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voxtral-tts: bill speech at the model's published rate ($16/1M characters) #4

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inference-bot wants to merge 1 commits from fix/voxtral-tts-pricing into main
pull from: fix/voxtral-tts-pricing
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config.yaml
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@@ -148,7 +148,13 @@ model_list:
# models: the member portal's /v1/models filters them out of the chat model # models: the member portal's /v1/models filters them out of the chat model
# list (see AUDIO_MODEL_PREFIXES there) so members don't see them in the # list (see AUDIO_MODEL_PREFIXES there) so members don't see them in the
# picker, but they remain fully callable via /v1/audio/* for API users. # picker, but they remain fully callable via /v1/audio/* for API users.
# whisper-large-v3-turbo: $0.05/1M input tokens. voxtral-tts: listed $0/$0. # whisper-large-v3-turbo: $0.05/1M input tokens. voxtral-tts: Tinfoil's
# catalog lists it $0/$0 and every cost field was left NULL, so speech usage
# never consumed member allowance. Billed at the model's published rate
# instead: $16/1M characters, i.e. LiteLLM's own bundled price for the same
# weights (mistral/voxtral-mini-tts-*, sourced to Mistral's model card) and
# the rate OpenAI's TTS entries use for the same field. Revisit if the
# Tinfoil invoice shows a different number.
# NOTE: LiteLLM routes audio by the openai/ provider + the endpoint called # NOTE: LiteLLM routes audio by the openai/ provider + the endpoint called
# (/v1/audio/transcriptions or /v1/audio/speech) — there is no # (/v1/audio/transcriptions or /v1/audio/speech) — there is no
# "audio_transcription/" provider prefix. # "audio_transcription/" provider prefix.
@@ -168,6 +174,10 @@ model_list:
api_key: os.environ/TINFOIL_API_KEY api_key: os.environ/TINFOIL_API_KEY
model_info: model_info:
mode: audio_speech mode: audio_speech
# $16 per 1M characters. This is the field LiteLLM reads for speech on the
# openai adapter (same key OpenAI's tts-1 uses upstream), so member spend
# and budget caps now account for voice mode.
input_cost_per_character: 0.000016
# --- Embedding model (Tinfoil, in-TEE) --- # --- Embedding model (Tinfoil, in-TEE) ---
# nomic-embed-text — 768-dim text embeddings for RAG/semantic search. # nomic-embed-text — 768-dim text embeddings for RAG/semantic search.