Adds the OpenAI gpt-6-astra entry to both price files with standard, flex, priority (fast mode), batch, and above-272K long-context rates, and regression tests covering each tier and the batch rates.
The unconditional region read let a base_model or custom pricing
deployment resolve to the regional cost-map key: a bedrock kimi
base_model shifted to regional rates and vertex claude-opus-5 with a
us-east5 key priced 0.0. Region now applies only when the model name
comes from the provider response (provider_response_model or the
response's own model), matching the base branch. Restores the #38069
regression test and adds region-on-provider-model and base-model-free
cases
Adds a Gemini audio transcription config that maps /v1/audio/transcriptions
onto the Interactions API (speaker attribution and word timestamps land on
the OpenAI verbose_json shape), registers both models with published pricing,
routes text-only Live sessions to TEXT responseModalities so
gemini-3.5-transcribe-live sessions survive, and makes the token-priced
transcription cost path provider-aware instead of hardcoding OpenAI.
Vertex Gemini 3.x models route through cost_per_character (the cost_router
token-path gate only matches gemini-2), and its token fallbacks dropped
service_tier, so ON_DEMAND_FLEX responses were still billed at the standard
rate. Pass the tier through the call site and all four fallbacks.
The GA vertex model had no cost map entry, and the realtime cost handler
accepted the router's price-less auto-registered deployment entry for the
session.created model at zero-defaulted rates, so sessions billed 0.0 even
when base_model pointed at the priced preview key. Adds the GA entry at its
published rates and makes the handler fall through zero-defaulted candidates
unless their cost map entry explicitly declares pricing.
When a router-facing model_name alias contains a '/' whose leading segment
is not a registered provider (e.g. 'vertex/claude-opus-5' for deployment
'vertex_ai/claude-opus-5'), _select_model_name_for_cost_calc re-prefixed
it into a non-existent key ('vertex_ai/vertex/claude-opus-5'), so cost
lookup silently priced every streamed request at $0 - token counts were
recorded, no error raised, budgets never tripped.
After prefixing, walk the alias tail and return the first assembly that
exists in litellm.model_cost ('vertex_ai/claude-opus-5'). Provider/region
segments in the head are preserved, and an alias that resolves to no
known key keeps the previous behavior (no crash, legacy double-prefix).
Fixes#38069
* test(cost-calc): stop 182 global writes leaking out of the cost-calc suites
Across test_cost_calculator.py and llm_cost_calc/test_llm_cost_calc_utils.py,
58 tests opened by setting LITELLM_LOCAL_MODEL_COST_MAP in os.environ and
replacing litellm.model_cost, and none of them put the env var back. The
second file already had a _local_model_cost_map fixture doing it by hand with
a try/finally, so both idioms sat in the same file.
Keep that fixture, give it monkeypatch, and have every one of those tests ask
for it. The margin and discount tests drop their hand-rolled
copy-then-restore in favour of monkeypatch.setattr, which also puts the
global back when an assertion fails part way through.
Both files also drop a sys.path.insert whose argument resolves outside the
repo, so it was never what made the imports work.
TQ003 1077 -> 1075, TQ004 768 -> 693, TQ005 2836 -> 2731, and the budget
ceilings come down with them.
* fix(test): make the streamed-cost tests load the map they assert against
The local_cost_map fixture set LITELLM_LOCAL_MODEL_COST_MAP but never reloaded
litellm.model_cost, and reading the variable is not what loads the map. So the
three streaming-cost tests billed against whatever map the process happened to
be holding, and their hardcoded prices only held when something else had
already swapped in the checked-in one. This branch stops the cost-calc tests
leaking that map, which left test_main billing at the ambient prices instead.
The fixture now loads the map it names, so the prices these tests assert hold
on their own.
batch_cost_calculator gated the batch rate fields on truthiness, so a deployment
that configures input_cost_per_token_batches or its output twin as 0.0 was read
as having configured nothing and that token direction fell through to half the
standard rate. Layering declared rates over published ones made this reachable:
a deployment declaring only a zero batch rate previously kept a fabricated zero
on the standard field, which happened to bill nothing.
