MiniMax H3 Open Weights | Try in Video Generator →

Veo2 | Powerful Text-to-Video API

google/

Google Veo2 creates high-quality image-to-video outputs with realistic motion and extensive camera controls for customizable styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

text-to-video
Input

Idle

$2.5per run

Next:

ExamplesView all

An elderly man with kind, smiling eyes sits on a park bench, teaching his young granddaughter how to play the ukulele. The scene is bathed in the warm, soft glow of a late afternoon golden hour. The camera slowly pushes in, capturing the genuine emotion and connection between them. Highly detailed, photorealistic.

A chubby clay fox bouncing through a colorful candyland, squishy physics, exaggerated elastic motion, stop-motion style lighting, playful soundtrack, soft shadows and texture details visible in each frame.

A dragon soaring through the clouds above a floating island kingdom, golden sunlight breaking through, feathers and embers trailing in the wind, sweeping aerial shots, orchestral background, epic cinematic atmosphere.

A lone cowboy rides across the desert at golden hour, dust trailing behind his horse, the sunlight casting long shadows on the sand, dramatic wide-angle shot, shallow depth of field, cinematic color grading

A polar bear walking slowly across melting ice floes under a cloudy arctic sky, calm ambient sounds, close-up of its breath visible in the cold air, cinematic slow motion as it looks toward the camera

Time-lapse of a city built from giant classical musical instruments (like cellos, pianos, French horns). The neck of a violin serves as a bridge, piano keys form the steps of a plaza. Vehicles are musical notes gliding along a staff. As time progresses, the sky changes from sunrise to sunset, and the light and shadows on the instrumental architecture shift accordingly.

An old man rides a rural bus alone, looking out the window at rolling fields, reflections move across his face, slow-paced, melancholic realism, vintage tones

A man paddles a canoe across a calm mountain lake at golden hour, long lens tracking shot, shimmering reflections, birds flying overhead, serene and real

A woman running along the shoreline at dusk, soft ocean waves, wide angle drone shots, skin catching golden light, immersive slow-motion sequences, real filmic tones

A young woman rides a bicycle through a sunflower field at sunset, laughing with friends, POV shots and drone overheads, lens flare and vibrant colors, upbeat indie music feel

Related Models

README

Google Veo2 on WaveSpeedAI

Google Veo2 on WaveSpeedAI empowers you to create high-quality videos from both text prompts and static images, leveraging Google's advanced AI for next-generation content creation.

Overview

Veo2 on WaveSpeedAI is designed for seamless text-to-video and image-to-video generation. It understands real-world physics, human movement, and cinematic techniques, making it ideal for creators and developers who want to produce visually stunning, dynamic videos at scale.

🚀 Key Capabilities

  • Text-to-Video Generation Turn your ideas into cinematic videos with natural motion and high visual fidelity.
  • Image-to-Video Animation Animate static images into smooth, engaging video sequences.
  • Advanced Camera & Cinematic Controls Fine-tune camera movement, shot composition, and visual style for professional results.
  • Production-Ready Output Generate videos suitable for commercial use, marketing, and creative projects.
  • Scalable Processing Handle large volumes of content efficiently with WaveSpeedAI's robust infrastructure.

🌟 Popular Use Cases

  • Marketing & Advertising: Transform product photos into eye-catching video ads and dynamic social media content.
  • Content Creation: Bring blog images to life, generate video thumbnails, and create engaging presentations from text.
  • E-commerce: Showcase products in motion, create lifestyle videos, and enhance online shopping experiences.
  • Creative Projects: Animate illustrations, craft cinematic sequences, and explore new forms of motion graphics.

✨ Prompting Tips

To get the best results with Veo2 on WaveSpeedAI, try these strategies:

  • Shot Composition: Close-up, two shot, over-the-shoulder
  • Lens & Focus: Macro lens, shallow focus, wide-angle lens
  • Genre & Style: Sci-fi, romantic comedy, action movie
  • Camera Motion: Zoom shot, dolly shot, tracking shot, pan shot

🎬 Example Prompt

A close-up shot of melting icicles on a frozen rock wall, with cool blue tones and a zoom-in camera movement, capturing the detailed motion of water drips.

Note:This website uses AI models provided by third parties.

Veo2 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/veo2 with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Veo2 below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "aspect_ratio": "16:9",
    "duration": 5,
    "resolution": "720p",
    "enable_prompt_expansion": true
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/google/veo2" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/google/veo2";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "aspect_ratio": "16:9",
        "duration": 5,
        "resolution": "720p",
        "enable_prompt_expansion": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "aspect_ratio": "16:9",
    "duration": 5,
    "resolution": "720p",
    "enable_prompt_expansion": True
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/google/veo2", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Veo2 API — Frequently asked questions

What is the Veo2 API?

Veo2 is a Google model for video generation, exposed as a REST API on WaveSpeedAI. Google Veo2 creates high-quality image-to-video outputs with realistic motion and extensive camera controls for customizable styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Veo2 API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/google/google-veo2.

How much does Veo2 cost per run?

Veo2 starts at $2.50 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Veo2 accept?

Key inputs: `prompt`, `aspect_ratio`, `resolution`, `duration`, `seed`, `negative_prompt`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/google/google-veo2.

How long does Veo2 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 36 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Veo2 outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Google). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Veo2 | Powerful Text-to-Video API on WaveSpeedAI