Decision summary
- Bidirectional live captions / voice agents on Gemini API -> Gemini 3.5 Transcribe
- Air-gapped or pinable MIT weights -> Whisper large-v3
- Up to three-speaker attribution in-product -> Gemini 3.5 Transcribe
- Speech translation as a Whisper feature -> Whisper large-v3
- Microsoft ASR instead -> MAI-Transcribe vs Whisper
Speech-to-text comparison
Frontier comparisonGemini 3.5 Transcribe vs Whisper large-v3
Gemini 3.5 Transcribe and Whisper large-v3 are the current Google-hosted vs OpenAI-open ASR pair.
Featured · Updated 6 weeks ago · Last verified: September 2026
Choose Gemini 3.5 Transcribe when
Live voice agents, captioning, and meeting or call-log transcription on Google surfaces.
Choose Whisper large-v3 when
Self-hosted or partner-API transcription and translation pipelines you can pin and evaluate.
- Bidirectional live captions / voice agents on Gemini API: Gemini 3.5 Transcribe
- Air-gapped or pinable MIT weights: Whisper large-v3
- Up to three-speaker attribution in-product: Gemini 3.5 Transcribe
Decision axes: Best for · Access model · Live vs batch · Languages
Reasoning
Not an LLM comparison; evaluate WER, latency, and formatting on your audio.
Coding
Both show up in voice-agent stacks. Test the exact SDK and chunking path.
Multimodal
Audio in, text out for both. Gemini adds productized speaker and vocabulary features.
Speed
Streaming Gemini vs batched Whisper on GPU are different SLOs.
Enterprise
Preview Google API vs self-host MIT weights and PII handling.
Curated matrix for Gemini 3.5 Transcribe vs Whisper large-v3 — confirm live limits and pricing on each vendor’s official pages.
How they compare
Criterion-by-criterion notes from the catalog—not a ranking. Validate on your own gold set.
| Criterion | Gemini 3.5 Transcribe | Whisper large-v3 |
|---|---|---|
| Best for | Live voice agents, captioning, and meeting or call-log transcription on Google surfaces. | Self-hosted or partner-API transcription and translation pipelines you can pin and evaluate. |
| Access model | Public preview via Gemini API, AI Studio, Antigravity, and Gemini Enterprise Agent Platform. | MIT-licensed OpenAI Whisper weights; also used via third-party APIs. |
| Live vs batch | gemini-3.5-transcribe-live for bidirectional streaming; gemini-3.5-transcribe for recorded audio with timestamps. | Not a first-party bidirectional live product on this catalog row; real-time stacks are typically chunked ASR you assemble. |
| Languages | Multilingual ASR; Google cites FLEURS WER of 5.50% streaming and 5.04% non-streaming (vendor-reported). | Broad multilingual transcription and translation; robustness to accents and noise is a common reason teams keep it. |
| Speakers | Pre-recorded speaker attribution documented for up to three speakers; more than three is experimental. | No first-party diarization product equivalent to Gemini's three-speaker attribution; add an external diarizer if required. |
| Formatting | Filler-word cleanup and custom vocabulary are positioned for voice agents and post-call analytics. | Timestamps are available; PII redaction and punctuation policy are yours to implement. |
| Deployment | Google-hosted. Consumer surfaces and languages vary by product and region. | Local GPU/CPU or partner hosts. Latency tracks hardware and chunking. |
| Operational risk | Preview status. Artificial Analysis WER (4.0% streaming / 2.6% non-streaming) must be remeasured on your domain. | Hallucinations on silence or music; add VAD and confidence thresholds. Throughput needs capacity planning. |
Key insights
Concrete technical or product signals.
- This is Google vs OpenAI ASR, not MAI-Transcribe vs Whisper.
- Gemini's WER figures are vendor-reported via Artificial Analysis and FLEURS; Whisper has no comparable vendor WER on this catalog row.
- Streaming productization is the practical Gemini advantage; self-host control is the Whisper advantage.
Use cases
Where this shines in production.
- Replacing batch Whisper jobs with a hosted streaming ASR API
- Keeping air-gapped transcription with open weights
- Voice agents that need speaker labels and custom vocabulary
Limitations & trade-offs
What to watch for.
- Not a shared WER bake-off on your audio.
- Gemini preview features and languages can change.
- Whisper translation is not the same as Gemini's productized formatting.
Watch-outs
Who should pass, and what to validate before rollout.
- Do not choose Gemini expecting MIT weights.
- Do not choose Whisper expecting first-party three-speaker diarization.
- Do not use this as MAI-Transcribe vs Whisper.
Final recommendation
What to do after reading the table.
Use Gemini 3.5 Transcribe when you want Google's live ASR product. Keep Whisper when weights, translation, or air-gap matter.
FAQ
Is Gemini 3.5 Transcribe better than Whisper large-v3?
Use Gemini 3.5 Transcribe when you want Google's live ASR product. Keep Whisper when weights, translation, or air-gap matter.
Can I use both Gemini 3.5 Transcribe and Whisper large-v3?
Yes. Many teams keep both and route by constraint. Use Gemini 3.5 Transcribe for Live voice agents, captioning, and meeting or call-log transcription on Google surfaces, and Whisper large-v3 for Self-hosted or partner-API transcription and translation pipelines you can pin and evaluate. Confirm vendor terms before mixing them in one production workflow.