diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py
index 490cd71b793..d00cb74aae0 100644
--- a/litellm/llms/bedrock/embed/cohere_transformation.py
+++ b/litellm/llms/bedrock/embed/cohere_transformation.py
@@ -15,7 +15,7 @@ def __init__(self) -> None:
         pass
 
     def get_supported_openai_params(self) -> List[str]:
-        return ["encoding_format"]
+        return ["encoding_format", "dimensions"]
 
     def map_openai_params(
         self, non_default_params: dict, optional_params: dict
@@ -23,6 +23,8 @@ def map_openai_params(
         for k, v in non_default_params.items():
             if k == "encoding_format":
                 optional_params["embedding_types"] = v
+            elif k == "dimensions":
+                optional_params["output_dimension"] = v
         return optional_params
 
     def _is_v3_model(self, model: str) -> bool:
diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py
index a85aaafe23d..6293efe9e09 100644
--- a/litellm/types/llms/bedrock.py
+++ b/litellm/types/llms/bedrock.py
@@ -397,6 +397,7 @@ class CohereEmbeddingRequest(TypedDict, total=False):
     input_type: Required[COHERE_EMBEDDING_INPUT_TYPES]
     truncate: Literal["NONE", "START", "END"]
     embedding_types: Literal["float", "int8", "uint8", "binary", "ubinary"]
+    output_dimension: int
 
 
 class CohereEmbeddingRequestWithModel(CohereEmbeddingRequest):
diff --git a/litellm/utils.py b/litellm/utils.py
index 7c4eec7ba32..a5df9381fc7 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -3242,7 +3242,7 @@ def _check_valid_arg(supported_params: Optional[list]):
             object = litellm.AmazonTitanMultimodalEmbeddingG1Config()
         elif "amazon.titan-embed-text-v2:0" in model:
             object = litellm.AmazonTitanV2Config()
-        elif "cohere.embed-multilingual-v3" in model:
+        elif "cohere.embed-multilingual-v3" in model or "cohere.embed-v4" in model:
             object = litellm.BedrockCohereEmbeddingConfig()
         elif "twelvelabs" in model or "marengo" in model:
             object = litellm.TwelveLabsMarengoEmbeddingConfig()