Submagic vs Whisper
Submagic
submagic.co
Whisper
github.comSide by side
| Figure | Submagic | Whisper |
|---|---|---|
| Price from | $12/mo | $0.01/mo |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 6 | 3 |
| Spec score · AI Video Caption Generators | 9.0 (#5) | 9.5 (#1) |
| API rate limits | Published limits are 1,000 requests/hour for lightweight operations, 500 for retrieval and uploads, and 50 for exports. | The documented Free tier allows 3 requests per minute and 200 requests per day, while Build allows 2,500 requests per minute. |
| Company legal name | The platform publisher is TURBO STUDIO S.A.S., a French simplified joint-stock company registered in Paris. | Crunchbase lists the legal name as OpenAI OpCo, LLC and also lists OpenAI LP and OpenAI Group PBC as alternate names. |
| Rating on G2 | 4.6 out of 5 from 85 reviews | 4.4 out of 5 from 52 reviews. |
| What it does | Submagic is a browser-based AI editor for creating short-form videos with captions, B-roll and visual effects. | Whisper is a general-purpose speech-recognition model that supports multilingual transcription, speech translation and language identification. |
Plans
Submagic
- Starter$12 a month
- Pro$23 a month
- Business$41 a month
- Magic Clips add-on$19 a month
- Business + API$41 a month
- Custom PlanPriced on request
Whisper
- Self-hosted WhisperFree
- Whisper (open-source code and model weights)Free
- Whisper API$0.01 a month
In their own words
Whisper
Whisper is an open-source speech recognition model for transcription, speech translation, language identification, and voice activity detection. Its documented command-line and Python interfaces process audio files, and it can translate speech from multiple languages into English. The repository lists six model sizes—tiny, base, small, medium, large, and turbo—with different speed and memory tradeoffs; stated VRAM needs range from about 1 GB to 10 GB, depending on size. Installation is documented through pip and requires the ffmpeg command-line tool. Rust may be needed if a prebuilt tiktoken wheel is unavailable. The code and model weights are released under the MIT License, with local installation using Python and ffmpeg. Whisper is listed for Linux, macOS, Windows, and self-hosted deployment. OpenAI says it was trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Performance varies widely by language, and it does not outperform models specialized for LibriSpeech. Turbo is not trained for translation; multilingual models are recommended for translating non-English speech into English.