diff --git a/python/semantic_kernel/connectors/ai/bedrock/services/model_provider/bedrock_cohere.py b/python/semantic_kernel/connectors/ai/bedrock/services/model_provider/bedrock_cohere.py
index 3e498758ae72..e9151b336c97 100644
--- a/python/semantic_kernel/connectors/ai/bedrock/services/model_provider/bedrock_cohere.py
+++ b/python/semantic_kernel/connectors/ai/bedrock/services/model_provider/bedrock_cohere.py
@@ -90,12 +90,34 @@ def get_text_embedding_request_body(text: str, settings: BedrockEmbeddingPromptE
     })
 
 
+# Preference order when the response is keyed by embedding type. It is fixed (not the JSON key order)
+# so the same requested types always yield the same representation for every text.
+_EMBEDDING_TYPE_PRIORITY = ("float", "int8", "uint8", "binary", "ubinary")
+
+
+def _is_numeric_vector(value: Any) -> bool:
+    return (
+        isinstance(value, list)
+        and len(value) > 0
+        and all(isinstance(v, (int, float)) and not isinstance(v, bool) for v in value)
+    )
+
+
 def parse_text_embedding_response(response: dict[str, Any]) -> list[float]:
     """Parse the response from text embedding for Cohere Command models."""
-    if "embeddings" not in response or not isinstance(response["embeddings"], list) or len(response["embeddings"]) == 0:
+    embeddings = response.get("embeddings")
+    if isinstance(embeddings, dict):
+        # With `embedding_types` set, the embeddings are keyed by type ({"float": [[...]], "int8": [[...]]}).
+        # Pick the first present type in a fixed priority order, then validate that it holds a non-empty vector.
+        selected = next((embeddings[t] for t in _EMBEDDING_TYPE_PRIORITY if t in embeddings), None)
+        if not isinstance(selected, list) or len(selected) == 0 or not _is_numeric_vector(selected[0]):
+            raise ServiceInvalidResponseError("The response from Cohere model does not contain valid embeddings.")
+        return selected[0]  # type: ignore
+
+    if not isinstance(embeddings, list) or len(embeddings) == 0:
         raise ServiceInvalidResponseError("The response from Cohere model does not contain embeddings.")
 
-    return response.get("embeddings")[0]  # type: ignore
+    return embeddings[0]  # type: ignore
 
 
 # endregion
diff --git a/python/tests/unit/connectors/ai/bedrock/services/test_bedrock_text_embedding_generation.py b/python/tests/unit/connectors/ai/bedrock/services/test_bedrock_text_embedding_generation.py
index a591f458c204..b021d1672f6f 100644
--- a/python/tests/unit/connectors/ai/bedrock/services/test_bedrock_text_embedding_generation.py
+++ b/python/tests/unit/connectors/ai/bedrock/services/test_bedrock_text_embedding_generation.py
@@ -1,5 +1,6 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+import json
 from unittest.mock import ANY, Mock, patch
 
 import boto3
@@ -233,3 +234,62 @@ async def test_bedrock_text_embedding_with_invalid_response(
 
         with pytest.raises(ServiceInvalidResponseError):
             await bedrock_text_embedding.generate_embeddings(["hello", "world"])
+
+
+@pytest.mark.parametrize(
+    "embeddings, expected",
+    [
+        ({"float": [[0.1, 0.2, 0.3]]}, [0.1, 0.2, 0.3]),
+        ({"int8": [[1, 2, 3]], "float": [[0.1, 0.2, 0.3]]}, [0.1, 0.2, 0.3]),
+        ({"int8": [[1, 2, 3]]}, [1, 2, 3]),
+        # Selection follows a fixed priority, not the JSON key order.
+        ({"binary": [[1]], "int8": [[1, 2]]}, [1, 2]),
+        ({"int8": [[1, 2]], "binary": [[1]]}, [1, 2]),
+        ([[0.1, 0.2, 0.3]], [0.1, 0.2, 0.3]),
+    ],
+)
+async def test_bedrock_cohere_text_embedding_with_embedding_types(embeddings, expected) -> None:
+    """Cohere returns the embeddings keyed by type when `embedding_types` is requested."""
+    body = Mock()
+    body.read.return_value = json.dumps({"embeddings": embeddings})
+
+    with patch.object(MockBedrockRuntimeClient, "invoke_model", return_value={"body": body}) as mock_model_invoke:
+        bedrock_text_embedding = BedrockTextEmbedding(
+            model_id="cohere.command",
+            runtime_client=MockBedrockRuntimeClient(),
+            client=MockBedrockClient(),
+        )
+
+        settings = BedrockEmbeddingPromptExecutionSettings(extension_data={"embedding_types": ["float", "int8"]})
+        response = await bedrock_text_embedding.generate_embeddings(["hello"], settings)
+
+        assert json.loads(mock_model_invoke.call_args.kwargs["body"])["embedding_types"] == ["float", "int8"]
+        assert response.tolist() == [expected]
+
+
+@pytest.mark.parametrize(
+    "embeddings",
+    [
+        {},
+        {"float": []},
+        {"float": 0.1},
+        {"float": [0.1, 0.2]},
+        {"float": [[]]},
+        {"int8": [[1, 2]], "float": []},
+        {"float": [["a", "b"]]},
+        {"unknown": [[1, 2]]},
+    ],
+)
+async def test_bedrock_cohere_text_embedding_with_invalid_typed_response(embeddings) -> None:
+    body = Mock()
+    body.read.return_value = json.dumps({"embeddings": embeddings})
+
+    with patch.object(MockBedrockRuntimeClient, "invoke_model", return_value={"body": body}):
+        bedrock_text_embedding = BedrockTextEmbedding(
+            model_id="cohere.command",
+            runtime_client=MockBedrockRuntimeClient(),
+            client=MockBedrockClient(),
+        )
+
+        with pytest.raises(ServiceInvalidResponseError):
+            await bedrock_text_embedding.generate_embeddings(["hello"])