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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
Duração: 0:08
Settings
aspect_ratio
16:9
resolution
1080p
duration
8
Input
Prompt
show what happens in this location
Duração: 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
Duração: 0:08
Settings
aspect_ratio
16:9
resolution
720p
duration
8
Prompts de exemplo
Exemplos do catálogo Railwail. Eles não foram gerados ao vivo nesta página.
Aerial
Duração: 0:08
Sweeping aerial view of coastal city at golden hour
03
Sobre Google Veo 3.1
ResumoA partir de 23 de setembro de 2026
Google Veo 3.1 é um modelo de Google DeepMind na categoria Geração de vídeos. No Railwail, Google Veo 3.1 custa US$ 0,48 por segundo de vídeo. Dependendo das configurações, o preço varia de US$ 0,24/s a US$ 0,48/s. Uma execução padrão de 4 segundos custa US$ 1,92. Proporções de aspecto suportadas: 16:9 e 9:16. Durações possíveis: 4, 6 ou 8 s.
Fundo
Sobre Google DeepMind
Fundado em 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.
Parâmetros
Undisclosed
Capacidades
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.
Treinamento & licença
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.
Licença: Proprietary commercial licence via Google Cloud / Vertex AI and Gemini API; commercial use under Google's generative-AI terms; mandatory SynthID watermarking.
Testes de segurança: Google Responsible AI framework with safety filters, public-figure filters, child-safety classifiers and provenance via SynthID; ongoing red-team programme.
Limitações conhecidas
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.
Entrada do catálogo atualizada
23 de setembro de 2026
Parâmetros de entrada
Entradas e configurações do esquema de entrada do modelo. O exemplo na seção API mostra quais delas a API aceita.
Google Veo 3.1 é um modelo de Google DeepMind na categoria Geração de vídeos. No Railwail você pode chamá-lo com uma chave de API através da API Railwail.
Quanto custa Google Veo 3.1 no Railwail?
No Railwail, Google Veo 3.1 custa US$ 0,48 por segundo de vídeo. Dependendo das configurações, o preço varia de US$ 0,24/s a US$ 0,48/s. Uma execução padrão de 4 segundos custa US$ 1,92. O preço é conhecido antes do início da execução. O uso é pago com créditos pré-pagos; 1 crédito equivale a US$ 0,01.
Quais configurações Google Veo 3.1 suporta?
De acordo com seu esquema de entrada, Google Veo 3.1 conhece estes parâmetros: prompt, duration (4, 6 ou 8), start_image e aspect_ratio (16:9 ou 9:16).
Qual é a velocidade de Google Veo 3.1?
Ainda não há execuções medidas suficientes de Google Veo 3.1 no Railwail para indicar um tempo de execução. Depende da entrada, das configurações e da carga no provedor.
Google Veo 3.1 é melhor que HunyuanVideo?
Depende da tarefa. Google Veo 3.1 (Google DeepMind) e HunyuanVideo (Tencent) são ambos modelos na categoria Geração de vídeos. A página de comparação mostra seus preços e especificações lado a lado.
Crie uma chave de API Railwail e envie sua solicitação com o ID do modelo veo-3-1. Exemplos de código para curl, Python e JavaScript estão na seção API desta página.
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.
