diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
index 6f513ce1bd4..c875bf5b535 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
@@ -716,6 +716,136 @@ def test_generic_cost_per_token_tier_without_an_output_rate_bills_the_model_rate
         litellm.model_cost.pop(model, None)
 
 
+def test_generic_cost_per_token_tier_without_cache_rates_bills_cache_at_the_tier_input_rate():
+    model = "litellm-test-tiered-no-cache-rates"
+    custom_llm_provider = "openrouter"
+    litellm.register_model(
+        {
+            model: {
+                "litellm_provider": custom_llm_provider,
+                "mode": "chat",
+                "cache_read_input_token_cost": 9e-09,
+                "cache_creation_input_token_cost": 9e-06,
+                "tiered_pricing": [
+                    {
+                        "range": [0, 32000],
+                        "input_cost_per_token": 4.6e-07,
+                        "output_cost_per_token": 2.3e-06,
+                    },
+                    {
+                        "range": [32000, 128000],
+                        "input_cost_per_token": 7e-07,
+                        "output_cost_per_token": 3.5e-06,
+                    },
+                ],
+            }
+        }
+    )
+
+    try:
+        uncached = Usage(prompt_tokens=40000, completion_tokens=100, total_tokens=40100)
+        cached = Usage(
+            prompt_tokens=40000,
+            completion_tokens=100,
+            total_tokens=40100,
+            prompt_tokens_details=PromptTokensDetailsWrapper(
+                cached_tokens=5000, cache_creation_tokens=15000
+            ),
+        )
+        uncached_prompt_cost, _ = generic_cost_per_token(
+            model=model,
+            usage=uncached,
+            custom_llm_provider=custom_llm_provider,
+        )
+        cached_prompt_cost, cached_completion_cost = generic_cost_per_token(
+            model=model,
+            usage=cached,
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        tier_input_rate = 7e-07
+        assert round(cached_prompt_cost, 12) == round(40000 * tier_input_rate, 12)
+        assert round(cached_prompt_cost, 12) == round(uncached_prompt_cost, 12)
+        assert round(cached_completion_cost, 12) == round(100 * 3.5e-06, 12)
+    finally:
+        litellm.model_cost.pop(model, None)
+
+
+def test_generic_cost_per_token_tier_without_a_1hr_cache_rate_bills_the_tier_cache_creation_rate():
+    model = "litellm-test-tiered-no-1hr-cache-rate"
+    custom_llm_provider = "openrouter"
+    litellm.register_model(
+        {
+            model: {
+                "litellm_provider": custom_llm_provider,
+                "mode": "chat",
+                "cache_creation_input_token_cost_above_1hr": 9e-05,
+                "tiered_pricing": [
+                    {
+                        "range": [0, 128000],
+                        "input_cost_per_token": 7e-07,
+                        "output_cost_per_token": 3.5e-06,
+                        "cache_creation_input_token_cost": 8.75e-07,
+                    }
+                ],
+            }
+        }
+    )
+
+    try:
+        usage = Usage(
+            prompt_tokens=1000,
+            completion_tokens=10,
+            total_tokens=1010,
+            prompt_tokens_details=PromptTokensDetailsWrapper(
+                cache_creation_tokens=800,
+                cache_creation_token_details=CacheCreationTokenDetails(
+                    ephemeral_5m_input_tokens=300, ephemeral_1h_input_tokens=500
+                ),
+            ),
+        )
+        prompt_cost, completion_cost = generic_cost_per_token(
+            model=model,
+            usage=usage,
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        tier_cache_creation_rate = 8.75e-07
+        expected_prompt = (200 * 7e-07) + (800 * tier_cache_creation_rate)
+        assert round(prompt_cost, 12) == round(expected_prompt, 12)
+        assert round(completion_cost, 12) == round(10 * 3.5e-06, 12)
+    finally:
+        litellm.model_cost.pop(model, None)
+
+
+def test_generic_cost_per_token_tier_without_an_input_rate_is_not_a_priced_tier():
+    model = "litellm-test-tiered-no-input-rate"
+    custom_llm_provider = "openrouter"
+    litellm.register_model(
+        {
+            model: {
+                "litellm_provider": custom_llm_provider,
+                "mode": "chat",
+                "input_cost_per_token": 1e-06,
+                "output_cost_per_token": 2e-06,
+                "tiered_pricing": [{"range": [0, 128000], "output_cost_per_token": 3.5e-06}],
+            }
+        }
+    )
+
+    try:
+        usage = Usage(prompt_tokens=1000, completion_tokens=100, total_tokens=1100)
+        prompt_cost, completion_cost = generic_cost_per_token(
+            model=model,
+            usage=usage,
+            custom_llm_provider=custom_llm_provider,
+        )
+        assert round(prompt_cost, 12) == round(1000 * 1e-06, 12)
+        assert round(completion_cost, 12) == round(100 * 2e-06, 12)
+    finally:
+        litellm.model_cost.pop(model, None)
+
+
 def test_router_deployment_with_input_only_tiers_bills_completions_at_the_backend_rate():
     """Regression: the router registers a deployment's custom pricing as a standalone
     model_cost entry holding only the supplied fields, so an input-only tier table left