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* fix(cost-map): add mistral voxtral 3 transcription and tts rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(cost-map): add azure_ai phi-4 family retirement dates Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(cost-map): add elevenlabs v4 and flash/turbo tts rows at API rates and together minimax-m3.1-flash Co-authored-by: Shebin Sabu <85486313+shebin09@users.noreply.github.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore(cost-map): credit elevenlabs api pricing from #47917 Co-authored-by: Shebin Sabu <85486313+shebin09@users.noreply.github.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Shebin Sabu <85486313+shebin09@users.noreply.github.com>
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
"""ElevenLabs text-to-speech prices match the published API rates.
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https://elevenlabs.io/pricing/api lists "v3" and "v2 Multilingual" at $0.08 and
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"Flash / Turbo" at $0.04, each "Price per 1K characters", and says API usage is
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billed in US dollars, not credits. The map had v3 and Multilingual v2 at a
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Scale-plan credit estimate ($0.18 per 1K characters) and no Flash rows.
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"""
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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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REPO_ROOT = Path(__file__).parents[2]
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PRICES = {
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"elevenlabs/eleven_v3": 8e-05,
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"elevenlabs/eleven_multilingual_v2": 8e-05,
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"elevenlabs/eleven_flash_v2_5": 4e-05,
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"elevenlabs/eleven_flash_v2": 4e-05,
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}
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@pytest.fixture(autouse=True)
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def local_model_cost_map(monkeypatch):
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"""Never depend on the published cost map having these rows yet."""
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monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
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monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
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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, per_character", PRICES.items())
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def test_metadata_and_backup(model, per_character):
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main = json.loads((REPO_ROOT / "model_prices_and_context_window.json").read_text())
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backup = json.loads((REPO_ROOT / "litellm/model_prices_and_context_window_backup.json").read_text())
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assert main[model] == backup[model]
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info = litellm.get_model_info(model)
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assert info["litellm_provider"] == "elevenlabs"
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assert info["mode"] == "audio_speech"
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assert info["input_cost_per_character"] == per_character
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@pytest.mark.parametrize("model, per_character", PRICES.items())
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def test_speech_cost_is_charged_per_character(model, per_character):
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cost = litellm.completion_cost(model=model, call_type="speech", prompt="x" * 1000)
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assert cost == pytest.approx(per_character * 1000)
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