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audio model comparison

Gemini 3.1 Flash Live Preview vs GPT-4o Transcribe

Gemini 3.1 Flash Live Preview and GPT-4o Transcribe are both current audio models, from Google and OpenAI. Gemini 3.1 Flash Live Preview is 17% cheaper per audio minute than GPT-4o Transcribe, $0.005 vs $0.006. Gemini 3.1 Flash Live Preview has the larger context window, 131,072 vs 16,000 tokens.

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 3.1 Flash Live PreviewGPT-4o Transcribe
Price per audio minute$0.005 (better)$0.006
Artificial Analysis AA-WER v2Not measured3.96 % WER (#6 of 6)
ProviderGoogleOpenAI
Release dateMarch 26, 2026March 20, 2025
AccessAPI onlyAPI only
Context window131,072 tokens16,000 tokens
Max output65,536 tokens2,000 tokens
Inputtext, image, audio, videotext, audio
Outputtext, audiotext

Scores:Artificial Analysis · 2026-10-13

When to choose each

Gemini 3.1 Flash Live Preview

Best for

  • Realtime voice applications with multimodal context

Watch out for

  • This preview endpoint may change before becoming stable and has input prices that vary by modality
Full Gemini 3.1 Flash Live Preview profile

GPT-4o Transcribe

Best for

  • Speech transcription

Watch out for

  • OpenAI announced its deprecation on 2026-08-26 and recommends GPT Transcribe, which costs $0.0045 per minute against about $0.006 here
Full GPT-4o Transcribe profile

Gemini 3.1 Flash Live Preview vs GPT-4o Transcribe FAQ

Which is cheaper, Gemini 3.1 Flash Live Preview or GPT-4o Transcribe?

Gemini 3.1 Flash Live Preview is 17% cheaper per audio minute than GPT-4o Transcribe, $0.005 vs $0.006.

Which has the larger context window, Gemini 3.1 Flash Live Preview or GPT-4o Transcribe?

Gemini 3.1 Flash Live Preview has the larger context window, 131,072 vs 16,000 tokens.

Production evaluation

Choosing between Gemini 3.1 Flash Live Preview and GPT-4o Transcribe?

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