Vosk vs Whisper
Both are alternatives to Deepgram. Here's how they stack up — verified facts, no spin.
Also searched as Whisper vs Vosk — same comparison, one verdict.
Vosk
Real-time transcription on a CPU. Runs on a Raspberry Pi.
Vosk is a lightweight offline speech-recognition toolkit built for constrained environments — it runs in real time on a CPU, works on Android, iOS and single-board computers, and its models are tens of megabytes rather than gigabytes. It will not match Whisper on accuracy for difficult audio. It is the right answer when you need speech recognition on a device, offline, with no GPU and no network, which is a requirement no managed API can satisfy at any price. Apache-2.0.
Whisper
The model that changed the category. MIT, and free to run forever.
Whisper is OpenAI's speech-recognition model, released openly under MIT — weights included. It handles around a hundred languages, is robust to accents and background noise, and does translation as well as transcription. Releasing it is what collapsed the economics of this category: a model competitive with the commercial APIs became something anyone could download. The reference implementation is slower than the optimised runtimes, but it is the simplest thing that works and the baseline everything else is measured against.
Side by side
| Vosk | Whisper | |
|---|---|---|
| Sovereignty Score | 95 | 94 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | MIT |
| Pricing | Free, Apache-2.0. | Free — model weights and code both MIT. |
Vosk edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Vosk
Strengths
- +Real-time on CPU — no GPU required at all
- +Tiny models; runs on phones and single-board computers
- +Fully offline, which some products require by law
- +Around twenty languages supported
Trade-offs
- −Noticeably less accurate than Whisper on hard audio
- −Smaller language coverage
- −No built-in diarization
Whisper
Strengths
- +Genuinely open weights under MIT, not a restricted community licence
- +About a hundred languages, robust to noise and accent
- +Reference implementation — simplest possible starting point
- +Translation to English included
Trade-offs
- −Slower and hungrier than faster-whisper for identical output
- −Weak word-level timestamps without help
- −No speaker diarization
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.