diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
index c87357122ce..fd29c4c0001 100644
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -775,7 +775,7 @@ class ChunkProcessor:
                 content = delta.get(delta_key, "")
                 if content is None:
                     continue  # openai v1.0.0 sets content = None for chunks
-                content_list.append(content)
+                content_list.append(delta_content_as_str(content))
 
         # Combine the "content" strings into a single string || combine the 'function' strings into a single string
         combined_content: Final = "".join(content_list)
@@ -1583,12 +1583,33 @@ def _delta_carries_more_than_text(delta: Mapping[str, object]) -> bool:
     return any(delta.get(field) is not None for field in _NON_TEXT_DELTA_FIELDS)
 
 
+def delta_content_as_str(content: object) -> str:
+    """Coerce a streamed delta content value to plain text.
+
+    OpenAI-compatible providers are supposed to stream delta.content as a
+    string, but some stream a list of content parts instead. Join the text
+    parts into one string and skip every part that carries no usable text, so
+    the running answer text neither loses the content nor raises when a part
+    holds a null value or is not a mapping at all.
+    """
+    if isinstance(content, str):
+        return content
+    if not isinstance(content, list):
+        return ""
+    texts: Final[list[str]] = []  # mutable-ok: accumulator of streamed delta content text parts
+    for part in content:  # pyright: ignore[reportUnknownVariableType]  # delta content parts are dynamically typed
+        text = part.get("text") if isinstance(part, Mapping) else getattr(part, "text", None)  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType, reportUnknownArgumentType]  # dynamic delta parts
+        if isinstance(text, str):  # pyright: ignore[reportUnknownArgumentType]  # dynamic text value
+            texts.append(text)  # pyright: ignore[reportUnknownArgumentType]  # dynamic text value
+    return "".join(texts)
+
+
 def _simple_text_part(choices: Sequence[object]) -> str | None:
     deltas: Final = tuple(_stream_choice_delta(choice) for choice in choices)
     if any(_delta_carries_more_than_text(delta) for delta in deltas):
         return None
     content: Final = deltas[0].get("content")
-    return content if isinstance(content, str) else ""
+    return delta_content_as_str(content)
 
 
 _NO_TRACE_FIELDS: Final[Mapping[str, object]] = MappingProxyType({})
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 6839e11f8c7..4e0217e09fc 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -1102,9 +1102,17 @@ class CustomStreamWrapper:
         model_response: ModelResponseStream,
         response_obj: Mapping[str, object],
     ) -> bool:
+        from litellm.litellm_core_utils.streaming_chunk_builder_utils import (
+            delta_content_as_str,
+        )
+
+        # Providers may stream delta.content as a list of content parts; fold it to
+        # text here too, otherwise every chunk after the first is judged empty and
+        # dropped (only the first survives, via the role clause below).
+        content_str: Final = delta_content_as_str(completion_obj.get("content"))
         if (
             "content" in completion_obj
-            and (isinstance(completion_obj["content"], str) and len(completion_obj["content"]) > 0)
+            and len(content_str) > 0
             or (
                 "tool_calls" in completion_obj
                 and completion_obj["tool_calls"] is not None
@@ -2840,9 +2848,20 @@ class CustomStreamWrapper:
         return None if is_model_response_stream_empty(model_response=usage_free_chunk) else usage_free_chunk
 
     def _accumulate_streamed_delta(self, delta: object) -> None:
+        from litellm.litellm_core_utils.streaming_chunk_builder_utils import (
+            delta_content_as_str,
+        )
+
         get: Final = getattr(delta, "get", None)
-        content: Final = get("content") if callable(get) else None
-        self.response_uptil_now += content if isinstance(content, str) else ""
+        content: Final = get("content") if callable(get) else getattr(delta, "content", None)
+        if isinstance(content, str):
+            self.response_uptil_now += content
+        else:
+            # Providers that stream delta.content as a list of content parts must
+            # still accumulate as plain text. delta_content_as_str folds those
+            # parts and yields "" for anything that is not a content list, which is
+            # exactly what a non-text delta contributed before.
+            self.response_uptil_now += delta_content_as_str(content)
         if not self._emitted_disqualifying_content and self._delta_disqualifies_continuation(delta):
             self._emitted_disqualifying_content = True
 
diff --git a/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
index 527c83d5760..289e24905b5 100644
--- a/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
@@ -2522,6 +2522,71 @@ def test_stream_chunk_builder_rebuilds_each_text_completion_choice_by_index() ->
     assert (response.usage.prompt_tokens, response.usage.completion_tokens) == (1, 4)
 
