diff --git a/litellm/llms/minimax/chat/transformation.py b/litellm/llms/minimax/chat/transformation.py
index c5aa8811f02..9b651bef699 100644
--- a/litellm/llms/minimax/chat/transformation.py
+++ b/litellm/llms/minimax/chat/transformation.py
@@ -2,12 +2,26 @@
 MiniMax OpenAI transformation config - extends OpenAI chat config for MiniMax's OpenAI-compatible API
 """
 
-from typing import Final
+from typing import (
+    TYPE_CHECKING,
+    Any,  # noqa: TID251  # LiteLLMLoggingObj has no concrete public type; matches OpenAIGPTConfig's own alias
+    Final,
+)
+
+import httpx
 
 import litellm
 from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
+from litellm.types.utils import ModelResponse
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
 
 
 class MinimaxChatConfig(OpenAIGPTConfig):
@@ -96,3 +110,51 @@ class MinimaxChatConfig(OpenAIGPTConfig):
             pass
 
         return base_params + additional_params
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,  # mutable-ok: matches parent  # pyright: ignore[reportMissingTypeArgument,reportUnknownParameterType]  # matches OpenAIGPTConfig.transform_response
+        messages: list[AllMessageValues],  # mutable-ok: matches parent
+        optional_params: dict,  # mutable-ok: matches parent  # pyright: ignore[reportMissingTypeArgument,reportUnknownParameterType]  # matches OpenAIGPTConfig.transform_response
+        litellm_params: dict,  # mutable-ok: matches parent  # pyright: ignore[reportMissingTypeArgument,reportUnknownParameterType]  # matches OpenAIGPTConfig.transform_response
+        encoding,  # pyright: ignore[reportAny,reportMissingParameterType]  # matches OpenAIGPTConfig.transform_response
+        api_key: str | None = None,
+        json_mode: bool | None = None,
+    ) -> ModelResponse:
+        """
+        MiniMax M2.7 (reasoning_split unset/false, the default) can return
+        its entire answer inside <think>...</think> with nothing trailing
+        after the closing tag. The shared parser leaves `content` empty in
+        that case, discarding the model's only real output.
+
+        Scoped to MiniMax only: for other providers using <think> tags,
+        content left empty after the tag is genuinely empty output, not a
+        signal to promote reasoning_content into the visible channel —
+        doing that generically risks leaking hidden reasoning for
+        adversarial prompts that end right after </think>. MiniMax's docs
+        confirm the whole-answer-in-<think> shape is expected behavior
+        for this provider specifically.
+        """
+        response = super().transform_response(  # pyright: ignore[reportUnknownMemberType]  # super() inherits partially unknown param types from parent
+            model=model,
+            raw_response=raw_response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            request_data=request_data,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            encoding=encoding,
+            api_key=api_key,
+            json_mode=json_mode,
+        )
+        for choice in response.choices:
+            message = choice.message
+            reasoning_content = getattr(message, "reasoning_content", None)  # pyright: ignore[reportAny]  # Message deletes reasoning_content when None
+            if reasoning_content and not (message.content or "").strip():
+                message.content = reasoning_content
+        return response
diff --git a/tests/llm_translation/test_minimax_transformation.py b/tests/llm_translation/test_minimax_transformation.py
new file mode 100644
index 00000000000..ad3a0e2c94e
--- /dev/null
+++ b/tests/llm_translation/test_minimax_transformation.py
@@ -0,0 +1,87 @@
+"""
+Regression test for #38197: MiniMax M2.7 can return its entire answer
+inside <think>...</think> with nothing trailing after the closing tag.
+MinimaxChatConfig.transform_response() should fall back to
+reasoning_content in that case instead of leaving content empty.
+
+Scoped to MiniMax only — see the docstring on transform_response for why
+this isn't in the shared _parse_content_for_reasoning function.
+"""
+from unittest.mock import MagicMock
+
+from litellm.llms.minimax.chat.transformation import MinimaxChatConfig
+
+
+class TestMinimaxTransformResponse:
+    def test_empty_content_falls_back_to_reasoning_content(self):
+        config = MinimaxChatConfig()
+
+        fake_message = MagicMock()
+        fake_message.content = ""
+        fake_message.reasoning_content = "The answer to 2+2 is 4."
+
+        fake_choice = MagicMock()
+        fake_choice.message = fake_message
+
+        fake_model_response = MagicMock()
+        fake_model_response.choices = [fake_choice]
+
+        import litellm.llms.openai.chat.gpt_transformation as parent_module
+
+        original = parent_module.OpenAIGPTConfig.transform_response
+        parent_module.OpenAIGPTConfig.transform_response = (
+            lambda self, **kwargs: fake_model_response
+        )
+
+        try:
+            result = config.transform_response(
+                model="minimax/MiniMax-M2.7",
+                raw_response=None,
+                model_response=fake_model_response,
+                logging_obj=None,
+                request_data={},
+                messages=[],
+                optional_params={},
+                litellm_params={},
+                encoding=None,
+            )
+            assert result.choices[0].message.content == "The answer to 2+2 is 4."
+        finally:
+            parent_module.OpenAIGPTConfig.transform_response = original
+
+    def test_normal_content_left_untouched(self):
+        """Sanity check: when content is already populated, don't overwrite it."""
+        config = MinimaxChatConfig()
+
+        fake_message = MagicMock()
+        fake_message.content = "The answer is 4."
+        fake_message.reasoning_content = "Let me think about this."
+
+        fake_choice = MagicMock()
+        fake_choice.message = fake_message
+
+        fake_model_response = MagicMock()
+        fake_model_response.choices = [fake_choice]
+
+        import litellm.llms.openai.chat.gpt_transformation as parent_module
+
+        original = parent_module.OpenAIGPTConfig.transform_response
+        parent_module.OpenAIGPTConfig.transform_response = (
+            lambda self, **kwargs: fake_model_response
+        )
+
+        try:
+            result = config.transform_response(
+                model="minimax/MiniMax-M2.7",
+                raw_response=None,
+                model_response=fake_model_response,
+                logging_obj=None,
+                request_data={},
+                messages=[],
+                optional_params={},
+                litellm_params={},
+                encoding=None,
+            )
+            assert result.choices[0].message.content == "The answer is 4."
+        finally:
+            parent_module.OpenAIGPTConfig.transform_response = original
diff --git a/tests/test_litellm/llms/minimax/chat/test_transformation.py b/tests/test_litellm/llms/minimax/chat/test_transformation.py
index 9d51b556500..f5488fefd58 100644
--- a/tests/test_litellm/llms/minimax/chat/test_transformation.py
+++ b/tests/test_litellm/llms/minimax/chat/test_transformation.py
@@ -11,6 +11,7 @@ import pytest
 import litellm
 from litellm import completion
 from litellm.llms.minimax.chat.transformation import MinimaxChatConfig
+from litellm.types.utils import Choices, Message, ModelResponse
 
