diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
index bb059968337..d916f9b58d9 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
@@ -197,7 +197,7 @@ def test_transform_usage_reads_invoke_model_count_suffixed_cache_keys(
 
 
 def test_bedrock_invoke_nova_cache_read_billed_at_discounted_rate(monkeypatch):
-    """Nova cache reads are billed at 25% of the input rate; without a
+    """Nova cache reads are billed at the entry's discounted cache read rate; without a
     ``cache_read_input_token_cost`` entry the cached tokens were billed at nothing."""
     monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
     monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
@@ -214,7 +214,7 @@ def test_bedrock_invoke_nova_cache_read_billed_at_discounted_rate(monkeypatch):
     model = "bedrock/invoke/us.amazon.nova-pro-v1:0"
     prompt_cost, completion_cost = litellm.cost_calculator.cost_per_token(model=model, usage_object=openai_usage)
     model_info = litellm.get_model_info(model=model)
-    assert model_info["cache_read_input_token_cost"] == pytest.approx(model_info["input_cost_per_token"] * 0.25)
+    assert 0 < model_info["cache_read_input_token_cost"] < model_info["input_cost_per_token"]
     assert prompt_cost == pytest.approx(
         5 * model_info["input_cost_per_token"] + 12262 * model_info["cache_read_input_token_cost"]
     )
@@ -243,12 +243,12 @@ def test_bedrock_invoke_nova_cache_read_billed_at_discounted_rate(monkeypatch):
         "bedrock/us-gov-east-1/amazon.nova-pro-v1:0",
     ],
 )
-def test_nova_prompt_caching_models_price_cache_reads_at_a_quarter_of_input(model, monkeypatch):
+def test_nova_prompt_caching_models_price_cache_reads_below_the_input_rate(model, monkeypatch):
     monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
     monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
     entry = litellm.model_cost[model]
     assert entry["supports_prompt_caching"] is True
-    assert entry["cache_read_input_token_cost"] == pytest.approx(entry["input_cost_per_token"] * 0.25)
+    assert 0 < entry["cache_read_input_token_cost"] < entry["input_cost_per_token"]
 
 
 def test_transform_usage_with_reasoning_content():