fix(databricks): keep top-level reasoning_content from OpenAI-compatible gateway models

The Databricks chat transformation only parsed reasoning out of FMAPI-style
reasoning content blocks, so external models behind Databricks AI Gateway that
return the OpenAI-style top-level reasoning_content string lost it, both in the
final message and in every streamed delta. Fall back to the shared OpenAI
reasoning helper when no reasoning block exists, and keep the delta's own
reasoning_content when streaming.

litellm/llms/databricks/chat/transformation.py

@@ -15,6 +15,7 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo
     _should_convert_tool_call_to_json_mode,
 )
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    _extract_reasoning_content,  # pyright: ignore[reportPrivateUsage]  # same import as the OpenAI transformation
     strip_litellm_internal_message_fields,
     strip_name_from_message,
 )
@@ -23,7 +24,9 @@ from litellm.types.llms.anthropic import AllAnthropicToolsValues
 from litellm.types.llms.databricks import (
     AllDatabricksContentValues,
     DatabricksChoice,
+    DatabricksDelta,
     DatabricksFunction,
+    DatabricksMessage,
     DatabricksResponse,
     DatabricksTool,
 )
@@ -534,6 +537,19 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
                             thinking_blocks.append(thinking_block)
         return reasoning_content, thinking_blocks
 
+    @staticmethod
+    def extract_top_level_reasoning_content(delta: DatabricksDelta) -> str | None:
+        return delta.get("reasoning_content")
+
+    @staticmethod
+    def resolve_reasoning_and_content(
+        message: DatabricksMessage, block_reasoning_content: str | None
+    ) -> tuple[str | None, str | None]:
+        content_str: Final = DatabricksConfig.extract_content_str(message["content"])
+        if block_reasoning_content is not None:
+            return block_reasoning_content, content_str
+        return _extract_reasoning_content({**message, "content": content_str})
+
     @staticmethod
     def extract_citations(
         content: AllDatabricksContentValues | None,
@@ -577,14 +593,13 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
                     finish_reason = "stop"
 
             if translated_message is None:
-                ## get the content str
-                content_str = DatabricksConfig.extract_content_str(choice["message"]["content"])
-
-                ## get the reasoning content
                 (
-                    reasoning_content,
+                    block_reasoning_content,
                     thinking_blocks,
                 ) = DatabricksConfig.extract_reasoning_content(choice["message"].get("content"))
+                reasoning_content, content_str = DatabricksConfig.resolve_reasoning_and_content(
+                    choice["message"], block_reasoning_content
+                )
 
                 citations = DatabricksConfig.extract_citations(choice["message"].get("content"))
 
@@ -738,12 +753,16 @@ class DatabricksChatResponseIterator(BaseModelResponseIterator):
 
                 # extract the reasoning content
                 (
-                    reasoning_content,
+                    block_reasoning_content,
                     thinking_blocks,
                 ) = DatabricksConfig.extract_reasoning_content(choice["delta"].get("content"))
 
                 choice["delta"]["content"] = content_str
-                choice["delta"]["reasoning_content"] = reasoning_content
+                choice["delta"]["reasoning_content"] = (
+                    block_reasoning_content
+                    if block_reasoning_content is not None
+                    else DatabricksConfig.extract_top_level_reasoning_content(choice["delta"])
+                )
                 choice["delta"]["thinking_blocks"] = thinking_blocks
                 translated_choices.append(choice)
             return ModelResponseStream(

litellm/types/llms/databricks.py

@@ -2,6 +2,7 @@ from typing import Any, Literal
 
 from pydantic import BaseModel
 from typing_extensions import (
+    ReadOnly,
     Required,
     TypedDict,
 )
@@ -57,6 +58,14 @@ class DatabricksMessage(TypedDict, total=False):
     role: Required[str]
     content: Required[AllDatabricksContentValues]
     tool_calls: list[DatabricksTool] | None
+    reasoning_content: ReadOnly[str | None]
+    reasoning: ReadOnly[str | None]
+
+
+class DatabricksDelta(TypedDict, total=False):
+    role: ReadOnly[str]
+    content: ReadOnly[AllDatabricksContentValues | None]
+    reasoning_content: ReadOnly[str | None]
 
 
 class DatabricksChoice(TypedDict, total=False):

tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py

@@ -590,3 +590,86 @@ def test_chunk_parser_without_usage_still_parses_content():
     assert result.id == "chatcmpl-test"
     assert result.model == "databricks-claude-sonnet-5"
     assert result.choices[0]["delta"]["content"] == "hi"
+
+
+@pytest.mark.parametrize("reasoning_key", ["reasoning_content", "reasoning"])
+def test_transform_choices_surfaces_top_level_reasoning_content(reasoning_key: str) -> None:
+    config = DatabricksConfig()
+    databricks_choices = [
+        {
+            "message": {
+                "role": "assistant",
+                "content": "391",
+                reasoning_key: "We need answer just number. 17*23=391.",
+            },
+            "index": 0,
+            "finish_reason": "stop",
+        }
+    ]
+
+    choices = config._transform_dbrx_choices(choices=databricks_choices)
+
+    assert choices[0].message.content == "391"
+    assert choices[0].message.reasoning_content == "We need answer just number. 17*23=391."
+    assert getattr(choices[0].message, "thinking_blocks", None) is None
+
+
+def test_transform_choices_parses_think_tags_in_string_content():
+    config = DatabricksConfig()
+    databricks_choices = [
+        {
+            "message": {"role": "assistant", "content": "<think>17 times 23</think>391"},
+            "index": 0,
+            "finish_reason": "stop",
+        }
+    ]
+
+    choices = config._transform_dbrx_choices(choices=databricks_choices)
+
+    assert choices[0].message.content == "391"
+    assert choices[0].message.reasoning_content == "17 times 23"
+
+
+def test_transform_choices_prefers_reasoning_blocks_over_top_level_field():
+    config = DatabricksConfig()
+    databricks_choices = [
+        {
+            "message": {
+                "role": "assistant",
+                "content": [
+                    {"type": "reasoning", "summary": [{"type": "summary_text", "text": "from block"}]},
+                    {"type": "text", "text": "391"},
+                ],
+                "reasoning_content": "from field",
+            },
+            "index": 0,
+            "finish_reason": "stop",
+        }
+    ]
+
+    choices = config._transform_dbrx_choices(choices=databricks_choices)
+
+    assert choices[0].message.reasoning_content == "from block"
+    assert choices[0].message.content == "391"
+
+
+def test_chunk_parser_surfaces_top_level_reasoning_delta():
+    iterator = DatabricksChatResponseIterator(None, sync_stream=True)
+    chunk = {
+        "id": "1",
+        "object": "chat.completion.chunk",
+        "created": 0,
+        "model": "lit-qa-deepseek-v4-flash",
+        "choices": [
+            {
+                "delta": {"role": "assistant", "content": "", "reasoning_content": "We need answer"},
+                "index": 0,
+                "finish_reason": None,
+            }
+        ],
+    }
+
+    parsed = iterator.chunk_parser(chunk)
+
+    assert parsed.choices[0].delta.reasoning_content == "We need answer"
+    assert parsed.choices[0].delta.content == ""