OpenAI: Whisper Large V3

2 providers live

openai/whisper-large-v3

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2 live providers

Whisper Large V3 is OpenAI's open-source automatic speech recognition model offering both audio transcription and translation. It supports 99+ languages and accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg. With 1,550M parameters, it achieves a 10.3% word error rate and is well-suited for noise-robust, multilingual transcription in demanding conditions.

Input: $7.50; per million seconds of audio

Providers

Ranked by price and speed. Requests go to the best healthy provider, then fail over down the list.

#	Provider	Input
1	DeepInfraNon-EU Data policy not declared	$7.50/M seconds
2	ScalewayEU Data policy not declared	$57.6182/M seconds

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Price history

Input price since Oct 23, 2026, in USD per million seconds of audio.

DeepInfra
Scaleway

Data table

Provider	From	Input
DeepInfra	Oct 23, 2026	$7.50
Scaleway	Oct 23, 2026	$57.7058
Scaleway	Oct 23, 2026	$57.644
Scaleway	Oct 26, 2026	$57.6182

Questions

How much does OpenAI: Whisper Large V3 cost?

OpenAI: Whisper Large V3 costs $7.50 per million seconds of audio at DeepInfra, the provider requests go to first. Each provider sets its own price, and you pay the price of the provider that answered.

Do providers train on my prompts?

Each provider declares it, and the table above shows every declaration. You can limit a request to providers that keep nothing, or to providers in the EU.

Can I serve OpenAI: Whisper Large V3 myself?

Yes. Apply as a provider, add your endpoint and list this model at your price. It goes live once the automated check passes.

Similar models

OpenAI: Whisper Large V3 Turboopenai/whisper-large-v3-turbo

$3.33 per million seconds of audio. 1 provider.

Call it

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.llm.buzz/api/v1",
    api_key=os.environ["LLM_BUZZ_KEY"],
)
with open("call.mp3", "rb") as audio:
    transcript = client.audio.transcriptions.create(
        model="openai/whisper-large-v3",
        file=audio,
    )

import fs from "node:fs";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.llm.buzz/api/v1",
  apiKey: process.env.LLM_BUZZ_KEY,
});
const transcript = await client.audio.transcriptions.create({
  model: "openai/whisper-large-v3",
  file: fs.createReadStream("call.mp3"),
});

curl https://api.llm.buzz/api/v1/audio/transcriptions \
  -H "Authorization: Bearer $LLM_BUZZ_KEY" \
  -F model=openai/whisper-large-v3 \
  -F file=@call.mp3

Endpoints

POST /api/v1/audio/transcriptions

Authentication

Send your llm.buzz key as a Bearer token: Authorization: Bearer $LLM_BUZZ_KEY. Create keys in the console. The same key calls every model; each request pays the price of the provider that answered it.

More in Authentication and the API reference.

Call OpenAI: Whisper Large V3 with one keyEach request pays the price of the provider that answered it.

Get API key