diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index e402023d240..90dd7e279d5 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -805,6 +805,10 @@ class CustomStreamWrapper:
                 "function_call" in completion_obj
                 and completion_obj["function_call"] is not None
             )
+            or (
+                "reasoning_content" in completion_obj
+                and completion_obj["reasoning_content"] is not None
+            )
             or (
                 "tool_calls" in model_response.choices[0].delta
                 and model_response.choices[0].delta["tool_calls"] is not None
@@ -1134,7 +1138,10 @@ class CustomStreamWrapper:
             ):
                 if self.received_finish_reason is not None:
                     _chunk_has_content = isinstance(chunk, dict) and (
-                        bool(chunk.get("text", "")) or chunk.get("tool_use") is not None
+                    _chunk_has_content = isinstance(chunk, dict) and (
+                        bool(chunk.get("text", ""))
+                        or chunk.get("tool_use") is not None
+                        or bool(chunk.get("reasoning_content", ""))
                     )
                     if not _chunk_has_content and (
                         not isinstance(chunk, dict)
@@ -1166,6 +1173,14 @@ class CustomStreamWrapper:
                 ):
                     completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]]
 
+                if (
+                    "reasoning_content" in anthropic_response_obj
+                    and anthropic_response_obj["reasoning_content"] is not None
+                ):
+                    completion_obj["reasoning_content"] = anthropic_response_obj[
+                        "reasoning_content"
+                    ]
+
                 if (
                     "provider_specific_fields" in anthropic_response_obj
                     and anthropic_response_obj["provider_specific_fields"] is not None
@@ -2393,16 +2408,22 @@ def convert_generic_chunk_to_model_response_stream(
 ) -> ModelResponseStream:
     from litellm.types.utils import Delta
 
+    delta_kwargs: Dict[str, Any] = {
+        "content": chunk["text"],
+        "tool_calls": chunk.get("tool_use", None),
+    }
+
+    reasoning_content = chunk.get("reasoning_content", None)
+    if reasoning_content is not None:
+        delta_kwargs["reasoning_content"] = reasoning_content
+
     model_response_stream = ModelResponseStream(
         id=str(uuid.uuid4()),
         model="",
         choices=[
             StreamingChoices(
                 index=chunk.get("index", 0),
-                delta=Delta(
-                    content=chunk["text"],
-                    tool_calls=chunk.get("tool_use", None),
-                ),
+                delta=Delta(**delta_kwargs),
             )
         ],
         finish_reason=chunk["finish_reason"] if chunk["is_finished"] else None,
diff --git a/litellm/llms/databricks/streaming_utils.py b/litellm/llms/databricks/streaming_utils.py
index eebe3182881..c6dedcc6827 100644
--- a/litellm/llms/databricks/streaming_utils.py
+++ b/litellm/llms/databricks/streaming_utils.py
@@ -24,10 +24,14 @@ class ModelResponseIterator:
             is_finished = False
             finish_reason = ""
             usage: Optional[ChatCompletionUsageBlock] = None
+            reasoning_content: Optional[str] = None
 
             if processed_chunk.choices[0].delta.content is not None:  # type: ignore
                 text = processed_chunk.choices[0].delta.content  # type: ignore
 
+            if getattr(processed_chunk.choices[0].delta, "reasoning_content", None) is not None:  # type: ignore
+                reasoning_content = processed_chunk.choices[0].delta.reasoning_content  # type: ignore
+
             if (
                 processed_chunk.choices[0].delta.tool_calls is not None  # type: ignore
                 and len(processed_chunk.choices[0].delta.tool_calls) > 0  # type: ignore
@@ -68,6 +72,7 @@ class ModelResponseIterator:
                 finish_reason=finish_reason,
                 usage=usage,
                 index=0,
+                reasoning_content=reasoning_content,
             )
         except json.JSONDecodeError:
             raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index cd5806b3ab7..80c0e3284a0 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -273,6 +273,7 @@ class GenericStreamingChunk(TypedDict, total=False):
     finish_reason: Required[str]
     usage: Required[Optional[ChatCompletionUsageBlock]]
     index: int
+    reasoning_content: Optional[str]
 
     # use this dict if you want to return any provider specific fields in the response
     provider_specific_fields: Optional[Dict[str, Any]]
diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py
index aad3de306c7..10aa4438565 100644
--- a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py
+++ b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py
@@ -192,6 +192,50 @@ def test_is_chunk_non_empty_with_annotations(
     )
 
