The /v1/messages bridge decided a Claude target could take `reasoning_effort` from
the model name, which says nothing about the params the provider in front of it
accepts. Snowflake serves Claude over the Anthropic dialect and declares `thinking`
alone, so `get_optional_params` raised `UnsupportedParamsError` before the request
reached the wire: every adaptive request carrying an effort tier turned a 200 into
a 400 for all seven of its Claude entries.
The tier is now offered only where the target declares the param, reading the same
`get_supported_openai_params` the sibling `_supports_prompt_cache_key` reads twelve
lines up. A target declaring neither carrier keeps its bare `thinking` block, which
is what this bridge sent before it carried a tier at all.
Without a resolved provider the tier stays behind rather than being offered blind.
Resolving one from the model's prefix instead would run an OAuth device flow for
github_copilot and chatgpt, blocking for minutes, and one of the two callers in that
position is a logging callback. The copilot case is pinned by a test.
/v1/messages forwarded `thinking` verbatim for a Claude-family model and then returned,
carrying `output_config.effort` only when the model string started with a Bedrock prefix.
Every other bridged provider got a bare adaptive thinking block, so the caller's effort did
nothing: max and minimal produced byte-identical upstream bodies.
Send those targets the tier as `reasoning_effort`, which is the param they take. Bedrock keeps
taking `output_config`, since the two are not interchangeable there: an application inference
profile ARN resolves to no chat config, so `reasoning_effort` is dropped and the tier vanishes,
and a provider that rebuilds `output_config` from it overwrites a caller-set `thinking.display`
on the way. The tier stays a plain string, the summary already travelling inside the forwarded
`thinking` block. Adaptive with no tier, and budgeted thinking, both stay exactly as they were.
Kimi K3 accepts exactly low, high and max, defaults to max, and always thinks.
The map could not say that: medium and high have no supports_*_reasoning_effort
flag because every other reasoning model takes them, so the ten kimi-k3 entries
carried supports_reasoning alone and resolved to unknown. The dashboard then fell
back to a capability-blind level list that deliberately omits max, which is why a
kimi-k3 tier cannot be set to max thinking today.
Add reasoning_effort_levels, an array key in the shape the map already uses for
supported_endpoints and supported_modalities. Where present it is read first and
wins whole; every other entry keeps answering through the per-level flags,
unchanged. It is deliberately a different name from the computed
ModelGroupInfo.supported_reasoning_efforts, which stays derived from a group's
deployments and is never seeded from one deployment's model_info.
The levels are per entry rather than per model, because the deployments differ:
Moonshot, Together, Fireworks and Azure Foundry all forward the level unchanged
and get the model's own low/high/max, while Perplexity documents a six-value
enum it maps down internally and gets that. The /v1/messages degradation chain
consults the same declaration, so the level the map advertises is the level that
path forwards.
Extract the Soniox SRT/VTT cue grouping and rendering into a shared
litellm_core_utils/audio_utils/subtitle_utils module, have Gemini
transcription request word timestamps whenever response_format is srt or
vtt, and let the http handler rewrite the response text into the
synthesized subtitle document (dropping the internally requested words
array) for any provider config that opts in via
supports_subtitle_synthesis
Gemini Live sends no usageMetadata and no turnComplete for
gemini-3.5-transcribe-live sessions, so realtime spend logged as 0.0.
Attach estimated usage to the input_audio_transcription.completed event
using Google's published billing estimate (25 audio tokens/sec of input,
175 text tokens/min of output) derived from the streamed pcm16 audio
duration, gated to audio_transcription-mode models so conversational
Live models keep billing through usageMetadata. Also capture that usage
in the provider_config backend path so realtime cost calculation sees it.
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.
OpenAI's chat completions API rejects tool_reference content parts in
role tool messages, so a mixed text plus reference tool result carried
through the Anthropic adapter turned a previously working request into
a 400 on chat-routed OpenAI and Azure deployments. Strip the reference
parts there, keeping a reference-only result as an empty-text tool
message so the preceding tool_call stays answered, mirroring the
Responses bridge skip.
For non-Anthropic models served over /v1/messages, the outer wrapper recomputes
cost over the adapter-translated Anthropic response dict. That dict dropped every
web search usage signal, so the recompute overwrote the correct cost breakdown
with a token-only one: x-litellm-response-cost-tool-usage read 0.0 and
x-litellm-response-cost-original excluded the search cost, while the total kept it.
The adapter now maps web search request counts (from Usage.server_tool_use or
Gemini's prompt_tokens_details) into usage.server_tool_use.web_search_requests,
matching the Anthropic API shape, and the Gemini web search cost calculator falls
back to server_tool_use when prompt_tokens_details carries no count. The shared
get_web_search_requests helper is now public since five modules consume it.
Resolves LIT-6288
get_fireworks_session_id fell back to litellm_trace_id when no session id was
given. That id is generated per request (uuid4 when absent), so x-session-affinity
carried a different value every time and Fireworks prompt caching never hit;
cached_tokens stayed 0 across identical prompts.
The None path the original change described was effectively unreachable because
of it. Drop the fallback so affinity comes only from an id the caller actually
supplied: litellm_session_id, session_id, or metadata.session_id.
Callers who were relying on a trace id for affinity can pass litellm_session_id
instead, which is stable across the requests they want grouped.
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Gemini API Maps-grounded prompts were billed as web search and Vertex AI Maps-grounded prompts were not billed at all. Classify grounding metadata per candidate into web search vs Maps requests, carry a distinct google_maps_grounding_requests usage counter through non-streaming and streaming paths, and price it via the new google_maps_grounding_cost_per_query cost map key with per-query and per-prompt defaults keyed off web_search_billing_unit. Fixes#35906
Live probes against every gemini_native_audio model on both providers show
setup accepts a valid prebuilt voice and 1007s only unknown voice names, so
the strip predicate rested on a false premise and silently discarded the
client's voice on AI Studio native-audio sessions
