diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index 983c8f3736a..13da1bb648c 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -208,10 +208,9 @@ def _parse_above_token_threshold(key: str) -> float:
 def _get_effective_prompt_tokens_for_tiered_pricing(usage: Usage) -> float:
     """Return the total effective input tokens for tier-threshold comparisons.
 
-    Some providers (Anthropic direct, Bedrock) roll cache_creation and cache_read
-    tokens into prompt_tokens before constructing the Usage object, while others
-    (Vertex AI) keep them separate.  When prompt_tokens_details is present we can
-    derive the true total from its per-category fields and avoid double-counting.
+    Some providers report cache tokens inside prompt_tokens, while others keep
+    them separate. When prompt_tokens_details is present, prefer the category
+    fields so the threshold check can avoid double-counting cache tokens.
     """
     if usage.prompt_tokens_details is not None:
         details = usage.prompt_tokens_details
@@ -231,7 +230,7 @@ def _get_effective_prompt_tokens_for_tiered_pricing(usage: Usage) -> float:
         text_tokens = float(raw_text_tokens or 0)
         return text_tokens + cached_tokens + cache_creation
 
-    # No prompt_tokens_details — add explicit cache fields only if they are
+    # No prompt_tokens_details. Add explicit cache fields only if they are
     # not already rolled into prompt_tokens (determined by their presence).
     prompt_tokens = float(getattr(usage, "prompt_tokens", 0) or 0)
     cache_read_tokens = float(getattr(usage, "cache_read_input_tokens", 0) or 0)