fix(usage): preserve OpenAI-wire cache writes at canonical accounting boundary (#85706)
Salvaged from PR #85702 by @JoaoMarcos44, composed onto the mapping-safe _usage_get reads (PR #74591 by @RelaxJonh) and the flat cached_tokens / Anthropic-name fallbacks (PRs #66105, #52571): - cache-write precedence in the chat_completions branch: details.cache_write_tokens > details.cache_creation_input_tokens > usage.cache_creation_input_tokens > usage.cache_write_tokens - codex_responses branch reads details.cache_write_tokens (GPT-5.6+ documented name) with cache_creation_tokens fallback (from PR #70522) - _usage_count(): clamp malformed negative counters to 0 - all reads in every branch are mapping-safe via _usage_get
This commit is contained in:
joaomarcos
+60
-25
@@ -1043,6 +1043,16 @@ def _usage_get(obj: Any, name: str, default: Any = 0) -> Any:
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return getattr(obj, name, default)
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def _usage_count(value: Any) -> int:
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"""Coerce a usage counter to a non-negative integer.
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Providers occasionally emit malformed negative counters; clamp them to 0
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so a bad field cannot corrupt session accounting (#85706).
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"""
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return max(0, _to_int(value))
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def resolve_billing_route(
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model_name: str,
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provider: Optional[str] = None,
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@@ -1282,30 +1292,42 @@ def normalize_usage(
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mode = (api_mode or "").strip().lower()
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if mode == "anthropic_messages" or provider_name == "anthropic":
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input_tokens = _to_int(_usage_get(response_usage, "input_tokens", 0))
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output_tokens = _to_int(_usage_get(response_usage, "output_tokens", 0))
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cache_read_tokens = _to_int(_usage_get(response_usage, "cache_read_input_tokens", 0))
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cache_write_tokens = _to_int(_usage_get(response_usage, "cache_creation_input_tokens", 0))
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elif mode == "codex_responses":
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input_total = _to_int(_usage_get(response_usage, "input_tokens", 0))
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output_tokens = _to_int(_usage_get(response_usage, "output_tokens", 0))
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details = _usage_get(response_usage, "input_tokens_details", None)
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cache_read_tokens = _to_int(_usage_get(details, "cached_tokens", 0) if details else 0)
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cache_write_tokens = _to_int(
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_usage_get(details, "cache_creation_tokens", 0) if details else 0
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input_tokens = _usage_count(_usage_get(response_usage, "input_tokens", 0))
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output_tokens = _usage_count(_usage_get(response_usage, "output_tokens", 0))
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cache_read_tokens = _usage_count(_usage_get(response_usage, "cache_read_input_tokens", 0))
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cache_write_tokens = _usage_count(
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_usage_get(response_usage, "cache_creation_input_tokens", 0)
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)
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elif mode == "codex_responses":
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input_total = _usage_count(_usage_get(response_usage, "input_tokens", 0))
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output_tokens = _usage_count(_usage_get(response_usage, "output_tokens", 0))
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details = _usage_get(response_usage, "input_tokens_details", None)
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cache_read_tokens = _usage_count(
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_usage_get(details, "cached_tokens", 0) if details else 0
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)
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# OpenAI's documented field for GPT-5.6+ explicit cache writes is
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# `cache_write_tokens` (billed at 1.25x); `cache_creation_tokens` is
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# kept as a fallback for older/alternate Responses-compatible
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# endpoints (#70543).
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cache_write_tokens = _usage_count(
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_usage_get(details, "cache_write_tokens", 0) if details else 0
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)
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if not cache_write_tokens:
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cache_write_tokens = _usage_count(
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_usage_get(details, "cache_creation_tokens", 0) if details else 0
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)
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input_tokens = max(0, input_total - cache_read_tokens - cache_write_tokens)
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else:
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# OpenAI-style names first; fall back to Anthropic-style
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# (input_tokens/output_tokens). Local OpenAI-compatible servers like
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# mlx_vlm.server emit the Anthropic names in chat_completions responses,
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# and the OpenAI Python client preserves them as extra attributes.
