Minimax Hailuo 2.3 T2V Pro

Minimax Hailuo 2.3 T2V Pro

Playground

MiniMax Hailuo 2.3 Pro is a text-to-video model delivering 1080p videos with 2.5x efficiency and 85% complex-instruction accuracy. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Hailuo 2.3 Pro is the premium text-to-video model from MiniMax, engineered for creators who demand cinematic realism, dynamic motion, and superior visual coherence. It transforms text prompts into richly detailed 5-second 1080p videos — merging professional-grade quality with cutting-edge physical simulation.

🎬 Why It Looks Great

• Cinematic Fidelity – Generates ultra-smooth motion, realistic lighting, and lifelike shadows in every frame.
• Advanced Physics & Scene Logic – Accurately models object dynamics, reflections, and camera movement.
• High Prompt Accuracy – Faithfully interprets natural-language descriptions with exceptional semantic precision.
• Consistent Characters – Maintains subject identity and spatial layout throughout the clip.
• Refined Aesthetic – Tuned for film-like color grading, depth, and atmosphere.

⚙️ Limits and Performance

• Input: text prompt only
• Output duration: fixed — 5 seconds
• Resolution: up to 1080p
• Processing time: approximately 40–70 seconds per job (depending on complexity and queue load)

💰 Pricing

🚀 How to Use

1. Write a clear text prompt describing your scene, characters, lighting, and motion. Example: “A traveler walks through a neon-lit rainy street at night, reflections glowing on wet pavement.”
2. Submit your job — no reference image required.
3. Wait for processing (typically under 1 minute).
4. Download your completed 5-second cinematic video.

💡 Pro Tips

• Use film-style language — include camera direction (wide shot, slow zoom, tracking).
• Mention lighting type (sunset glow, neon reflections, soft cinematic light).
• Keep prompts concise (1–2 sentences) for best fidelity.
• For stable subjects, include descriptors like same person or consistent background.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "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/minimax/hailuo-2.3/t2v-pro" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(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' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  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

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters