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02
Examples
Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
Input
Prompt
the woman is giving an interview for a podcast, wearing a pink top with the logo, it also neatly says "Veo 3.1", she is in a midcentury modern studio with pink lighting, she talks about using Veo 3.1 with reference images to put things into videos you're making, the logo is also in a framed picture against black behind her
Lengde: 0:08
Settings
aspect_ratio
16:9
resolution
1080p
duration
8
Input
Prompt
show what happens in this location
Lengde: 0:08
Settings
aspect_ratio
16:9
resolution
1080p
duration
8
Input
Prompt
the woman are having a conversation in a coffee shop, with the logo in the background. They talk about using Veo 3.1 with reference images to put things into videos
Lengde: 0:08
Settings
aspect_ratio
16:9
resolution
720p
duration
8
Eksempelprompts
Eksempler fra Railwail-katalogen. De ble ikke generert live på denne siden.
Aerial
Lengde: 0:08
Sweeping aerial view of coastal city at golden hour
03
Om Google Veo 3.1
Kort sagtPer 23. september 2026
Google Veo 3.1 er en modell fra Google DeepMind i kategorien Videogenerering. På Railwail koster Google Veo 3.1 0,48 USD per videosekund. Avhengig av innstillingene varierer prisen fra 0,24 USD/s til 0,48 USD/s. En standardkjøring på 4 sekunder koster 1,92 USD. Støttede bildeformat: 16:9 og 9:16. Mulige lengder: 4, 6 eller 8 s.
Bakgrunn
Om Google DeepMind
Grunnlagt 2010 · London, United Kingdom
Google DeepMind, formed in 2023 from the merger of Google Brain and DeepMind under Demis Hassabis, runs the Veo video-generation programme. After the headline launch of Veo 3 (May 2025, Google I/O) with native audio, the team shipped Veo 3.1 in late 2025 as an incremental quality and capability upgrade. Veo 3.1 improves prompt adherence, motion physics, character consistency across extended sequences and audio fidelity (including richer ambient soundscapes and more controllable dialogue). The model is exposed via Vertex AI, the Gemini API, Google Labs (VideoFX, Whisk) and the Flow filmmaking surface aimed at professional creators.
Latent video diffusion / DiT with joint audio-video diffusion and reference-frame conditioning
Veo 3.1 retains the joint audio-video diffusion architecture introduced in Veo 3: video is encoded into a spatio-temporal latent space via a 3D causal VAE and denoised by a transformer-based diffusion model, while a coupled audio diffusion module generates synchronized music, ambient sound and dialogue. Improvements in 3.1 are reported across motion physics (water, fabric, crowds), audio quality and the ability to stay consistent across extended sequences via reference-frame and subject-reference conditioning. Native clips run up to 8 seconds at 1080p with extensions for longer sequences and a separate cascaded super-resolution stage for 4K. Text conditioning uses Gemini-family encoders; image and reference-frame conditioning add identity and layout control. Training expands on the Veo 3 corpus with additional curated multilingual audio-video data and refined recaptioning.
Parametere
Undisclosed
Evner
All Veo 3 features plus improved physics, audio fidelity and consistency
Up to 8-second 1080p clips natively, 4K via cascaded super-resolution
Reference-frame and subject-reference conditioning
Joint audio-video diffusion with music, ambient sound and dialogue lip-sync
Rich cinematographic prompt vocabulary
Multilingual prompts via Gemini text encoders
Available via Vertex AI, Gemini API, VideoFX, Whisk and Flow
SynthID audio + visual watermarking
Best for: high-end commercial creative, longer sequences, branded campaigns with sound.
Trening og lisens
Expanded curated multilingual audio-video corpus including licensed footage, public web video (including YouTube under Google's terms) and synthetic data, with multi-granularity captions from Gemini vision-language models.
Lisens: Proprietary commercial licence via Google Cloud / Vertex AI and Gemini API; commercial use under Google's generative-AI terms; mandatory SynthID watermarking.
Sikkerhetstesting: Google Responsible AI framework with safety filters, public-figure filters, child-safety classifiers and provenance via SynthID; ongoing red-team programme.
