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video model comparison

Gemini Omni Flash 1.1 vs Veo 3.1 Preview

Gemini Omni Flash 1.1 and Veo 3.1 Preview are both current video models, from Google. Gemini Omni Flash 1.1 is 75% cheaper per video second than Veo 3.1 Preview, $0.10 vs $0.40.

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 Omni Flash 1.1Veo 3.1 Preview
Price per video second$0.10 (better)$0.40
Artificial Analysis Video Arena (text to video)1114 Elo (#2 of 4)Not measured
Artificial Analysis Video Arena (image to video)1130 Elo (#1 of 3)Not measured
Arena Text-to-VideoNot measured1364 Elo (#1 of 3)
Arena Image-to-VideoNot measured1398 Elo (#2 of 2)
ProviderGoogleGoogle
Release dateAugust 27, 2026October 15, 2025
AccessAPI onlyAPI only
Context window1,048,576 tokensNot reported
Max outputNot reportedNot reported
Inputtext, image, videotext, image
Outputvideo, audiovideo, audio

Scores:Artificial Analysis · 2026-10-13Artificial Analysis · 2026-10-13Arena · 2026-10-12

When to choose each

Gemini Omni Flash 1.1

Best for

  • Short clips with sound that need conversational edits

Watch out for

  • Each clip is 3 to 10 seconds; extensions add 10 seconds at a time up to 40 seconds
  • Editing uploaded videos is not available in every region, and voice editing is not supported
Full Gemini Omni Flash 1.1 profile

Veo 3.1 Preview

Best for

  • Gemini API video generation with audio

Watch out for

  • Preview models may change and have stricter rate limits
Full Veo 3.1 Preview profile

Gemini Omni Flash 1.1 vs Veo 3.1 Preview FAQ

Which is cheaper, Gemini Omni Flash 1.1 or Veo 3.1 Preview?

Gemini Omni Flash 1.1 is 75% cheaper per video second than Veo 3.1 Preview, $0.10 vs $0.40.

Production evaluation

Choosing between Gemini Omni Flash 1.1 and Veo 3.1 Preview?

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