Luma Ray 1.6 generates high-quality videos from text prompts, with support for multiple sizes and advanced prompt optimization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Siap
$0.3per run·~33 / $10
A luminous deer walks through a forest made of giant, translucent crystals. The crystals refract moonlight, casting colorful light patterns on the ground. Magical, tranquil atmosphere, cinematic, 4K, camera follows the deer's movement slowly.
Grandmother teaching her granddaughter how to knit, soft sunlight through lace curtains, close-up of hands, quiet and loving atmosphere
Father helping his son ride a bicycle for the first time in a suburban park, laughter, shaky bike wheels, cheering in the background
Young couple assembling IKEA furniture in a messy living room, instruction sheet on the floor, casual bickering and giggles
Student sitting in a corner of a public library, flipping through a thick novel, warm reading lamp, ambient silence and turning pages
Freelancer working at home at night, typing on laptop, cat curled up nearby, gentle hum of electronics and lo-fi beats
Young man walking alone on a rainy sidewalk, neon lights reflecting in puddles, hood pulled up, melancholic mood
Man buying vegetables at a farmers' market, vendors calling out, fresh produce on display, sunlight filtering through tents
Fashionable young woman riding an escalator in a department store, shopping bags in hand, overhead ambient mall music
An astronaut performs a spacewalk in front of a massive, softly glowing nebula. The Earth is reflected in their helmet's visor. Epic, serene, and lonely atmosphere, wide-angle shot, camera slowly pans to reveal the vastness of space.
Create cinematic videos from pure imagination with Luma Ray 1.6 Text-to-Video. Simply describe your scene and watch it come to life — no source images required. Ray 1.6 excels at magical, fantastical, and visually stunning content with professional-grade camera work.
Need to animate an existing image? Try Luma Ray 1.6 I2V for image-to-video generation.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the scene, action, and atmosphere you want. |
| size | No | Output dimensions: 1280×720 (landscape) or 720×1280 (portrait). Default: 1280×720. |
| duration | No | Video length: 5 or 10 seconds. Default: 5. |
Per 5-second billing based on duration.
| Duration | Calculation | Cost |
|---|---|---|
| 5 seconds | 5 ÷ 5 × $0.30 | $0.30 |
| 10 seconds | 10 ÷ 5 × $0.30 | $0.60 |
| Size | Orientation | Best For |
|---|---|---|
| 1280×720 | Landscape | YouTube, presentations, cinematic content |
| 720×1280 | Portrait | TikTok, Instagram Reels, Stories, mobile |
| Model | Type | Cost (5s) | Best For |
|---|---|---|---|
| Ray 1.6 T2V | Text-to-Video | $0.30 | Pure imagination, fantasy scenes |
| Ray 1.6 I2V | Image-to-Video | $0.20 | Animating existing images |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/luma/ray-1.6-t2v with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Ray 1.6 T2v below.
set -euo pipefail
: "${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/luma/ray-1.6-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/luma/ray-1.6-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/luma/ray-1.6-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)Ray 1.6 T2v is a Luma model for video generation, exposed as a REST API on WaveSpeedAI. Luma Ray 1.6 generates high-quality videos from text prompts, with support for multiple sizes and advanced prompt optimization. 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/luma/luma-ray-1.6-t2v.
Ray 1.6 T2v starts at $0.30 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/luma/luma-ray-1.6-t2v.
Median end-to-end generation time on WaveSpeedAI is around 195 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Luma). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.