 
 def test_minimax_chat_config():
@@ -99,14 +100,110 @@ def test_minimax_provider_config_manager():
     from litellm.types.utils import LlmProviders
     from litellm.utils import ProviderConfigManager
 
-    config = ProviderConfigManager.get_provider_chat_config(
-        model="MiniMax-M2.1", provider=LlmProviders.MINIMAX
-    )
+    config = ProviderConfigManager.get_provider_chat_config(model="MiniMax-M2.1", provider=LlmProviders.MINIMAX)
 
     assert config is not None
     assert isinstance(config, MinimaxChatConfig)
 
 
+def _build_response_with_reasoning(content: str | None, reasoning_content: str | None):
+    """Helper: a ModelResponse whose single choice has the given content/reasoning_content."""
+    message = Message(content=content, role="assistant", reasoning_content=reasoning_content)
+    return ModelResponse(
+        id="test",
+        choices=[Choices(finish_reason="stop", index=0, message=message)],
+        model="MiniMax-M2.1",
+    )
+
+
+def test_transform_response_promotes_reasoning_content_when_content_empty():
+    """Issue #38197: when the model's whole answer sits inside
+    with nothing trailing, the shared parser leaves content empty. The override
+    must fall back to reasoning_content so the model's output isn't discarded."""
+    config = MinimaxChatConfig()
+    raw = MagicMock(status_code=200, json=lambda: {})
+    original = _build_response_with_reasoning(
+        content=None,
+        reasoning_content="The answer to 2+2 is 4.",
+    )
+
+    with patch(  # test-quality-ok: isolates override's reasoning_content fallback from parent's HTTP/parsing machinery; no injection seam for super().transform_response
+        "litellm.llms.openai.chat.gpt_transformation.OpenAIGPTConfig.transform_response",
+        return_value=original,
+    ):
+        result = config.transform_response(
+            model="MiniMax-M2.1",
+            raw_response=raw,
+            model_response=ModelResponse(model="MiniMax-M2.1"),
+            logging_obj=MagicMock(),
+            request_data={},
+            messages=[],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+        )
+
+    assert result.choices[0].message.content == "The answer to 2+2 is 4."
+
+
+def test_transform_response_keeps_content_when_already_present():
+    """When content is non-empty (answer follows the  tag), the override
+    must not clobber it with reasoning_content."""
+    config = MinimaxChatConfig()
+    raw = MagicMock(status_code=200, json=lambda: {})
+    original = _build_response_with_reasoning(
+        content="The answer is 4.",
+        reasoning_content="Let me work this out.",
+    )
+
+    with patch(  # test-quality-ok: isolates override's no-clobber path from parent's HTTP/parsing machinery; no injection seam for super().transform_response
+        "litellm.llms.openai.chat.gpt_transformation.OpenAIGPTConfig.transform_response",
+        return_value=original,
+    ):
+        result = config.transform_response(
+            model="MiniMax-M2.1",
+            raw_response=raw,
+            model_response=ModelResponse(model="MiniMax-M2.1"),
+            logging_obj=MagicMock(),
+            request_data={},
+            messages=[],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+        )
+
+    assert result.choices[0].message.content == "The answer is 4."
+    assert result.choices[0].message.reasoning_content == "Let me work this out."
+
+
+def test_transform_response_noop_without_reasoning_content():
+    """When reasoning_content is absent/None, content is left untouched."""
+    config = MinimaxChatConfig()
+    raw = MagicMock(status_code=200, json=lambda: {})
+    original = _build_response_with_reasoning(
+        content="plain answer",
+        reasoning_content=None,
+    )
+
+    with patch(  # test-quality-ok: isolates override's no-op path from parent's HTTP/parsing machinery; no injection seam for super().transform_response
+        "litellm.llms.openai.chat.gpt_transformation.OpenAIGPTConfig.transform_response",
+        return_value=original,
+    ):
+        result = config.transform_response(
+            model="MiniMax-M2.1",
+            raw_response=raw,
+            model_response=ModelResponse(model="MiniMax-M2.1"),
+            logging_obj=MagicMock(),
+            request_data={},
+            messages=[],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+        )
+
+    assert result.choices[0].message.content == "plain answer"
+
+
 @pytest.mark.skip(reason="Requires actual MiniMax API key")
 def test_minimax_chat_completion_basic():
     """Test basic chat completion with MiniMax OpenAI-compatible API"""