Speech-to-text AI models: two built purely for transcription
| # | AI model | Capabilities | API $/1M (in / out) | Context window | Price / mo | Output $/1M | Overview | Visit site |
|---|---|---|---|---|---|---|---|---|
| 1 | Gemini 3.5 Transcribe Google | API only | View | |||||
| 2 | Gemini 3.5 Transcribe Live Google | API only | View |
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Two purpose-built transcription models are listed: Gemini 3.5 Transcribe for recorded files and Gemini 3.5 Transcribe Live for streaming. Both handle automatic language detection, speaker diarisation, word-level timestamps and custom vocabulary.
Transcription is priced by audio duration or audio tokens rather than by words, which makes it far cheaper than reasoning over the same recording. The live variant costs more per minute and exists where text must appear as the person speaks: captions, live notes, voice commands. For meeting records, batch transcription afterwards is the economical path.
Models with this capability
The 2 models below all support this capability. Prices are input and output per 1M tokens from the vendor official price list; for dirhams multiply by 3.6725. Tap a card to open the model page.
How to choose
- Choose batch unless the text must appear as the person speaks.
- Test on your real audio: accents, overlap and background noise decide accuracy.
- Supply a custom vocabulary for names, products and jargon.
- Check language detection if your recordings are mixed.
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Which is right for meetings?
How accurate is it on accented English?
Can it handle two languages in one recording?
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