The two batch fields are now gated on presence. Verified no cost-map entry
changes behavior: the only three carrying a zero batch rate are embeddings, whose
standard output rate is also 0.0, so both paths yield the same zero.
Adds a parametrized regression over an explicit zero, an explicit non-zero, and
unset, plus coverage for the deployment id get_model_info cannot resolve, which
were the lines Codecov flagged.
Folds every successful auto-routed request into LiteLLM_AutoRouterSession with one
conditional upsert at spend-write time, classifying each turn (same model, first
visit, return to tier, out of order) against the row's own columns so nothing is
read before the write. The upsert's placeholders and argument tuple both derive
from the transaction dataclass's own field order, so the SQL and the call site
cannot drift apart. GET /auto_router/benchmarks aggregates the rollup, grouped
by the full (router, type) identity, and never scans LiteLLM_SpendLogs. A turn's
cache interaction is derived once from its usage record (savings.py owns the
extraction; compute_savings_spend derives cache reads from usage_object itself),
hits are counted order-independently so the overall hit rate matches its covered
denominator, caller-chosen session ids are bounded before entering the primary
key, and a poisoned statement drops only its own session's remaining turns.
Return misses inside the recorded TTL are named for what the telemetry shows
(within_ttl) rather than a presumed cause, since a provider can evict early.
Savings ride each router's derived baseline by default, so the response carries
no deployment-wide baseline label. Rollup retention has its own
maximum_autorouter_session_retention_period setting, pattern-identical to the
spend-logs knob and running in the same cleanup job on its own cutoff. Every
drain trigger sizes the queues through one owner and the enqueue honors
disable_spend_logs beside the tool-usage queue it mirrors.
* feat(dashscope): add qwen3.7-plus and qwen3.7-max to the model cost map
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore: limit backup cost map diff to the new dashscope entries
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(cost_calculator): adjust tier-only alias assertion for mapped qwen3.7-plus
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(dashscope): drop redundant cost map pinning tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(cost_calculator): point tier-only alias check at an unmapped model
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: shivam <shivam@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Anthropic-shaped usage was mapped through the Responses API usage converter, which ignores top-level cache_read_input_tokens/cache_creation_input_tokens, so cache hits on /v1/messages were billed entirely at the uncached input rate
combine_usage_objects iterates prompt_tokens_details model_fields and sums each;
with cache_write_tokens and cache_creation_tokens now mirroring each other via
__setattr__, the pair was summed twice, doubling cache creation counts for
Anthropic batch cost calc, mid-stream fallback usage merges, and realtime usage.
Collapse the mirrored pair to one representative before summing.
Route cost calculation to the deployment's router_model_id entry when it carries tiered_pricing but no flat per-token rate, so models like dashscope/qwen3.7-plus are billed via their tier table rather than the pricing-stripped shared alias.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(batches): price anthropic passthrough message batches correctly in batch cost job
Anthropic message batches created via the /anthropic passthrough were never
cost tracked. The CheckBatchCost job fetched batch results from the Files API
(POST /v1/files/msgbatch_.../content), which Anthropic rejects with "File id
must have file_ prefix"; the error response was silently wrapped as file
content, parsed as zero successful rows, logged as a $0 aretrieve_batch spend
row, and the job was marked batch_processed=true so the $0 was permanent.
Route msgbatch_ file ids to GET /v1/messages/batches/{id}/results in the
anthropic files transformation, raise on HTTP error status in
retrieve_file_content instead of returning the error body as content, parse
Anthropic's results JSONL shape (result.type == "succeeded",
result.message.usage with cache creation/read tokens) in batch_utils, price
cache creation tokens at cache_creation_input_token_cost in the batch cost
fallback (50% batch discount preserved for base input, cache reads, cache
writes, and output), and leave the managed object row unprocessed when cost
tracking fails so a later poll retries instead of permanently recording $0.
