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.
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
Gemini 2.5 Flash Preview TTS, Gemini 2.5 Pro Preview TTS, and the three
gemini-2.5-flash-native-audio entries carried rates copied from the text
models, so audio output was billed 2x to 6x under Google's published
prices. Set the published per-token rates on all ten keys, add
output_cost_per_audio_token to the native-audio entries, and drop the
long-context tier rates Google does not publish for Pro TTS.
OpenAI GPT-5.x models on Bedrock Converse expect reasoning effort under
additionalModelRequestFields as {"reasoning": {"effort": ...}}. They were
falling into the Anthropic branch and emitting a `thinking` block, which
Converse rejects with unknown_parameter.
The bedrock_converse gpt-5.6 entries were also missing supports_reasoning,
so reasoning_effort was dropped before mapping. Setting the flag lets the
existing config-driven supported-params path accept it, rather than adding
another model-name branch.
Backfill 21 serverless chat models, the multilingual-e5 embedding model, and
Llama-Guard-4-12B from the live Together catalog with per-token pricing and
capability flags. Mark 25 delisted together_ai entries with their documented
deprecation_date and point superseded models at a live successor via metadata.
Reprice Llama-3.3-70B-Instruct-Turbo to Together's current rate.
Address Greptile review comments and the strict lint budgets:
- read supports_adaptive_thinking from the model cost map instead of
substring-matching the model name, so aliases and newly onboarded
adaptive-only models need no code change
- add tencent/minimax-m3 to the pricing JSON (and backup), which also
fixes cost tracking for the model
- type the thinking/extra_body payloads with ReadOnly TypedDicts
- build the merged extra_body without rebinding or in-place mutation
Six defects in the RunwayML video provider:
- transform_video_create_request hardcoded /image_to_video, so text-to-video 400'd and video-to-video was unreachable; the endpoint is now selected from the inputs present (promptVideo/videoUri, promptImage, or text only)
- get_error_class raised instead of returning, turning a provider 4xx into a proxy 500 APIConnectionError; it now returns a RunwayMLError
- VideoObject.progress was typed int while Runway sends a 0..1 float, 500'ing status polls while RUNNING; it is now scaled to a 0..100 percent
- custom per-deployment pricing stored under litellm_metadata was ignored for video; the deployment model_info lookup now checks both metadata keys
- stale cost-map entries (gen3a_turbo, gen4_aleph) were removed and current models added, with output_cost_per_second_480p/_4k tier keys plumbed through the model-info and router types
- video cost now falls back to Runway's estimatedCost from the create response when no custom pricing is configured, and custom pricing always wins over it
Fixes#36483
Databricks documents databricks-claude-fable-5 as accepting text only,
where every sibling Claude endpoint accepts text and image. An earlier
commit flipped the flag to true on the reasoning that fable-5 was the
only entry of its generation declaring false, which had it backwards:
it is the only one because the endpoint really does refuse images.
Advertising vision here would surface the model in capability filters
and hand the caller a provider-side rejection.
