    langchain_message = {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "How many people are in this CSV file?",
+            },
+            {
+                "type": "file",
+                "base64": base64_string,
+                "mime_type": "text/csv",
+                "filename": "people.csv",
+            },
+        ],
+    }
+    payload = llm._get_request_payload([langchain_message])
+    block = payload["input"][0]["content"][1]
+    assert block["type"] == "input_file"
+
+    response = llm.invoke([langchain_message])
+    assert isinstance(response, AIMessage)
+    assert response.content
+    assert (
+        "3" in str(response.content).lower() or "three" in str(response.content).lower()
+    )
+
+    # Test OpenAI native format
+    openai_message = {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "How many people are in this CSV file?",
+            },
+            {
+                "type": "input_file",
+                "filename": "people.csv",
+                "file_data": f"data:text/csv;base64,{base64_string}",
+            },
+        ],
+    }
+    payload2 = llm._get_request_payload([openai_message])
+    block2 = payload2["input"][0]["content"][1]
+    assert block2["type"] == "input_file"
+
+    response2 = llm.invoke([openai_message])
+    assert isinstance(response2, AIMessage)
+    assert response2.content
+    assert (
+        "3" in str(response2.content).lower()
+        or "three" in str(response2.content).lower()
+    )
diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_responses_standard.py b/libs/partners/openai/tests/integration_tests/chat_models/test_responses_standard.py
index 532f0717901..a2b63e1ab8c 100644
--- a/libs/partners/openai/tests/integration_tests/chat_models/test_responses_standard.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_responses_standard.py
@@ -59,7 +59,7 @@ class TestOpenAIResponses(TestOpenAIStandard):
         """Test that the model can process PDF inputs."""
         super().test_openai_pdf_inputs(model)
         # Responses API additionally supports files via URL
-        url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
+        url = "https://www.berkshirehathaway.com/letters/2024ltr.pdf"
 
         message = HumanMessage(
             [