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audio model comparison

Gemini 3.5 Transcribe vs Gemini 3.8 Flash-Lite TTS

Gemini 3.5 Transcribe and Gemini 3.8 Flash-Lite TTS are both current audio models, from Google. Gemini 3.5 Transcribe is 44% cheaper per audio minute than Gemini 3.8 Flash-Lite TTS, $0.005 vs $0.009.

Data verified October 13, 2026 · Dataset 2026-10-13

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Side by side

Standard-tier prices in effect today. The better value in each row is highlighted.

FactGemini 3.5 TranscribeGemini 3.8 Flash-Lite TTS
Price per audio minute$0.005 (better)$0.009
Artificial Analysis AA-WER v22.6 % WER (#4 of 6)Not measured
Artificial Analysis Speech ArenaNot measured1246 Elo (#5 of 11)
ProviderGoogleGoogle
Release dateAugust 26, 2026September 22, 2026
AccessAPI onlyAPI only
Context windowNot reportedNot reported
Max outputNot reportedNot reported
Inputaudiotext
Outputtextaudio

Scores:Artificial Analysis · 2026-10-13Artificial Analysis · 2026-10-13

When to choose each

Gemini 3.5 Transcribe

Best for

  • Batch transcription with speaker labels
  • Multilingual meeting and call transcripts

Watch out for

  • One hour of audio per request, or 30 minutes with diarization or timestamps
  • Custom vocabulary cannot be combined with diarization or timestamps, and there is no Batch API
Full Gemini 3.5 Transcribe profile

Gemini 3.8 Flash-Lite TTS

Best for

  • High-volume speech generation such as notifications, IVR prompts and accessibility audio

Watch out for

  • Google doubles the rates on 2027-01-01, to $1.00 input and $12 output per 1M tokens (about $0.018 per minute)
  • No independent speech-quality benchmark is recorded here
Full Gemini 3.8 Flash-Lite TTS profile

Gemini 3.5 Transcribe vs Gemini 3.8 Flash-Lite TTS FAQ

Which is cheaper, Gemini 3.5 Transcribe or Gemini 3.8 Flash-Lite TTS?

Gemini 3.5 Transcribe is 44% cheaper per audio minute than Gemini 3.8 Flash-Lite TTS, $0.005 vs $0.009.

Production evaluation

Choosing between Gemini 3.5 Transcribe and Gemini 3.8 Flash-Lite TTS?

Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • A head-to-head evaluation on your real workload
  • Routing between models by cost and difficulty