 
+def test_stream_chunk_builder_joins_content_part_list_delta():
+    """A provider streaming delta.content as a content-part list must aggregate as text."""
+    chunks = [
+        ModelResponseStream(
+            id="chatcmpl-123",
+            created=1,
+            model="gpt-4.1-mini",
+            object="chat.completion.chunk",
+            choices=[
+                StreamingChoices(
+                    finish_reason=None,
+                    index=0,
+                    delta=Delta(content=[{"type": "text", "text": "Hello "}], role="assistant"),
+                )
+            ],
+        ),
+        ModelResponseStream(
+            id="chatcmpl-123",
+            created=2,
+            model="gpt-4.1-mini",
+            object="chat.completion.chunk",
+            choices=[
+                StreamingChoices(
+                    finish_reason="stop",
+                    index=0,
+                    delta=Delta(content=[{"type": "text", "text": "world"}], role=None),
+                )
+            ],
+        ),
+    ]
+
+    response = stream_chunk_builder(chunks=chunks)
+    assert response is not None
+    assert response.choices[0].message.content == "Hello world"
+
+
+def test_delta_content_as_str_skips_parts_without_usable_text():
+    """Parts holding a null value or not being mappings must not abort the join."""
+    from litellm.litellm_core_utils.streaming_chunk_builder_utils import delta_content_as_str
+
+    assert delta_content_as_str([{"type": "text", "text": "a"}, {"type": "text", "text": "b"}]) == "ab"
+    assert delta_content_as_str([{"type": "text", "text": None}]) == ""
+    assert delta_content_as_str(["bare string", 42, None]) == ""
+    assert (
+        delta_content_as_str(
+            [
+                {"type": "text", "text": "a"},
+                {"type": "text", "text": None},
+                {"type": "text", "text": "b"},
+            ]
+        )
+        == "ab"
+    )
+
+
+def test_delta_content_as_str_reads_text_from_non_mapping_parts():
+    """A part exposing only a ``text`` attribute still contributes its text."""
+    from litellm.litellm_core_utils.streaming_chunk_builder_utils import delta_content_as_str
+
+    class _TextPart:
+        text = "from attribute"
+
+    assert delta_content_as_str([_TextPart()]) == "from attribute"
+
+
 def test_stream_chunk_builder_keeps_null_text_completion_logprobs_fields_null() -> None:
     response: Final = litellm.stream_chunk_builder(
         chunks=[
diff --git a/tests/unit/litellm_core_utils/test_streaming_handler.py b/tests/unit/litellm_core_utils/test_streaming_handler.py
index 94fd915844a..857d3d2cbb1 100644
--- a/tests/unit/litellm_core_utils/test_streaming_handler.py
+++ b/tests/unit/litellm_core_utils/test_streaming_handler.py
@@ -9618,3 +9618,66 @@ def test_chunk_creator_usage_only_chunk_with_dict_usage_keeps_details(
     assert usage.prompt_tokens_details.cached_tokens == 80
     assert usage.completion_tokens_details is not None
     assert usage.completion_tokens_details.reasoning_tokens == 5
+
+
+def _chunk_with_list_content(text: str, chunk_id: str, created: int) -> ModelResponseStream:
+    return ModelResponseStream(
+        id=chunk_id,
+        object="chat.completion.chunk",
+        created=created,
+        model="gpt-5.6",
+        choices=[
+            StreamingChoices(
+                index=0,
+                delta=Delta(role="assistant", content=[{"type": "text", "text": text}]),
+                finish_reason=None,
+            )
+        ],
+    )
+
+def test_streaming_delta_content_list_accumulates_as_str():
+    """A provider streaming delta.content as a content-part list must still accumulate as text."""
+    chunks: Final = [
+        _chunk_with_list_content("hello ", "chunk1", 1741037890),
+        _chunk_with_list_content("world", "chunk2", 1741037891),
+    ]
+
+    response: Final = CustomStreamWrapper(
+        completion_stream=iter(chunks),
+        model="gpt-5.6",
+        logging_obj=MagicMock(),
+        custom_llm_provider="openai",
+    )
+
+    for _ in response:
+        pass
+
+    assert response.response_uptil_now == "hello world"
+
+def test_streaming_accumulates_content_part_with_null_text():
+    """A content part carrying a null value must not abort the accumulated stream."""
+    chunk: Final = ModelResponseStream(
+        id="chunk-null",
+        object="chat.completion.chunk",
+        created=1741037892,
+        model="gpt-5.6",
+        choices=[
+            StreamingChoices(
+                index=0,
+                delta=Delta(role="assistant", content=[{"type": "text", "text": None}]),
+                finish_reason=None,
+            )
+        ],
+    )
+
+    response: Final = CustomStreamWrapper(
+        completion_stream=iter([chunk]),
+        model="gpt-5.6",
+        logging_obj=MagicMock(),
+        custom_llm_provider="openai",
+    )
+
+    for _ in response:
+        pass
+
+    assert response.response_uptil_now == ""