 
+def test_is_chunk_non_empty_with_reasoning_content_in_completion_obj(
+    initialized_custom_stream_wrapper: CustomStreamWrapper,
+):
+    """Reasoning-only completion_obj chunks must not be dropped before Delta creation."""
+    chunk = {
+        "id": "reasoning-only-chunk",
+        "object": "chat.completion.chunk",
+        "created": 1741037890,
+        "model": "deepseek-reasoner",
+        "choices": [
+            {
+                "index": 0,
+                "delta": {"content": None},
+                "logprobs": None,
+                "finish_reason": None,
+            }
+        ],
+    }
+    assert (
+        initialized_custom_stream_wrapper.is_chunk_non_empty(
+            completion_obj={"content": "", "reasoning_content": "Thinking..."},
+            model_response=ModelResponseStream(**chunk),
+            response_obj={},
+        )
+        is True
+    )
+
+
+def test_return_processed_chunk_logic_keeps_reasoning_only_completion_obj(
+    initialized_custom_stream_wrapper: CustomStreamWrapper,
+):
+    """Reasoning-only chunks should be returned once completion_obj carries reasoning_content."""
+    completion_obj = {"content": "", "reasoning_content": "Thinking..."}
+    returned_chunk = initialized_custom_stream_wrapper.return_processed_chunk_logic(
+        completion_obj=completion_obj,
+        model_response=ModelResponseStream(),
+        response_obj={},
+    )
+
+    assert returned_chunk is not None
+    assert returned_chunk.choices[0].delta.reasoning_content == "Thinking..."
+    assert returned_chunk.choices[0].delta.content == ""
+
+
 def test_optional_combine_thinking_block_in_choices(
     initialized_custom_stream_wrapper: CustomStreamWrapper,
 ):
diff --git a/tests/test_litellm/llms/databricks/test_streaming_utils.py b/tests/test_litellm/llms/databricks/test_streaming_utils.py
new file mode 100644
index 00000000000..d928a42ee50
--- /dev/null
+++ b/tests/test_litellm/llms/databricks/test_streaming_utils.py
@@ -0,0 +1,105 @@
+"""
+Tests for the generic ModelResponseIterator in litellm/llms/databricks/streaming_utils.py.
+
+Verifies that reasoning_content is correctly extracted from streaming chunks,
+fixing the issue where OpenAI-like providers (Watsonx, Cerebras, etc.) lost
+reasoning_content during streaming.
+"""
+
+import os
+import sys
+
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../../../..")
+)  # Adds the parent directory to the system path
+
+from unittest.mock import MagicMock
+
+from litellm.llms.databricks.streaming_utils import ModelResponseIterator
+
+
+class TestModelResponseIteratorReasoningContent:
+    """Test reasoning_content extraction in the generic chunk_parser."""
+
+    def test_chunk_parser_extracts_reasoning_content(self):
+        """Verify reasoning_content is extracted from a streaming chunk delta."""
+        handler = ModelResponseIterator(
+            streaming_response=MagicMock(), sync_stream=True
+        )
+
+        chunk = {
+            "id": "chatcmpl-test-123",
+            "object": "chat.completion.chunk",
+            "created": 1700000000,
+            "model": "gpt-oss-120b",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "reasoning_content": "Let me think about this step by step.",
+                        "content": None,
+                    },
+                    "finish_reason": None,
+                }
+            ],
+        }
+
+        result = handler.chunk_parser(chunk)
+
+        assert result["reasoning_content"] == "Let me think about this step by step."
+        assert result["text"] == ""
+
+    def test_chunk_parser_reasoning_content_none_when_absent(self):
+        """Verify reasoning_content is None when not present in the chunk."""
+        handler = ModelResponseIterator(
+            streaming_response=MagicMock(), sync_stream=True
+        )
+
+        chunk = {
+            "id": "chatcmpl-test-456",
+            "object": "chat.completion.chunk",
+            "created": 1700000000,
+            "model": "gpt-oss-120b",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {"content": "Hello world"},
+                    "finish_reason": None,
+                }
+            ],
+        }
+
+        result = handler.chunk_parser(chunk)
+
+        assert result["reasoning_content"] is None
+        assert result["text"] == "Hello world"
+
+    def test_chunk_parser_both_content_and_reasoning(self):
+        """Verify both text and reasoning_content can be extracted simultaneously."""
+        handler = ModelResponseIterator(
+            streaming_response=MagicMock(), sync_stream=True
+        )
+
+        chunk = {
+            "id": "chatcmpl-test-789",
+            "object": "chat.completion.chunk",
+            "created": 1700000000,
+            "model": "gpt-oss-120b",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "content": "The answer is 42.",
+                        "reasoning_content": "Computing...",
+                    },
+                    "finish_reason": None,
+                }
+            ],
+        }
+
+        result = handler.chunk_parser(chunk)
+
+        assert result["text"] == "The answer is 42."
+        assert result["reasoning_content"] == "Computing..."