Preserve deployment identity through savings calculation, with canonical model fallback only when either ID is absent. Cover negotiated rates, unchanged deployments, alias/base-model cache accounting and missing IDs.
Fixes#38811. Based on the deployment-identity approach proposed by @QuantumBreakz in #38834.
Co-authored-by: Claude Code <noreply@anthropic.com>
A streamed Responses API relay handed the success handler a bare ResponsesAPIResponse, which the streaming assembly step drops, so the relay never reached the spend callbacks. Hand it the terminal response.completed event instead, which the assembly step already converts, and cover the whole flush path with a regression test that fails on the previous tip.
litellm's azure_ai config rejects reasoning_effort for gpt-chat-latest and Azure documents a fixed reasoning level for it, so the entry no longer advertises reasoning_effort_levels. The catalog metadata tests compare cost_per_token and the whisper transcription cost with the entry the calculator read instead of with list-price literals, the pattern #40195 removed
The merge base read the variable by truthiness, so any non-empty value
turned the global flag on. Parsing it as a flag made a value such as
temperature or enabled silently turn it off, and the only docs for the
variable describe it as a list of parameter names, so keep those values
on and log a warning that asks for true or false. A blank value stays off
without a warning
Nothing calls it since the streaming pipeline detects rewrites at run time
through the stream observer, so the base method and the content filter's
override were dead code with dead tests
A policy that both adds a guardrail and steps it in a post_call pipeline
used to drop the guardrail from the request's guardrail list outright, so
its pre_call stage never ran. The per-hook loops already skip guardrails
by pipeline mode, so the mode-agnostic subtraction only lost coverage
CustomGuardrail.__init_subclass__ wrapped _StreamRewriteObserver.apply_guardrail in log_guardrail_information, so every streaming step recorded a second standard_logging_guardrail_information entry and span next to the inner guardrail's own. The observer's method now carries the marker that skips the wrapper. The step also adds the guardrail to the applied guardrails header the way the non-streaming unified path does, so streamed spend rows name the guardrail that scanned them
The streaming loop skipped every guardrail stepped by a post_call pipeline, even when the pipeline was dropped from the stream for lacking the unified apply_guardrail interface, so a default_on guardrail that only implements async_post_call_streaming_iterator_hook stopped governing streams it governed on the merge base. The skip set now comes from the pipelines that will gate the stream
Direct litellm.cost_per_token callers that name a Model Router deployment as
the model get the routing fee again, as they did before this branch, and the
fee is still charged exactly once on every completion_cost path. The
grok-4-20 entries bill cached prompt tokens at the input rate, since Azure has
no cached-input meter for them, and the model_router twin carries the same
limits and retirement date as model-router. The catalog test now exercises
the cost calculator and map relations instead of pinning map fields.
Restores the public keyword removed at 415bdbfd8f. A direct caller that
names the Model Router as the request model gets the routing fee folded
into the prompt cost once; completion_cost never passes it and charges
the fee through the additional-costs hook as before
A deployment drop_params string that is not a flag value (a typo like ture) stayed silently off. The router now logs one warning per deployment. LITELLM_DROP_PARAMS and litellm_settings.drop_params share the same helper, so a non-flag value there warns as well instead of flipping silently from on to off
The router fee was folded into azure_ai.cost_per_token and then added again
by the additional_costs hook, so every routed request paid it twice. The hook
now owns the fee, the entry named by the deployment supplies the price, and a
response priced as the router entry itself is not charged again
model-router, gpt-chat-latest and cohere-command-a carry the limits from the
Foundry models page, and model-router and grok-4-20-* carry their retirement
dates. The router tests now run at the completion_cost level with a Logging
object, which is the path the proxy takes, and fail at the merge base
The failure hook's usage estimate and the project ITPM reservation both
called litellm.token_counter inline on the event loop, so a large request
that failed or hit the limiter stalled the gateway the same way the
count_tokens endpoints did. Both now run through asyncify. The loop-lag
probe the existing tests used moves into a shared helper that warms the
tokenizer first, and two new tests fail when either count runs inline
get_litellm_params normalizes drop_params once, so a client-body string and
router_settings.default_litellm_params reach the anthropic, bedrock, and
azure_ai gates as a bool. LITELLM_DROP_PARAMS=false now means off. A value
that is neither a flag nor a string logs one warning and counts as unset,
both in the deployment validator and in litellm_settings.
Marengo 3.0 requests now get a 400 naming any textTruncate, lengthSec,
useFixedLengthSec, or minClipSec parameter, and any video or audio option
sent with a text, image, text_image, or multi_input request, instead of
silently dropping them. drop_params (global, per deployment, or per
request) drops them instead. Pydantic validation errors name the field
and the reason, and the 3.0 marker is the exact "marengo-embed-3-" model
id segment.
The totals, the per-token-type lines and the reported rates each resolved off-peak pricing on their own clock read, so a quote taken as a window opened could bill on one side of the boundary and report rates from the other. /cost/estimate now pins a billing moment for the whole quote, and every rate lookup answers for the pinned moment instead of reading the clock again
Claude-Session: https://claude.ai/code/session_011Tn3657NkV6ojLqewL64Kb
The new metadata test sat at the top of tests/test_litellm. The azure_ai metadata tests live in tests/test_litellm/llms/azure_ai next to the cost calculator test, so this moves it there and bumps its repo-root lookup by the two extra directory levels. No test changes.
A string above TOKEN_COUNTER_MAX_EXACT_CHARS was counted from its first cap characters and scaled, so a string whose start tokenizes unlike its end got a skewed count, and that count reaches fallback billing when the provider sends no usage. The estimate now tokenizes 16 evenly spaced samples that together total the cap and scales their sum by the string's length, keeping the same bound on work while tracking the whole string
AWS prices Marengo 2.7 and 3.0 text and image embeddings per request, never per
token, and their responses carry no token count. The old transform estimated
prompt tokens from the vector length, which billed a text request at 128 tokens
times the per-token rate (0.00896 instead of 0.00007). Marengo responses now
report zero tokens with query_count and image_count derived from the request
batch, and all six Marengo cost-map entries price per request (with the video
and audio per-second and per-image rates on the base entries). query_count is a
new prompt_tokens_details field wired to input_cost_per_query in the cost
calculator.
* fix(proxy): log disable_budget_reservation notice once at config load
The disabled-budget-reservation reminder fired as a WARNING inside request
authentication, so every authenticated request on a proxy that deliberately
set the flag produced one warning line. The notice now runs once per worker
when general_settings loads, at INFO, and the request path only skips the
reservation. Reservation skipping and read-time budget checks are unchanged
* fix(proxy): keep budget notice sentinel with constants
* fix(proxy): expose shared budget notice state
Add cost map entries for azure_ai/gpt-chat-latest, codex-mini, whisper,
model-router, cohere-command-a, grok-4-20-reasoning, and
grok-4-20-non-reasoning, priced from the live Azure AI Foundry and Azure
OpenAI pricing pages and the Azure Retail Prices API.
Skip the model router flat fee when the response model is the router
entry itself, since the generic cost already priced that fee. Before,
azure_ai/model_router charged it twice.
Resolves LIT-3157
Both proxy token counting endpoints already count in a worker thread, but the
HuggingFace tokenizer's encode holds the GIL for the whole call, so a 600k-token
count on a Claude model still froze the event loop for up to 0.8 s and every
other request with it. Count through encode_batch_fast, which releases the GIL,
and tokenize at most TOKEN_COUNTER_MAX_EXACT_CHARS characters of any one string
(default 4,000,000), scaling the exact count of that prefix by the string's
length above it so the largest payloads stay bounded.
