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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.
Prompt
a dog riding a skateboard
Length: 0:05
Settings
aspect_ratio
16:9
duration
5
Example prompts
Examples from the Railwail catalog. They were not generated live on this page.
Nature Scene
Length: 0:08
Aerial drone shot slowly rising above a misty old-growth forest at dawn, revealing a winding river cutting through the valley below, golden sunlight breaking through clouds
Cinematic Portrait
Length: 0:05
Close-up of a woman's face as she opens her eyes and looks into the camera, shallow depth of field, soft golden backlight, hair gently moving in the breeze, cinematic 24fps
03
About Google Veo 2
TL;DRAs of September 23, 2026
Google Veo 2 is a model by Google DeepMind in the Video generation category. On Railwail, Google Veo 2 costs $0.60 per second of video. A default run of 8 seconds costs $4.80. Supported aspect ratios: 16:9 and 9:16. Possible lengths: 5, 6, 7, or 8 s. Newer version: Google Veo 3.1.
Background
About Google DeepMind
Founded 2010 · London, United Kingdom
Google DeepMind is the merged AI research organisation formed in April 2023 when Google Brain and DeepMind (acquired by Google in 2014) were combined into a single unit led by Demis Hassabis (CEO). DeepMind was founded in 2010 by Hassabis, Shane Legg and Mustafa Suleyman, and is famous for AlphaGo, AlphaFold and the Gemini language-model family. On the video side, DeepMind shipped Imagen Video (2022), Lumiere (2024), Veo (May 2024, Google I/O), Veo 2 (December 2024), Veo 3 with native audio (May 2025) and Veo 3.1 (late 2025). Veo is exposed via Vertex AI, the Gemini API, Google Labs (VideoFX, Whisk) and YouTube's Dream Screen.
Latent video diffusion / Diffusion Transformer hybrid with cascaded super-resolution
Veo 2 is a closed text-to-video and image-to-video model that builds on DeepMind's Imagen and Lumiere research. It uses a spatio-temporal latent diffusion architecture with a transformer-based denoiser conditioned on rich text embeddings from Gemini-family encoders and optional image embeddings. Veo 2 generates clips up to 8 seconds at up to 4K resolution (extended versions reach minutes), with strong understanding of camera language ('dolly', 'aerial', '35mm anamorphic') and real-world physics. DeepMind report substantial gains over Veo 1 on motion realism, prompt adherence and detail. The pipeline combines a base diffusion model with cascaded spatial and temporal super-resolution and an upscaler. Veo 2 was trained on a curated YouTube-and-partner video corpus with synthetic captioning at multiple granularities, and post-trained with reward models for prompt adherence and aesthetic quality.
Parameters
Undisclosed
Capabilities
Text-to-video and image-to-video generation
Up to 8-second clips, up to 4K resolution
Rich cinematographic prompt vocabulary (lenses, lighting, camera moves)
Strong real-world physics and object permanence
Available via Vertex AI, Gemini API, VideoFX and YouTube Dream Screen
SynthID watermarking embedded in every frame
Multilingual prompts via Gemini text encoders
Lower hallucination of artefacts than Veo 1
Best for: high-quality marketing, film previs, broadcast inserts, enterprise creative.
Training & license
Curated mix of licensed video, public web video (including YouTube under Google's terms) and synthetic data, with multi-granularity captioning produced by Gemini vision models. Exact dataset size undisclosed.
License: Proprietary commercial licence via Google Cloud / Vertex AI and the Gemini API; commercial usage permitted under Google's generative AI terms; SynthID watermark required on all output.
Safety testing: Subject to Google's responsible AI framework with safety filters, public-figure filters, child-safety classifiers and provenance via SynthID; covered by Google's Responsible AI Progress Reports.
Known limitations
No native audio in Veo 2 (audio first appears in Veo 3)
8-second clip limit per base generation
Closed access with gated waitlist on some surfaces
Strict moderation on people, brands and political content
Limited control over exact frame composition without seed images
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google-veo-2",
"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": "google-veo-2",
"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: "google-veo-2",
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 the Gemini API; commercial usage permitted under Google's generative AI terms; SynthID watermark required on all output.
Catalog entry updated
September 23, 2026
Input parameters
Inputs and settings from the model's input schema. The example in the API section shows which of them the API accepts.
Google Veo 2 is a model by Google DeepMind in the Video generation category. On Railwail you can call it with an API key through the Railwail API.
How much does Google Veo 2 cost on Railwail?
On Railwail, Google Veo 2 costs $0.60 per second of video. A default run of 8 seconds costs $4.80. The price is known before the run starts. Usage is paid from prepaid credits; 1 credit equals $0.01.
Which settings does Google Veo 2 support?
According to its input schema, Google Veo 2 knows these parameters: prompt, duration (5, 6, 7, or 8), start_image, and aspect_ratio (16:9 or 9:16).
How fast is Google Veo 2?
There are not enough measured runs of Google Veo 2 on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.
Is Google Veo 2 better than Google Veo 3.1?
That depends on the task. Google Veo 2 (Google DeepMind) and Google Veo 3.1 (Google DeepMind) are both models in the Video generation category. The comparison page shows their prices and specifications side by side.
Create a Railwail API key and send your request with the model ID google-veo-2. Code examples for curl, Python and JavaScript are in the API section of this page.
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.
