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feat(mistral): register voxtral models in the cost map
Voxtral was absent from the cost map, so mistral/* on the proxy expanded to a model list without it and /v1/models never surfaced the audio models. Adds voxtral-mini-2602 / voxtral-mini-latest as mode=audio_transcription priced at $0.003/min against /v1/audio/transcriptions, and voxtral-small-2507 / voxtral-small-latest as an audio-capable chat model at $0.1/$0.3 per MTok. Names, aliases, context windows and capabilities come from a live GET https://api.mistral.ai/v1/models; the realtime transcription and TTS voxtral variants are left out since LiteLLM has no route for them yet.
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Devin AI
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@ -28066,6 +28066,52 @@
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"mistral/voxtral-mini-2602": {
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"input_cost_per_second": 5e-05,
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"litellm_provider": "mistral",
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"max_input_tokens": 16384,
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"mode": "audio_transcription",
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"mistral/voxtral-mini-latest": {
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"input_cost_per_second": 5e-05,
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"litellm_provider": "mistral",
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"max_input_tokens": 16384,
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"mode": "audio_transcription",
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"mistral/voxtral-small-2507": {
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"input_cost_per_token": 1e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 32768,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"output_cost_per_token": 3e-07,
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-small-25-07",
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"supports_audio_input": true,
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"mistral/voxtral-small-latest": {
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"input_cost_per_token": 1e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 32768,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"output_cost_per_token": 3e-07,
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-small-25-07",
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"supports_audio_input": true,
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"moonshot.kimi-k2-thinking": {
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"input_cost_per_token": 6e-07,
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"litellm_provider": "bedrock_converse",
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@ -28141,6 +28141,52 @@
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"supports_tool_choice": true,
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"supports_vision": true
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},
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"mistral/voxtral-mini-2602": {
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"input_cost_per_second": 5e-05,
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"litellm_provider": "mistral",
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"max_input_tokens": 16384,
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"mode": "audio_transcription",
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"mistral/voxtral-mini-latest": {
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"input_cost_per_second": 5e-05,
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"litellm_provider": "mistral",
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"max_input_tokens": 16384,
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"mode": "audio_transcription",
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-26-02",
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"supported_endpoints": [
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"/v1/audio/transcriptions"
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]
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},
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"mistral/voxtral-small-2507": {
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"input_cost_per_token": 1e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 32768,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"output_cost_per_token": 3e-07,
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-small-25-07",
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"supports_audio_input": true,
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"mistral/voxtral-small-latest": {
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"input_cost_per_token": 1e-07,
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"litellm_provider": "mistral",
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"max_input_tokens": 32768,
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"max_output_tokens": 32768,
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"max_tokens": 32768,
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"mode": "chat",
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"output_cost_per_token": 3e-07,
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"source": "https://docs.mistral.ai/models/model-cards/voxtral-small-25-07",
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"supports_audio_input": true,
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"supports_function_calling": true,
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"supports_tool_choice": true
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},
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"moonshot.kimi-k2-thinking": {
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"input_cost_per_token": 6e-07,
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"litellm_provider": "bedrock_converse",
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79
tests/test_litellm/test_mistral_voxtral_models.py
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79
tests/test_litellm/test_mistral_voxtral_models.py
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@ -0,0 +1,79 @@
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import json
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from pathlib import Path
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import pytest
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import litellm
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from litellm.proxy.auth.model_checks import get_provider_models
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from litellm.types.utils import TranscriptionResponse
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TRANSCRIPTION_MODELS = ("mistral/voxtral-mini-2602", "mistral/voxtral-mini-latest")
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CHAT_MODELS = ("mistral/voxtral-small-2507", "mistral/voxtral-small-latest")
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ALL_VOXTRAL_MODELS = TRANSCRIPTION_MODELS + CHAT_MODELS
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def _load_cost_maps() -> tuple[dict, dict]:
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repo_root = Path(__file__).parents[2]
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with open(repo_root / "model_prices_and_context_window.json") as f:
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main_cost = json.load(f)
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with open(repo_root / "litellm" / "model_prices_and_context_window_backup.json") as f:
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backup_cost = json.load(f)
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return main_cost, backup_cost
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@pytest.fixture
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def local_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
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local_map = litellm.get_model_cost_map(url="")
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monkeypatch.setattr(litellm, "model_cost", local_map)
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litellm.add_known_models(local_map)
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litellm.get_model_info.cache_clear()
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yield
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litellm.get_model_info.cache_clear()
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@pytest.mark.parametrize("model", TRANSCRIPTION_MODELS)
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def test_voxtral_transcription_models_are_priced_per_second(model: str) -> None:
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main_cost, _ = _load_cost_maps()
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info = main_cost[model]
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assert info["mode"] == "audio_transcription"
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assert info["supported_endpoints"] == ["/v1/audio/transcriptions"]
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assert info["input_cost_per_second"] == pytest.approx(0.003 / 60)
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assert "output_cost_per_second" not in info
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@pytest.mark.parametrize("model", CHAT_MODELS)
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def test_voxtral_small_is_an_audio_capable_chat_model(model: str) -> None:
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main_cost, _ = _load_cost_maps()
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info = main_cost[model]
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assert info["mode"] == "chat"
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assert info["supports_audio_input"] is True
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assert info["input_cost_per_token"] == pytest.approx(1e-07)
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assert info["output_cost_per_token"] == pytest.approx(3e-07)
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@pytest.mark.parametrize("model", ALL_VOXTRAL_MODELS)
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def test_backup_cost_map_matches_main(model: str) -> None:
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main_cost, backup_cost = _load_cost_maps()
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assert backup_cost.get(model) == main_cost.get(model)
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def test_voxtral_models_are_discoverable_for_the_mistral_wildcard(local_cost_map: None) -> None:
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"""Regression for #34616: `mistral/*` on the proxy expands to the provider's known models,
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so voxtral was missing from /v1/models until it landed in the cost map."""
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provider_models = get_provider_models(provider="mistral") or []
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assert set(ALL_VOXTRAL_MODELS).issubset(set(provider_models))
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def test_voxtral_transcription_cost_is_billed_per_audio_second(local_cost_map: None) -> None:
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response = TranscriptionResponse(text="demo text")
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response.duration = 90.0
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cost = litellm.completion_cost(
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completion_response=response,
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model="mistral/voxtral-mini-latest",
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custom_llm_provider="mistral",
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call_type="atranscription",
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)
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assert cost == pytest.approx(90.0 * 0.003 / 60)
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