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refactor(agent/usage_pricing): table-drive normalize_usage field paths per API shape

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This commit is contained in:
Teknium
2026-09-02 19:07:15 -07:00
parent aa0ebfc103
commit cc7b9ef8b2
1 changed files with 40 additions and 41 deletions
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81
agent/usage_pricing.py
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@@ -470,16 +470,38 @@ def get_pricing_entry(
return _lookup_official_docs_pricing(route)
# Usage-field candidate paths per API shape: (input/prompt total, output, cache
# read, cache write); the first non-zero path wins.
_ANTHROPIC_USAGE_SHAPE = (
(("input_tokens",),), (("output_tokens",),), (("cache_read_input_tokens",),), (("cache_creation_input_tokens",),)
)
# OpenAI's documented GPT-5.6+ field is `cache_write_tokens` (billed at 1.25x);
# `cache_creation_tokens` is a fallback for older endpoints.
_CODEX_USAGE_SHAPE = (
(("input_tokens",),), (("output_tokens",),), (("input_tokens_details", "cached_tokens"),),
(("input_tokens_details", "cache_write_tokens"), ("input_tokens_details", "cache_creation_tokens")),
)
# OpenAI-style names first, then Anthropic-style: local OpenAI-compatible
# servers (e.g. mlx_vlm.server) emit input_tokens/output_tokens and the OpenAI
# client preserves them as extra attributes. Cache reads: nested OpenAI shape,
# then Anthropic-style top-level fields exposed by proxies routing Claude
# (OpenRouter, Vercel AI Gateway, Cline), then DeepSeek's prompt_cache_hit_tokens,
# then Kimi/Moonshot's cached_tokens — without these, direct sessions show 0
# hits and bill hits at the full input rate.
_CHAT_USAGE_SHAPE = (
(("prompt_tokens",), ("input_tokens",)),
(("completion_tokens",), ("output_tokens",)),
(("prompt_tokens_details", "cached_tokens"), ("cache_read_input_tokens",), ("prompt_cache_hit_tokens",), ("cached_tokens",)),
(("prompt_tokens_details", "cache_write_tokens"), ("prompt_tokens_details", "cache_creation_input_tokens"),
("cache_creation_input_tokens",), ("cache_write_tokens",)),
)
def normalize_usage(
response_usage: Any, *, provider: Optional[str] = None, api_mode: Optional[str] = None
) -> CanonicalUsage:
"""Normalize raw API response usage into canonical token buckets.
Three shapes: Anthropic (input/output/cache_read_input/cache_creation_input
tokens), Codex Responses and OpenAI Chat Completions. In the latter two the
input/prompt total INCLUDES cached tokens and the ``*_details`` object breaks
them out, so input_tokens is derived by subtraction.
"""
"""Normalize raw API response usage into canonical token buckets (Anthropic,
Codex Responses, or OpenAI Chat Completions shape)."""
if not response_usage:
return CanonicalUsage()
@@ -488,42 +510,19 @@ def normalize_usage(
u = response_usage
if mode == "anthropic_messages" or provider_name == "anthropic":
input_tokens = _usage_field(u, "input_tokens")
output_tokens = _usage_field(u, "output_tokens")
cache_read_tokens = _usage_field(u, "cache_read_input_tokens")
cache_write_tokens = _usage_field(u, "cache_creation_input_tokens")
shape = _ANTHROPIC_USAGE_SHAPE
elif mode == "codex_responses":
input_total = _usage_field(u, "input_tokens")
output_tokens = _usage_field(u, "output_tokens")
cache_read_tokens = _usage_field(u, "input_tokens_details", "cached_tokens")
# OpenAI's documented GPT-5.6+ field is `cache_write_tokens` (billed at
# 1.25x); `cache_creation_tokens` is a fallback for older endpoints.
cache_write_tokens = _first_nonzero(
u, ("input_tokens_details", "cache_write_tokens"),
("input_tokens_details", "cache_creation_tokens"),
)
input_tokens = max(0, input_total - cache_read_tokens - cache_write_tokens)
shape = _CODEX_USAGE_SHAPE
else:
# OpenAI-style names first, then Anthropic-style: local OpenAI-compatible
# servers (e.g. mlx_vlm.server) emit input_tokens/output_tokens and the
# OpenAI client preserves them as extra attributes.
prompt_total = _first_nonzero(u, ("prompt_tokens",), ("input_tokens",))
output_tokens = _first_nonzero(u, ("completion_tokens",), ("output_tokens",))
# Cache reads: nested OpenAI shape, then Anthropic-style top-level fields
# exposed by proxies routing Claude (OpenRouter, Vercel AI Gateway, Cline),
# then DeepSeek's top-level prompt_cache_hit_tokens, then Kimi/Moonshot's
# top-level cached_tokens — without these, direct sessions show 0 hits
# and bill hits at the full input rate.
cache_read_tokens = _first_nonzero(
u, ("prompt_tokens_details", "cached_tokens"), ("cache_read_input_tokens",),
("prompt_cache_hit_tokens",), ("cached_tokens",),
)
cache_write_tokens = _first_nonzero(
u, ("prompt_tokens_details", "cache_write_tokens"),
("prompt_tokens_details", "cache_creation_input_tokens"),
("cache_creation_input_tokens",), ("cache_write_tokens",),
)
input_tokens = max(0, prompt_total - cache_read_tokens - cache_write_tokens)
shape = _CHAT_USAGE_SHAPE
prompt_total, output_tokens, cache_read_tokens, cache_write_tokens = (
_first_nonzero(u, *paths) for paths in shape
)
# Anthropic reports uncached input directly; Codex/Chat totals INCLUDE
# cached tokens, so the cache buckets are subtracted back out.
input_tokens = prompt_total if shape is _ANTHROPIC_USAGE_SHAPE else max(
0, prompt_total - cache_read_tokens - cache_write_tokens
)
# Responses API: output_tokens_details.reasoning_tokens. Chat Completions
# (OpenAI, OpenRouter, DeepSeek, ...): completion_tokens_details.reasoning_tokens.