Open SourceOpenAI

Whisper V3 Large

Name: Whisper V3 Large
Availability: InStock
Author: OpenAI

Whisper V3 Large is OpenAI's automatic speech recognition model, used for multilingual audio transcription and speech translation into English.

License Open Source

From

$0.0034 / minute

across 1 provider

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API Pricing

Provider	Price /min	Updated	Source
Scaleway	$0.0034/min	11/2/2026	Visit

Prices updated daily. Last check: Nov 2, 2026

Model Details

General

Creator: OpenAI
Modalities: Text

Capabilities

Open Source: Yes
Aliases: Whisper Large V3, whisper-large-v3, openai/whisper-large-v3

Strengths & Limitations

Strengths

Open weights, so the same model can be run self-hosted or bought from many inference providers without vendor lock-in
Multilingual transcription across a broad set of languages from a single model, with automatic language detection
Can translate non-English speech directly into English text in one pass
Largest Whisper checkpoint, generally more accurate on accented and noisy audio than the smaller tiny/base/small/medium variants
Widely supported tooling ecosystem (Hugging Face Transformers, faster-whisper/CTranslate2, whisper.cpp and similar runtimes) for CPU, GPU, and quantized deployment
Provider competition on the same open checkpoint means transcription-per-minute pricing can be compared directly across hosts
Commonly served with segment timestamps, making it practical for subtitle and caption workflows

Limitations

Designed for batch/file transcription rather than native low-latency streaming; real-time use requires chunking logic in the serving layer
No built-in speaker diarization — separating speakers requires an additional model or pipeline stage
Processes audio in short fixed windows, so long-form accuracy depends on the provider's chunking and stitching implementation
Can hallucinate text during silence, music, or very noisy segments, a known failure mode of the Whisper family
Accuracy varies substantially by language; lower-resource languages perform worse than English
Higher compute cost and slower inference than the smaller Whisper checkpoints for the same audio length

Key Features

•Automatic speech recognition (audio in, text out)

•Speech-to-English translation mode

•Automatic spoken-language identification

•Multilingual coverage across a broad set of languages

•Segment-level timestamp output (provider dependent)

•Prompt/initial-text conditioning to bias vocabulary and formatting

•Open weights suitable for self-hosting and quantized runtimes

•Largest checkpoint in the Whisper model family

About Whisper V3 Large

Whisper V3 Large (also referenced as whisper-large-v3 or openai/whisper-large-v3) is a speech-to-text model from OpenAI and the largest configuration in the Whisper family. Whisper models are encoder-decoder transformers trained on large volumes of weakly supervised audio paired with text, and the Large variant sits above the smaller tiny/base/small/medium checkpoints in the same lineage. Because OpenAI published Whisper weights openly, the model is served by many inference providers as well as run locally, which is why it appears with several provider-specific identifiers. As a speech recognition model, Whisper V3 Large takes audio as input and returns text. It supports transcription in a wide range of languages and can also translate speech directly into English text, along with automatic language identification. Audio is processed in fixed-length windows internally, so long recordings are handled by chunking or sequential decoding in the serving stack rather than by a token context window in the sense used for chat models. Providers commonly expose optional features on top of the base model, such as segment-level timestamps, prompt or initial-text conditioning, and word-level timing via alignment; the exact set varies by provider, and we do not track per-provider feature matrices here. In practice Whisper V3 Large is used for podcast and meeting transcription, subtitle and caption generation, call-center and voice-note processing, and as the transcription stage in voice agent pipelines. It is generally chosen over smaller Whisper checkpoints when accuracy on accented, noisy, or non-English audio matters more than throughput or cost, and it competes with newer purpose-built ASR APIs that add native diarization or streaming. Billing for speech-to-text is usually based on audio duration rather than tokens, so compare providers using the pricing table on this page.

Common Use Cases

Whisper V3 Large fits workloads where transcription accuracy matters more than latency: transcribing podcasts, interviews, lectures, and recorded meetings; generating subtitles and closed captions with segment timestamps; producing searchable text archives from large audio or video libraries; and processing multilingual voice notes or support calls where the input language is not known in advance. Its translation mode makes it useful for turning non-English recordings into English transcripts in a single step. It is also frequently used as the speech-to-text stage in voice assistant and agent pipelines, though builders targeting conversational latency often pair it with chunked streaming logic or choose a smaller Whisper checkpoint or a purpose-built streaming ASR service instead. For tasks needing speaker labels, sentiment, or summarization, Whisper handles the transcription step and downstream models handle the rest.

Frequently Asked Questions

How much does Whisper V3 Large cost to use?

Pricing depends on the provider and the billing model — speech-to-text is usually charged per minute or per hour of audio processed, and some hosts also offer batch or self-hosted GPU options priced by compute time. Because the weights are open, the same model is offered by multiple providers at different rates. Check the pricing table on this page for current per-provider pricing.

What is Whisper V3 Large best used for?

Batch transcription of recorded audio where accuracy is the priority: podcasts, meetings, interviews, lectures, and media archives, plus subtitle generation and multilingual transcription or speech-to-English translation.

How does it differ from the smaller Whisper models?

Whisper V3 Large is the largest checkpoint in the family. It generally produces more accurate transcripts, especially on accented, noisy, or non-English audio, at the cost of slower inference and higher compute requirements than the tiny, base, small, and medium variants.

Does Whisper V3 Large support real-time streaming transcription?

It was designed for file-based transcription over short fixed audio windows, not native streaming. Near-real-time use is possible by feeding overlapping audio chunks, but latency and stability depend on the serving implementation rather than the model itself.

Can it identify who is speaking?

No — diarization is not part of the model. Speaker labels require an additional diarization model or a provider pipeline that combines Whisper output with speaker segmentation.

Can I run Whisper V3 Large myself instead of using an API?

Yes. OpenAI released Whisper weights openly, and the model runs through Hugging Face Transformers as well as optimized runtimes such as faster-whisper/CTranslate2 and whisper.cpp, including quantized CPU builds. Self-hosting shifts the cost to GPU or CPU rental, which you can compare against per-minute API rates.