Whisper Large v3
OpenAI · released Nov 7, 2023 · openai/whisper-large-v3
Speech to textTranscribes a recording into words
- Type
- Open weightsApache License 2.0
- Languages
- 99
- Size
- 1.5B
Context measured in tokens
Our take
Written Aug 2, 2026Whisper Large v3 turns recorded speech into written words, and is still the name most people reach for. It handles 99 languages and is small enough to run on a laptop, though newer models now beat it on accuracy.
Reach for it when the recording is clean and the language coverage matters — it gets about one word in sixty wrong on read-aloud audio, and handles far more languages than most alternatives. It is also small enough to run on your own machine rather than paying by the minute. Skip it if you are transcribing meetings or heavily accented speech, where its error rate rises more than eightfold.
The case for it
- 99 languages on one model, which very few transcription models match.
- Excellent on clean read-aloud audio, at about a quarter of its own average error rate.
- Runs on consumer hardware: it fits in memory on almost every device tracked.
The case against it
- Meeting audio is its weak spot, with more than eight times the error rate it manages on clean speech.
- Mid-table on headline accuracy now, at 54th of 74 on the Open ASR Leaderboard.
- Every accuracy figure here is English; nothing we hold measures the rest.
How good is it?
TranscriptionTurning speech into text2.5 of 5Open ASR WER · 54th of 74
93.4%
Misses roughly one word in 15, averaged over nine English test sets.
462×41st of 62
an hour of audio in 8 seconds, on the board's own hardware. Your machine will differ.
99
Listed on the model card. The accuracy above is English only.
Percentage of words wrong on each set, lower better. Bars are scaled to this model's own worst case, not to the board.
Which languages ↓ ↑
English · Chinese · German · Spanish · Russian · Korean · French · Japanese · Portuguese · Turkish · Polish · Catalan · Dutch · Arabic · Swedish · Italian · Indonesian · Hindi · Finnish · Vietnamese · Hebrew · Ukrainian · Greek · Malay · Czech · Romanian · Danish · Hungarian · Tamil · Norwegian · Thai · Urdu · Croatian · Bulgarian · Lithuanian · Latin · Māori · Malayalam · Welsh · Slovak · Telugu · Persian · Latvian · Bangla · Serbian · Azerbaijani · Slovenian · Kannada · Estonian · Macedonian · Breton · Basque · Icelandic · Armenian · Nepali · Mongolian · Bosnian · Kazakh · Albanian · Swahili · Galician · Marathi · Punjabi · Sinhala · Khmer · Shona · Yoruba · Somali · Afrikaans · Occitan · Georgian · Belarusian · Tajik · Sindhi · Gujarati · Amharic · Yiddish · Lao · Uzbek · Faroese · Haitian Creole · Pashto · Turkmen · Norwegian Nynorsk · Maltese · Sanskrit · Luxembourgish · Burmese · bo · Filipino · Malagasy · Assamese · Tatar · Hawaiian · Lingala · Hausa · ba · Javanese · Sundanese
The figures above come from the Open ASR Leaderboard, an independent public test that runs every model on the same recordings. It is the only measurement of transcription quality we know of, so there are no other scores to show.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.
Every published score for this model9 scoresEvery figure we hold, from 9 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 20.6 GB spare means a 10% error in the size would not change the answer.
Apple M1 Pro (16-core GPU) · 32 GB
Room to spare. 21.8 GB spare means a 10% error in the size would not change the answer.
Apple M2 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 3.8 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 2 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per minute of audio
- $0.002
- Context served
- —
- Throughput
- Not measured
| Provider | Price per minute of audio | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| Groq | $0.002 | not reported | not measured | Unknown | Unknown | Unknown |
| Deepgram | $0.006 | not reported | not measured | Unknown | Unknown | Unknown |
DeepgramAcross the 2 listings we hold: 0 say they do not train on prompts, 0 say they do and 2 do not say. 0 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
When we formed this view
Dates behind this page
Prices last checked 6h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- 2 of 2 listings publish no parameter list, so what their API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 2 of 2 listings do not say whether they train on prompts.
- We hold no cached-input rate for any of its listings.
Licence and identifiers
What the licence allowsApache License 2.0, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- openai/whisper-large-v3
- Architecture
- Dense
- Modality record
- audio->text
- Catalogue slug
- openai-whisper-large-v3