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prompt_total = _to_int(_usage_get(response_usage, "prompt_tokens", 0)) or _to_int(
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_usage_get(response_usage, "input_tokens", 0)
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)
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output_tokens = _to_int(_usage_get(response_usage, "completion_tokens", 0)) or _to_int(
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_usage_get(response_usage, "output_tokens", 0)
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)
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prompt_total = _usage_count(
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_usage_get(response_usage, "prompt_tokens", 0)
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) or _usage_count(_usage_get(response_usage, "input_tokens", 0))
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output_tokens = _usage_count(
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_usage_get(response_usage, "completion_tokens", 0)
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) or _usage_count(_usage_get(response_usage, "output_tokens", 0))
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details = _usage_get(response_usage, "prompt_tokens_details", None)
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# Primary: OpenAI-style prompt_tokens_details. Fallback: Anthropic-style
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# top-level fields that some OpenAI-compatible proxies (OpenRouter, Vercel
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@@ -1313,16 +1335,20 @@ def normalize_usage(
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# fallback, cache writes are undercounted as 0 and cache reads can be
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# missed when the proxy only surfaces them at the top level.
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# Port of cline/cline#10266.
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cache_read_tokens = _to_int(_usage_get(details, "cached_tokens", 0) if details else 0)
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cache_read_tokens = _usage_count(
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_usage_get(details, "cached_tokens", 0) if details else 0
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)
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if not cache_read_tokens:
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cache_read_tokens = _to_int(_usage_get(response_usage, "cache_read_input_tokens", 0))
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cache_read_tokens = _usage_count(
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_usage_get(response_usage, "cache_read_input_tokens", 0)
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)
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if not cache_read_tokens:
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# DeepSeek's native API (api.deepseek.com) reports context-cache
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# hits as top-level prompt_cache_hit_tokens (+ the complementary
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# prompt_cache_miss_tokens; prompt_tokens = hit + miss), not the
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# OpenAI nested shape. Without this, direct DeepSeek sessions
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# always showed 0 cache-hit tokens (#61871).
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cache_read_tokens = _to_int(
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cache_read_tokens = _usage_count(
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_usage_get(response_usage, "prompt_cache_hit_tokens", 0)
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)
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if not cache_read_tokens:
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@@ -1331,16 +1357,25 @@ def normalize_usage(
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# OpenAI nested prompt_tokens_details.cached_tokens shape. Without
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# this, direct Kimi sessions always showed 0 cache-hit tokens and
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# the hits were billed at the full input rate (#65722).
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cache_read_tokens = _to_int(
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cache_read_tokens = _usage_count(
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_usage_get(response_usage, "cached_tokens", 0)
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)
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cache_write_tokens = _to_int(
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cache_write_tokens = _usage_count(
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_usage_get(details, "cache_write_tokens", 0) if details else 0
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)
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if not cache_write_tokens:
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cache_write_tokens = _to_int(
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cache_write_tokens = _usage_count(
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_usage_get(details, "cache_creation_input_tokens", 0)
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if details else 0
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)
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if not cache_write_tokens:
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cache_write_tokens = _usage_count(
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_usage_get(response_usage, "cache_creation_input_tokens", 0)
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)
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if not cache_write_tokens:
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cache_write_tokens = _usage_count(
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_usage_get(response_usage, "cache_write_tokens", 0)
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)
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input_tokens = max(0, prompt_total - cache_read_tokens - cache_write_tokens)
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reasoning_tokens = 0
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@@ -1353,11 +1388,11 @@ def normalize_usage(
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# single calls burning 21K reasoning tokens to emit 500 visible tokens).
