SAVRN Model Hub · Comparisons
seamless-m4t-v2-large vs whisper-large-v3
Seamless-m4t-v2-large has 2.3B parameters and whisper-large-v3 has 1.5B parameters; seamless-m4t-v2-large is released under Creative Commons Attribution-NonCommercial 4.0 and whisper-large-v3 under Apache License 2.0; at 16-bit, seamless-m4t-v2-large needs about 5.5 GB (1x MI300X from $1.85 an hour) and whisper-large-v3 about 3.7 GB (1x MI300X from $1.85 an hour).
| Field | seamless-m4t-v2-large facebook/seamless-m4t-v2-large | whisper-large-v3 openai/whisper-large-v3 |
|---|---|---|
| Publisher | AI at Meta | OpenAI |
| Task | Speech recognition | Speech recognition |
| Modality | Audio | Audio |
| Parameters, as reported | 2.3B parameters | 1.5B parameters |
| Architecture | SeamlessM4Tv2Model | WhisperForConditionalGeneration |
| Library | transformers | transformers |
| Context length | 4,096 tokens | Not stated |
| Repository size | 29.9 GB | 24.7 GB |
| Artifact formats | safetensors | safetensors, pytorch, jax |
| License | cc-by-nc-4.0 | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 5.5 GB | 3.7 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 1.4 GB | 0.9 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 5f8cc790b19f | 06f233fe06e7 |
| Downloads reported by the hub | 306.7k | 3.7M |
| Last observed | 2026-09-18 | 2026-10-16 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
whisper-large-v3
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| ARTPARK-IISc/Vaani-Benchmark-V1.0 | Task Hindi_WERMetric Hindi_WERComparison conditions not established | 26.8 | Not named Reported by a third party |
Evaluated revision not stated | 2026-06-26 |
| hf-audio/open-asr-leaderboard | Task ami_werMetric ami_werComparison conditions not established | 15.95 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task earnings22_werMetric earnings22_werComparison conditions not established | 11.29 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task gigaspeech_werMetric gigaspeech_werComparison conditions not established | 10.02 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task librispeech_clean_werMetric librispeech_clean_werComparison conditions not established | 2.01 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task librispeech_other_werMetric librispeech_other_werComparison conditions not established | 3.91 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task mean_werMetric mean_werComparison conditions not established | 7.44 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task rtfxMetric rtfxComparison conditions not established | 145.51 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task spgispeech_werMetric spgispeech_werComparison conditions not established | 2.94 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task tedlium_werMetric tedlium_werComparison conditions not established | 3.86 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
| hf-audio/open-asr-leaderboard | Task voxpopuli_werMetric voxpopuli_werComparison conditions not established | 9.54 | open-asr-leaderboard Reported by a third party |
Evaluated revision not stated | 2023-11-07 |
SAVRN's Notes on seamless-m4t-v2-large
SeamlessM4T v2 does speech and text translation in nearly 100 languages in one model: speech to text, speech to speech, text to speech and text to text. At 2.3B parameters it needs about 6 GB at 16-bit.
The license is CC-BY-NC-4.0, which rules out commercial use. For research, nonprofit and accessibility work it is uniquely broad. For commercial speech translation, combine a permissively licensed recognizer such as Whisper with a translation model.
SAVRN's Notes on whisper-large-v3
Whisper large-v3 is the reference open speech recognition model: 1.5B parameters, trained on more than 5 million hours of audio, transcribing and translating speech across dozens of languages. About 4.8 million downloads a month and more than 6,300 likes make it one of the most used models anywhere.
It needs about 4 GB at 16-bit, so it runs on nearly any GPU. Apache 2.0 allows commercial use. Whisper large-v3 turbo in our catalog is much faster with a small accuracy cost; choose between them on your own audio.
Questions
Which is larger, seamless-m4t-v2-large or whisper-large-v3?
seamless-m4t-v2-large (2.3B parameters) is larger than whisper-large-v3 (1.5B parameters), by the parameter counts their publishers report.
Which is cheaper to run, seamless-m4t-v2-large or whisper-large-v3?
At 4-bit, seamless-m4t-v2-large fits on 1x MI300X from $1.85 an hour and whisper-large-v3 on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use seamless-m4t-v2-large commercially?
Not without separate permission. seamless-m4t-v2-large is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.
Can I use whisper-large-v3 commercially?
Yes. whisper-large-v3 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.