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Update litellm/litellm_core_utils/llm_cost_calc/utils.py

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Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
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Aman 2026-06-11 19:49:50 +05:30 • committed by Aman Agnihotri
parent a47dae06ff
commit afb9e85e44
1 changed files with 19 additions and 0 deletions
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litellm/litellm_core_utils/llm_cost_calc/utils.py
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@ -610,6 +610,25 @@ def _apply_off_peak_to_base_costs(
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.
"""
if usage.prompt_tokens_details is not None:
details = usage.prompt_tokens_details
text_tokens = float(getattr(details, "text_tokens", None) or 0)
if text_tokens == 0:
# text_tokens not set by this provider; fall back to prompt_tokens
text_tokens = float(getattr(usage, "prompt_tokens", 0) or 0)
cached_tokens = float(getattr(details, "cached_tokens", 0) or 0)
cache_creation = float(getattr(details, "cache_creation_tokens", 0) or 0)
return text_tokens + cached_tokens + cache_creation
# 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)
cache_creation_tokens = float(getattr(usage, "cache_creation_input_tokens", 0) or 0)