Kjente begrensninger
8-second native clip limit
Audio short-form and English-leaning
Strict moderation on people, brands and political content
Closed model with no peer-reviewed paper
Per-clip cost higher than Veo 3 Fast or Veo 3.1 Fast tiers
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "veo-3-1",
"prompt": "A slow drone shot over a misty pine forest at sunrise"
}'
# Response: {"job_id": "...", "status": "queued", ...}
# Poll until status is completed, failed or cancelled:
curl https://railwail.com/api/v1/jobs/JOB_ID \
-H "Authorization: Bearer $RAILWAIL_API_KEY"
import os
import time
import requests
API = "https://railwail.com/api/v1"
headers = {"Authorization": f"Bearer {os.environ['RAILWAIL_API_KEY']}"}
job = requests.post(
f"{API}/videos/generations",
headers=headers,
json={
"model": "veo-3-1",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
},
).json()
while True:
status = requests.get(f"{API}/jobs/{job['job_id']}", headers=headers).json()
if status["status"] in ("completed", "failed", "cancelled"):
break
time.sleep(5)
print(status["status"], status.get("output_url"))
const API = "https://railwail.com/api/v1";
const headers = {
Authorization: `Bearer ${process.env.RAILWAIL_API_KEY}`,
"Content-Type": "application/json",
};
const job = await fetch(`${API}/videos/generations`, {
method: "POST",
headers,
body: JSON.stringify({
model: "veo-3-1",
prompt: "A slow drone shot over a misty pine forest at sunrise"
}),
}).then((r) => r.json());
let status;
do {
await new Promise((r) => setTimeout(r, 5000));
status = await fetch(`${API}/jobs/${job.job_id}`, { headers }).then((r) => r.json());
} while (!["completed", "failed", "cancelled"].includes(status.status));
console.log(status.status, status.output_url);
Proprietary commercial licence via Google Cloud / Vertex AI and Gemini API; commercial use under Google's generative-AI terms; mandatory SynthID watermarking.
Katalogoppføring oppdatert
23. september 2026
Inndataparametere
Inndataer og innstillinger fra modellens inndataskjema. Eksemplet i API-delen viser hvilke av dem APIen godtar.
Google Veo 3.1 er en modell fra Google DeepMind i kategorien Videogenerering. På Railwail kan du kalle den med en API-nøkkel gjennom Railwail API.
Hvor mye koster Google Veo 3.1 på Railwail?
På Railwail koster Google Veo 3.1 0,48 USD per videosekund. Avhengig av innstillingene varierer prisen fra 0,24 USD/s til 0,48 USD/s. En standardkjøring på 4 sekunder koster 1,92 USD. Prisen er kjent før kjøringen starter. Bruk betales fra forhåndskjøpte kreditter; 1 kreditt tilsvarer 0,01 USD.
Hvilke innstillinger støtter Google Veo 3.1?
I henhold til inndataskjemaet kjenner Google Veo 3.1 disse parametrene: prompt, duration (4, 6 eller 8), start_image og aspect_ratio (16:9 eller 9:16).
Hvor rask er Google Veo 3.1?
Det finnes ennå ikke nok målte kjøringer av Google Veo 3.1 på Railwail til å angi en kjøretid. Det avhenger av inndataene, innstillingene og belastningen hos leverandøren.
Er Google Veo 3.1 bedre enn HunyuanVideo?
Det avhenger av oppgaven. Google Veo 3.1 (Google DeepMind) og HunyuanVideo (Tencent) er begge modeller i kategorien Videogenerering. Sammenligningssiden viser prisene og spesifikasjonene deres side ved side.
Opprett en Railwail API-nøkkel og send forespørselen din med modell-ID-en veo-3-1. Kodeeksempler for curl, Python og JavaScript finnes i API-delen av denne siden.
Kuaishou's Kling v2.1, generating 5 and 10 second videos at 720p or 1080p from text or an image. Known for cinematic camera work and realistic physical motion, available on Replicate via the official KwaiVGI account.
