Minimax Music-02 is a compact, fast, cost-effective MoE music generator (230B params, 10B active) for high-quality music production. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.03per run·~33 / $1
A melancholic indie dream-pop song sung by a soft male voice, light guitars and distant reverb. Emotional and reflective.
An ethereal electronic vocal performance with auto-tune effects, floating tone, cinematic reverb
A gentle female voice singing a heartfelt pop ballad, emotional yet controlled, with a soft piano accompaniment.
A warm acoustic folk song sung by a male voice with gentle guitar and ambient background. Emotional, melodic, storytelling tone.
A powerful rock anthem sung by a raspy male vocal, with driving electric guitars, drums, and cinematic energy. Must sound like a live stadium performance.
An ethereal orchestral vocal performance with reverb and layered harmonies. Female vocal sings in a haunting, angelic tone, with cinematic strings and atmosphere.
A cinematic pop-rock anthem sung by a powerful male voice, with strong rhythm and rising strings. Heroic and emotional.
A melodic modern pop song with emotional female vocals and soft piano + ambient strings. Starts intimate, then builds into an uplifting chorus
A dreamy electronic pop track sung by a female voice, featuring reverb vocals, synth pads, and emotional delivery
An epic cinematic vocal performance with full orchestra, hybrid drums, and a strong female lead voice. Powerful, emotional, heroic tone.
MiniMax Music 02 is an AI music generation model that turns a style prompt + lyrics into a complete song. Describe the mood, genre, and vocal style, paste your lyrics, and the model produces fully arranged audio with vocals and backing instruments.
Prompt-guided composition Generate songs from natural language prompts like “melancholic indie dream-pop with soft male vocals and light guitars.”
Lyric-aware singing Paste structured lyrics (verses, chorus, bridge) and the model sings them, aligning melody and phrasing to your text.
Full arrangement Produces a mixed track with vocals, backing instruments, and effects — ready for demos, temp tracks, or creative exploration.
Configurable audio quality Supports multiple bitrates and sample rates (e.g., 256 kbps, 44.1 kHz) so you can balance quality and file size.
prompt (required) Musical description: genre, mood, instrumentation, vocal style, atmosphere.
lyrics (required) The words to sing. You can structure them by sections (Verse, Chorus, Bridge) using plain text.
bitrate Target audio bitrate, e.g. 256000 for 256 kbps.
sample_rate Audio sample rate, e.g. 44100 Hz.
Output: A single audio file (song) matching your prompt and lyrics.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/minimax/music-02 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 Music 02 below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"lyrics": "example",
"bitrate": 256000,
"sample_rate": 44100
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/minimax/music-02" \
-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/minimax/music-02";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"lyrics": "example",
"bitrate": 256000,
"sample_rate": 44100
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"lyrics": "example",
"bitrate": 256000,
"sample_rate": 44100
}
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/minimax/music-02", 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)Music 02 is a MiniMax model for audio generation, exposed as a REST API on WaveSpeedAI. Minimax Music-02 is a compact, fast, cost-effective MoE music generator (230B params, 10B active) for high-quality music production. 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/minimax/minimax-music-02.
Music 02 starts at $0.030 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`, `bitrate`, `lyrics`, `sample_rate`. 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/minimax/minimax-music-02.
Median end-to-end generation time on WaveSpeedAI is around 121 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 (MiniMax). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.