fix(health): pass responses-mode `input` as a list

When ``/health/test_connection`` hits a ``mode: responses`` model, the
handler called ``litellm.aresponses(input=prompt or 'test')`` — a bare
string. OpenAI's public Responses API is tolerant of either shape, but
the ChatGPT/Codex backend enforces a list and returns
``{"detail": "Input must be a list"}``, so the "Test Connection" button
on the models list page failed against ChatGPT OAuth models even though
normal inference worked fine.

Wrap the fallback in a list (``input or [prompt or 'test']``) so both
backends are happy. The calling site in ``main.ahealth_check`` already
forwards ``input=['test from litellm']`` for this mode, so the common
path flows through unchanged.

Regression test mocks ``litellm.aresponses`` and asserts the handler
passes a list for both the ``input=[...]`` and prompt-only cases.

litellm/litellm_core_utils/health_check_helpers.py

@@ -206,8 +206,11 @@ class HealthCheckHelpers:
                 filtered_model_params=_filter_model_params(model_params=model_params),
             ),
             "responses": lambda: litellm.aresponses(
+                # The ChatGPT/Codex backend rejects string input with
+                # ``{"detail": "Input must be a list"}``; OpenAI's own
+                # Responses API accepts either, so a list works for both.
                 **_filter_model_params(model_params=model_params),
-                input=prompt or "test",
+                input=input or [prompt or "test"],
             ),
             "ocr": lambda: litellm.aocr(
                 **_filter_model_params(model_params=model_params),

tests/test_litellm/litellm_core_utils/test_health_check_helpers.py

@@ -134,4 +134,48 @@ async def test_ahealth_check_failure_masks_raw_request_headers():
     if "Content-Type" in headers:
         assert headers["Content-Type"] == "application/json"
     
-    print(f"Masked Authorization header: {headers.get('Authorization', 'NOT FOUND')}") 
+    print(f"Masked Authorization header: {headers.get('Authorization', 'NOT FOUND')}")
+
+
+def test_get_mode_handlers_responses_wraps_input_in_list():
+    """
+    Regression: the ChatGPT/Codex responses backend rejects string input
+    with ``{"detail": "Input must be a list"}``. OpenAI's public Responses
+    API accepts either shape, so we always pass a list from the health
+    check to keep both backends happy.
+
+    See ``litellm/proxy/health_endpoints/_health_endpoints.py`` —
+    ``test_connection`` calls ``ahealth_check`` with
+    ``input=["test from litellm"]``; that must flow through unchanged,
+    and the ``prompt``-only fallback must still produce a list.
+    """
+    import asyncio
+
+    handlers = HealthCheckHelpers.get_mode_handlers(
+        model="chatgpt/gpt-5.4-codex",
+        custom_llm_provider="chatgpt",
+        model_params={"model": "chatgpt/gpt-5.4-codex"},
+        prompt="test from litellm",
+        input=["test from litellm"],
+    )
+    assert "responses" in handlers
+
+    with patch(
+        "litellm.aresponses", new=AsyncMock(return_value=MagicMock())
+    ) as mock_ar:
+        asyncio.get_event_loop().run_until_complete(handlers["responses"]())
+        assert mock_ar.call_args.kwargs["input"] == ["test from litellm"]
+
+    # Fallback: no ``input`` provided, prompt-only → still a list.
+    prompt_only_handlers = HealthCheckHelpers.get_mode_handlers(
+        model="chatgpt/gpt-5.4-codex",
+        custom_llm_provider="chatgpt",
+        model_params={"model": "chatgpt/gpt-5.4-codex"},
+        prompt="hello",
+        input=None,
+    )
+    with patch(
+        "litellm.aresponses", new=AsyncMock(return_value=MagicMock())
+    ) as mock_ar:
+        asyncio.get_event_loop().run_until_complete(prompt_only_handlers["responses"]())
+        assert mock_ar.call_args.kwargs["input"] == ["hello"]