diff --git a/litellm/tests/test_exceptions.py b/litellm/tests/test_exceptions.py
index 998e3eb9b9..8514996f4e 100644
--- a/litellm/tests/test_exceptions.py
+++ b/litellm/tests/test_exceptions.py
@@ -35,7 +35,10 @@ litellm.num_retries = 0
 
 # Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered
 
-models = ["command-nightly"]
+models = [
+    "sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4",
+    "bedrock/anthropic.claude-instant-v1",
+]
 
 
 # Test 1: Context Window Errors
@@ -45,7 +48,8 @@ def test_context_window(model):
     sample_text = "Say error 50 times" * 1000000
     messages = [{"content": sample_text, "role": "user"}]
     try:
-        litellm.set_verbose = True
+        litellm.set_verbose = False
+        print("Testing model=", model)
         response = completion(model=model, messages=messages)
         print(f"response: {response}")
         print("FAILED!")
diff --git a/litellm/utils.py b/litellm/utils.py
index 659bc7e794..6029493c65 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -5895,6 +5895,7 @@ def exception_type(
                     "too many tokens" in error_str
                     or "expected maxLength:" in error_str
                     or "Input is too long" in error_str
+                    or "prompt: length: 1.." in error_str
                     or "Too many input tokens" in error_str
                 ):
                     exception_mapping_worked = True
@@ -5988,6 +5989,17 @@ def exception_type(
                         llm_provider="sagemaker",
                         response=original_exception.response,
                     )
+                elif (
+                    "`inputs` tokens + `max_new_tokens` must be <=" in error_str
+                    or "instance type with more CPU capacity or memory" in error_str
+                ):
+                    exception_mapping_worked = True
+                    raise ContextWindowExceededError(
+                        message=f"SagemakerException - {error_str}",
+                        model=model,
+                        llm_provider="sagemaker",
+                        response=original_exception.response,
+                    )
             elif custom_llm_provider == "vertex_ai":
                 if (
                     "Vertex AI API has not been used in project" in error_str