Pika v2.0 Turbo is a text-to-video model that supports multiple sizes and includes advanced prompt optimization for improved results. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
待機中
$0.21回あたり·~50 / $10
A majestic snow leopard walks on the summit of a Himalayan peak, its fur fluttering in the harsh wind. The background features rolling snow-capped mountains and a golden sunrise. Drone long shot, photorealistic style, masterpiece, 8K UHD, cinematic lighting.
Military squad moving through abandoned warehouse, flashlight beams cutting through dust, whispered commands, cinematic realism
A woman hiding in a dimly lit apartment, heavy breathing, footsteps approaching outside, close-up on her eyes, suspense building
Intense argument between two people in a small kitchen, harsh lighting, handheld camera perspective, raw emotions
Firefighters entering a burning building, smoke everywhere, radios crackling, heat waves distorting vision, cinematic urgency
Sunlight pouring into a kitchen as someone prepares breakfast, eggs sizzling, toast popping up, morning calm
Teenagers hanging out at a convenience store at night, neon lights flickering, eating snacks, casual fun
Roommates cooking dinner together, music playing in the background, kitchen slightly messy, slice-of-life style
Young couple grocery shopping together, casual conversation, checking items off a list, supermarket ambiance
A futuristic hovercar speeds through the sky of a cyberpunk city on a rainy night. Its body glides past a massive holographic billboard, creating a ripple of digital distortion. Chase view, motion blur, neon lighting, photorealistic CG, extreme detail.
Pika V2.0 Turbo Text-to-Video is a fast and powerful text-to-video generation model optimized for speed. Generate high-quality 720p videos from text descriptions quickly and efficiently — perfect for rapid content creation and iteration.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the video you want to generate. |
| size | No | Output resolution: 1280×720 or 720×1280 (default: 1280×720). |
| duration | No | Video length: 5 or 10 seconds (default: 5). |
| Duration | Price |
|---|---|
| 5 seconds | $0.20 |
| 10 seconds | $0.40 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pika/v2.0-turbo-t2v with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for v2.0 Turbo T2v below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1280*720",
"duration": 5
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pika/v2.0-turbo-t2v" \
-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/pika/v2.0-turbo-t2v";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1280*720",
"duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"size": "1280*720",
"duration": 5
}
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/pika/v2.0-turbo-t2v", 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)v2.0 Turbo T2v is a Pika model for video generation, exposed as a REST API on WaveSpeedAI. Pika v2.0 Turbo is a text-to-video model that supports multiple sizes and includes advanced prompt optimization for improved results. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
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/pika/pika-v2.0-turbo-t2v.
v2.0 Turbo T2v starts at $0.20 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.
Key inputs: `prompt`, `duration`, `size`. 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/pika/pika-v2.0-turbo-t2v.
Average end-to-end generation time on WaveSpeedAI is around 217 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Pika). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.