Gemini Omni Flash 1.1
gemini-omni-flash-1-1-image-to-videogoogle/gemini-omni-flash/v1.1/image-to-video- 🎬 Image-to-video variant of Google's Omni Flash generative video model
- 🆕 Version 1.1 is a production-ready update focused on control
- 📏 Clips can be extended in 10-second steps, to 40 seconds
- 🧠 Uses up to 10 seconds of prior context, versus one previously
- 👁️ First-and-last-frame interpolation plus prompt or preset camera moves
- ⚡ Fast low-resolution drafts for prototyping, with 4K upscaling
- 🔒 Safety filters applied to both input prompts and generated video
- 🌐 English fully supported; other languages unevaluated by Google
Google is an American multinational technology corporation and one of the world's most valuable brands. A subsidiary of parent company Alphabet Inc., Google operates across search, cloud computing, consumer electronics, and artificial intelligence. Its DeepMind and Google…
Explore 28 more models by Google →Gemini Omni Flash is Google's multimodal model for video, image and text tasks, optimized for video generation and able to return video output alongside text in a single model. Google DeepMind positions it as a step toward models that create and edit anything from any input, starting with video, combining Gemini's reasoning with its generative media work. This catalog entry covers the image-to-video path of the 1.1 release: you supply a still image plus a prompt, and the model animates it.
Compared with the earlier Gemini Omni Flash, Google describes 1.1 as a production-ready update offering improved control over generative video. The headline change for image-driven work is context depth: 1.1 can analyze up to 10 seconds of prior footage, where previous models referenced only the final second, which Google says improves visual consistency and narrative adherence. It also adds scene extension in 10-second increments up to a 40-second total, keeping motion, characters and audio coherent, plus first-and-last-frame interpolation, 4K upscaling, and cheap low-resolution drafts for faster iteration.
It ships alongside Gemini Omni Flash 1.1 for text prompts, Gemini Omni Flash 1.1 R2V for reference images, and Gemini Omni Flash 1.1 Edit for conversational editing of existing clips.
Google notes the Omni Flash line is a preview family, applies content safety filters to both prompts and generated video, and has fully evaluated only English prompting.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Gemini Omni Flash 1.1 · Organic sellers
Top sellers · Gemini Omni Flash 1.1 · Organic
- Open Forge81.2%
- antseed-tidal-falcon-88cd9.2%
- Venice.ai Proxy8.7%
- attacompute.ai0.7%
- ▲ Apex Ant0.2%
| Seller | Health | Rep↓ | Sales | Video | Routing | $ / s | $ / video |
|---|---|---|---|---|---|---|---|
| Open Forge 0x1d90…b0aa | healthy | 91.11 | $24.44 | 5 min | #1 | $0.10 | — |
| Venice.ai Proxy 0x1f22…18c9 | healthy | 86.84 | $2.62 | 1.8 min | #2 | $0.0641 | — |
| ▲ Apex Ant 0x73b4…e736 | — | 81.71 | $0.07 | 4 s | #3 | $0.0525 | — |
| attacompute.ai 0xd5e7…7be0 | — | 51.22 | $0.20 | 4 s | gated | $0.136 | — |
| antseed-tidal-falcon-88cd 0x80d1…88cd | healthy | 12.78 | $2.77 | 16 s | gated | $0.08 | — |
| OpenCheap 0x0ee1…6c41 | — | 12.43 | — | — | gated | $0.095 | — |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.