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MiniMax H3 Open Weights Reference to Video generates coherent 480P / 768P videos from prompts and multimodal references, guided by up to 9 reference images, 3 reference videos, and 3 reference audios, with native stereo audio and flexible reference-based video generation on WaveSpeedAI infrastructure. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-video
Input

Idle

$0.25per run·~40 / $10

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ExamplesView all

Fast-paced fashion video, shot on ARRI Alexa 35, HDR golden hour natural light, hyperrealistic live-action. Stunning blonde European woman inside a blue sedan leaning out the window, outstretching palm toward camera for audience interaction. Camera pushes forward smoothly, focus shifts from her face to hand, subtle gentle orbit movement, hair blowing lightly in wind, 85mm lens, shallow depth of field, lifelike skin texture, clean frame, no text or logos.

Fast-paced thrilling action video, shot on ARRI Alexa 35, golden hour sunrise lighting, hyperrealistic cinematic footage. Handsome European man in skydiving harness leans out of small airplane doorway above sea of clouds. Dynamic camera movement, slow push-in toward the man, subtle camera shake, wind tousles his hair. He leans further outward, ready to leap. Shallow depth of field, crisp details, lifelike skin texture, airplane fuselage marked "WAVESPEED", dramatic warm backlight, intense adventurous atmosphere, smooth natural motion.

Related Models

README

MiniMax H3 Reference-to-Video (Open Weights)

Run the open-weights edition of MiniMax H3 on WaveSpeedAI's own GPU infrastructure. This endpoint is separate from the official minimax/h3 API: same model family, independently hosted, with its own 480p/768p resolutions and per-second pricing.

MiniMax H3 is an omni-modal video model that generates picture and native stereo audio in a single pass. In reference-to-video mode you guide the generation with up to 9 reference images, 3 reference videos, and 3 reference audio tracks, then tell the model — in the prompt — what each reference is for.

How to Use References

Refer to every input in the prompt with an angle-bracket tag: <Picture 1>, <Video 1>, <Audio 1>, and so on. The tags must be written exactly like that, with the brackets — plain text such as Picture 1 is treated as ordinary words, not a reference.

Assign a job to each reference. Explicit assignments work far better than leaving the model to guess:

"Use the character from <Picture 1>, place them in the setting from <Picture 2>, and match the camera motion of <Video 1>."

Numbering

  • Tags are numbered per type, in the order the inputs are provided: images are <Picture 1><Picture 9>, standalone audios are <Audio 1>…, videos are <Video 1><Video 3>.
  • A reference video's own soundtrack is used automatically and occupies the earliest <Audio …> slots; any standalone reference_audios you provide are numbered after the video soundtracks. If you only need the video's audio, refer to it via its <Video …> tag.

Rules

  • At least one reference image or video is required. Audio cannot be provided alone.
  • reference_videos is available only at 480p output resolution.
  • Reference video soundtracks are picked up automatically; total reference-video duration shares a 15-second budget and longer inputs are trimmed fairly.

How to Write a Great Prompt

Write the prompt as a timeline with a schedule, and weave the reference tags into it:

  1. Style & references — establish the look and say which reference drives identity, style, motion, or voice.
  2. Timeline — timestamped beats: "[0s-3s] <Picture 1> stands on the balcony from <Picture 2> … [3s-6s] turns toward camera and smiles …".
  3. Camera — explicit movement, including when to hold: "slow orbit, no cuts".
  4. Audio — an Audio: line with entrance cues; reference an input voice or track by tag if you want it matched.
  5. On-screen text — spell out readable words in quotes and add "no subtitles, do not add other text."

Parameters

ParameterRequiredDescription
promptYesDesired video with references addressed as <Picture 1>, <Video 1>, <Audio 1>, plus an Audio: line for the soundtrack.
reference_imagesNoUp to 9 reference image URLs.
reference_videosNoUp to 3 reference video URLs. 480p output only. Their soundtracks are used automatically.
reference_audiosNoUp to 3 standalone reference audio URLs (each trimmed to 15s).
aspect_ratioNo16:9, 9:16, 1:1, 4:3, 3:4, 21:9, or 9:21. Default: 16:9.
resolutionNo480p (faster, lower cost) or 768p (native canvas). Default: 480p.
durationNoOutput length in seconds, 315. Default: 5.
seedNoFixed seed for reproducible output.

At least one of reference_images, reference_videos, or reference_audios is required.

Specifications

ItemDetail
OutputMP4 with native stereo audio
Frame rate24 fps
Resolution480p or 768p
Duration315 seconds (snaps to the model's frame grid, so a 5s request lands at ~5.2s)
Reference imagesUp to 9
Reference videosUp to 3, 480p output only, 15s total budget
Reference audioUp to 3, trimmed to 15s each
SeedSupported

Pricing

Output video is billed per generated second, plus per-reference charges:

ItemPrice
Output video (480p)$0.05 / second
Output video (768p)$0.125 / second
Reference image$0.02 each
Reference audio$0.02 each
Reference video (480p)$0.05 / second

Example: a 10s 480p video with 2 reference images and a 5s reference video costs 10 × $0.05 + 2 × $0.02 + 5 × $0.05 = $0.79.

Explore the MiniMax H3 Open Weights Family

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Minimax H3 Reference To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/minimax-h3/reference-to-video 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 Minimax H3 Reference To Video 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",
    "resolution": "480p",
    "duration": 5,
    "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/minimax-h3/reference-to-video" \
  -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/wavespeed-ai/minimax-h3/reference-to-video";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "aspect_ratio": "16:9",
        "resolution": "480p",
        "duration": 5,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "resolution": "480p",
    "duration": 5,
    "seed": -1
}

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/wavespeed-ai/minimax-h3/reference-to-video", 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)

Minimax H3 Reference To Video API — Frequently asked questions

What is the Minimax H3 Reference To Video API?

Minimax H3 Reference To Video is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. MiniMax H3 Open Weights Reference to Video generates coherent 480P / 768P videos from prompts and multimodal references, guided by up to 9 reference images, 3 reference videos, and 3 reference audios, with native stereo audio and flexible reference-based video generation on WaveSpeedAI infrastructure. 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 Minimax H3 Reference To Video 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/wavespeed-ai/minimax-h3-reference-to-video.

How much does Minimax H3 Reference To Video cost per run?

Minimax H3 Reference To Video starts at $0.25 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 Minimax H3 Reference To Video accept?

Key inputs: `prompt`, `aspect_ratio`, `resolution`, `duration`, `seed`, `reference_images`. 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/wavespeed-ai/minimax-h3-reference-to-video.

How long does Minimax H3 Reference To Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 154 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 Minimax H3 Reference To Video outputs commercially?

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

MiniMax H3 Reference-to-Video Open Weights API on WaveSpeedAI