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output_details = _usage_get(response_usage, "output_tokens_details", None)
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if output_details:
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reasoning_tokens = _to_int(_usage_get(output_details, "reasoning_tokens", 0))
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reasoning_tokens = _usage_count(_usage_get(output_details, "reasoning_tokens", 0))
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if not reasoning_tokens:
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completion_details = _usage_get(response_usage, "completion_tokens_details", None)
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if completion_details:
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reasoning_tokens = _to_int(
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reasoning_tokens = _usage_count(
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_usage_get(completion_details, "reasoning_tokens", 0)
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)
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@@ -570,3 +570,133 @@ def test_normalize_usage_handles_dict_openai_chat_completions():
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assert result.cache_read_tokens == 200
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assert result.input_tokens == 500 - 200 # prompt_total - cache_read
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assert result.reasoning_tokens == 30
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def test_normalize_usage_openai_reads_nested_cache_creation_tokens():
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usage = SimpleNamespace(
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prompt_tokens=1000,
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completion_tokens=200,
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prompt_tokens_details=SimpleNamespace(
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cached_tokens=100,
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cache_creation_input_tokens=300,
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),
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)
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.cache_read_tokens == 100
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assert normalized.cache_write_tokens == 300
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assert normalized.input_tokens == 600
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def test_normalize_usage_openai_reads_mapping_cache_creation_tokens():
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usage = {
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"prompt_tokens": 1000,
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"completion_tokens": 200,
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"prompt_tokens_details": {"cache_creation_input_tokens": 300},
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}
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.cache_write_tokens == 300
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assert normalized.input_tokens == 700
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def test_normalize_usage_openai_prefers_nested_cache_write_tokens():
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usage = SimpleNamespace(
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prompt_tokens=1000,
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prompt_tokens_details=SimpleNamespace(
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cache_write_tokens=200,
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cache_creation_input_tokens=300,
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),
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cache_creation_input_tokens=400,
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cache_write_tokens=500,
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)
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.cache_write_tokens == 200
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def test_normalize_usage_mapping_preserves_reasoning_tokens():
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usage = {
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"prompt_tokens": 100,
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"completion_tokens": 20,
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"prompt_tokens_details": {"cached_tokens": 40},
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"completion_tokens_details": {"reasoning_tokens": 12},
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}
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.reasoning_tokens == 12
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def test_normalize_usage_mapping_anthropic_fields():
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usage = {
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"input_tokens": 80,
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"output_tokens": 20,
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"cache_read_input_tokens": 50,
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"cache_creation_input_tokens": 10,
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"output_tokens_details": {"reasoning_tokens": 7},
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}
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normalized = normalize_usage(usage, provider="anthropic", api_mode="anthropic_messages")
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assert normalized.cache_read_tokens == 50
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assert normalized.cache_write_tokens == 10
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assert normalized.reasoning_tokens == 7
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def test_normalize_usage_mapping_codex_fields():
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usage = {
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"input_tokens": 100,
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"output_tokens": 20,
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"input_tokens_details": {
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"cached_tokens": 60,
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"cache_creation_tokens": 10,
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},
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"output_tokens_details": {"reasoning_tokens": 5},
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}
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normalized = normalize_usage(usage, provider="openai-codex", api_mode="codex_responses")
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assert normalized.input_tokens == 30
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assert normalized.cache_read_tokens == 60
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assert normalized.cache_write_tokens == 10
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assert normalized.reasoning_tokens == 5
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def test_normalize_usage_clamps_negative_counters():
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usage = SimpleNamespace(
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prompt_tokens=100,
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completion_tokens=-5,
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prompt_tokens_details=SimpleNamespace(
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cached_tokens=-10,
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cache_write_tokens=-20,
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),
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completion_tokens_details=SimpleNamespace(reasoning_tokens=-3),
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)
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.input_tokens == 100
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assert normalized.output_tokens == 0
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assert normalized.cache_read_tokens == 0
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assert normalized.cache_write_tokens == 0
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assert normalized.reasoning_tokens == 0
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def test_normalize_usage_clamps_inconsistent_cache_total():
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usage = {
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"prompt_tokens": 100,
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"completion_tokens": 10,
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"prompt_tokens_details": {
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"cached_tokens": 80,
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"cache_creation_input_tokens": 50,
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},
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}
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.input_tokens == 0
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assert normalized.prompt_tokens == 130
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