diff --git a/litellm/llms/text_completion_codestral.py b/litellm/llms/text_completion_codestral.py
index 124c840c3..a46b67b12 100644
--- a/litellm/llms/text_completion_codestral.py
+++ b/litellm/llms/text_completion_codestral.py
@@ -17,6 +17,7 @@ from litellm.utils import (
     Choices,
 )
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.types.llms.databricks import GenericStreamingChunk
 import litellm
 from .prompt_templates.factory import prompt_factory, custom_prompt
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
@@ -160,6 +161,39 @@ class MistralTextCompletionConfig:
 
         return optional_params
 
+    def _chunk_parser(self, chunk_data: str) -> GenericStreamingChunk:
+        text = ""
+        is_finished = False
+        finish_reason = None
+        logprobs = None
+
+        chunk_data = chunk_data.replace("data:", "")
+        chunk_data = chunk_data.strip()
+        if len(chunk_data) == 0 or chunk_data == "[DONE]":
+            return {
+                "text": "",
+                "is_finished": is_finished,
+                "finish_reason": finish_reason,
+            }
+        chunk_data_dict = json.loads(chunk_data)
+        original_chunk = litellm.ModelResponse(**chunk_data_dict, stream=True)
+        _choices = chunk_data_dict.get("choices", []) or []
+        _choice = _choices[0]
+        text = _choice.get("delta", {}).get("content", "")
+
+        if _choice.get("finish_reason") is not None:
+            is_finished = True
+            finish_reason = _choice.get("finish_reason")
+            logprobs = _choice.get("logprobs")
+
+        return GenericStreamingChunk(
+            text=text,
+            original_chunk=original_chunk,
+            is_finished=is_finished,
+            finish_reason=finish_reason,
+            logprobs=logprobs,
+        )
+
 
 class CodestralTextCompletion(BaseLLM):
     def __init__(self) -> None:
@@ -452,7 +486,7 @@ class CodestralTextCompletion(BaseLLM):
                 logging_obj=logging_obj,
             ),
             model=model,
-            custom_llm_provider="codestral",
+            custom_llm_provider="text-completion-codestral",
             logging_obj=logging_obj,
         )
         return streamwrapper
diff --git a/litellm/tests/test_text_completion.py b/litellm/tests/test_text_completion.py
index 1ddd8ea6b..bfd292a51 100644
--- a/litellm/tests/test_text_completion.py
+++ b/litellm/tests/test_text_completion.py
@@ -4105,3 +4105,40 @@ async def test_completion_codestral_fim_api():
         # assert cost > 0.0
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
+
+
+@pytest.mark.asyncio
+async def test_completion_codestral_fim_api_stream():
+    try:
+        from litellm._logging import verbose_logger
+        import logging
+
+        litellm.set_verbose = False
+
+        # verbose_logger.setLevel(level=logging.DEBUG)
+        response = await litellm.atext_completion(
+            model="text-completion-codestral/codestral-2405",
+            prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
+            suffix="return True",
+            temperature=0,
+            top_p=1,
+            stream=True,
+            seed=10,
+            stop=["return"],
+        )
+
+        full_response = ""
+        # Add any assertions here to check the response
+        async for chunk in response:
+            print(chunk)
+            full_response += chunk.get("choices")[0].get("text") or ""
+
+        print("full_response", full_response)
+
+        assert len(full_response) > 2  # we at least have a few chars in response :)
+
+        # cost = litellm.completion_cost(completion_response=response)
+        # print("cost to make mistral completion=", cost)
+        # assert cost > 0.0
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
diff --git a/litellm/utils.py b/litellm/utils.py
index f66077d7a..5da62505a 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -8546,6 +8546,25 @@ class CustomStreamWrapper:
                         completion_tokens=response_obj["usage"].completion_tokens,
                         total_tokens=response_obj["usage"].total_tokens,
                     )
+            elif self.custom_llm_provider == "text-completion-codestral":
+                response_obj = litellm.MistralTextCompletionConfig()._chunk_parser(
+                    chunk
+                )
+                completion_obj["content"] = response_obj["text"]
+                print_verbose(f"completion obj content: {completion_obj['content']}")
+                if response_obj["is_finished"]:
+                    self.received_finish_reason = response_obj["finish_reason"]
+                if (
+                    self.stream_options
+                    and self.stream_options.get("include_usage", False) == True
+                    and response_obj["usage"] is not None
+                ):
+                    self.sent_stream_usage = True
+                    model_response.usage = litellm.Usage(
+                        prompt_tokens=response_obj["usage"].prompt_tokens,
+                        completion_tokens=response_obj["usage"].completion_tokens,
+                        total_tokens=response_obj["usage"].total_tokens,
+                    )
             elif self.custom_llm_provider == "databricks":
                 response_obj = litellm.DatabricksConfig()._chunk_parser(chunk)
                 completion_obj["content"] = response_obj["text"]
@@ -9019,6 +9038,7 @@ class CustomStreamWrapper:
                 or self.custom_llm_provider == "azure"
                 or self.custom_llm_provider == "custom_openai"
                 or self.custom_llm_provider == "text-completion-openai"
+                or self.custom_llm_provider == "text-completion-codestral"
                 or self.custom_llm_provider == "azure_text"
                 or self.custom_llm_provider == "anthropic"
                 or self.custom_llm_provider == "anthropic_text"