* fix(batches): carry cache token details into aggregated anthropic batch usage
* fix(llm_http_handler): send dict transcription request data as a JSON body
httpx form-encodes dicts passed via data= and silently ignores json=, so the
generic audio transcription path never actually sent a JSON body. No provider
hit this before; JSON-body speech APIs need it.
* feat(vertex_ai): add Google Cloud Speech-to-Text Chirp 3 transcription support
Adds a VertexAIAudioTranscriptionConfig wired through ProviderConfigManager so
vertex_ai/chirp_3 works on /v1/audio/transcriptions (sync and async) via the
Speech-to-Text v2 recognize API. Auth reuses the standard Vertex credential
resolution (vertex_project/vertex_location/vertex_credentials or ADC); the
location defaults to the us multi-region since chirp_3 is only served from the
us and eu multi-regions, and non-global locations use the regional
<location>-speech.googleapis.com host. Maps language to languageCodes (auto
language detection by default), joins all result alternatives into the
transcript, and tracks cost from totalBilledDuration with a
vertex_ai/chirp_3 price entry at Google's published $0.016/min.
* fix(vertex_ai): map bare ISO-639-1 language codes to BCP-47 for Speech-to-Text
OpenAI clients send language codes like "en", which Google rejects with 400
("not supported by the model chirp_3 in the location us"); Speech-to-Text
wants region-qualified BCP-47 like "en-US". Adds a shared
normalize_transcription_language_to_bcp47 helper in audio_utils (NVIDIA Riva's
transcription config already hand-rolled the same table privately) that maps
common bare codes and passes region-qualified ones through, and applies it in
the Vertex transcription request. Also narrows the response JSON parse guard
to ValueError.
* fix(vertex_ai): drop zero output_cost_per_second so chirp_3 cost tracking works
cost_per_second prefers output_cost_per_second whenever it is not None, so the
0.0 in the chirp_3 entry priced every transcription at $0.00 instead of using
input_cost_per_second. Remove it from both cost maps and pin the behavior with
a regression test computing 18s of chirp_3 audio to ~$0.0048.
* fix(vertex_ai): validate client-controllable location to prevent SSRF in Speech-to-Text
get_complete_url interpolated vertex_location straight into the request host,
and vertex_location is client-controllable on the proxy (it flows from the
request body and is not on the request-body blocklist). An authenticated caller
could send vertex_location="attacker.example/" to point the host at their own
server, so the proxy would POST the audio plus its admin-minted Google bearer
token and x-goog-user-project header to the attacker, exfiltrating a
cloud-platform-scoped OAuth token minted from the admin's credentials.
Factor the location validation the rest of vertex_ai already applied in
get_vertex_base_url (^[a-z][a-z0-9-]*$ plus the global allowance) into a shared
validate_vertex_location helper in common_utils and call it from both the chat
host builder and the new speech host builder. Invalid locations now raise a 400
VertexAIError instead of building a host. Also reject vertex_project values that
carry URL-structural characters, since it lands in the URL path.
Regression tests assert on the parsed netloc so the security property is pinned:
valid locations always resolve to a *speech.googleapis.com host and injection
inputs are rejected.
* fix(vertex_ai): reject unsupported transcription response_format values instead of silently ignoring
* fix: zero out crash-class basedpyright rules across litellm/
* feat(lint): add LIT009 banning inert type: ignore comments
* docs: require bracketed rule and reason on every suppression
* chore(lint): ratchet budgets down and zero crash-class pyright limits
* fix: narrow auto router routelayer through a local before calling
* test: add regression tests for crash-class fixes
* fix: drop dead AZURE_AD_TOKEN lookups and word-bound the type-ignore regex
Pass transcription_cost through additional_costs so cost_breakdown's
input_cost + output_cost + additional_costs sums to total_cost instead
of silently folding it into total_cost only.
Co-authored-by: Cursor <cursoragent@cursor.com>
