MiniMax Music 2.5 is a full-dimensional breakthrough in AI music generation with high-fidelity audio, humanized vocals, and precise creative control. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.075per run·~13 / $1
Cinematic orchestral, epic,hans zimmer style, motivational, building tension, heavy war drums, strings, brass section, heroic, triumphant, emotional crescendo, wide soundstage, 8k audio quality.
Aggressive Phonk, drift phonk, distorted 808 bass, high bpm, cowbell melody, dark atmosphere, energetic, heavy percussion, male rap vocals, gritty texture, hype music for workout.
MiniMax Music 2.5 is an advanced AI music generation model that creates complete songs from text prompts and lyrics. Describe your desired musical style and provide lyrics — the model generates full-length music with vocals, instrumentals, and professional production quality.
Complete song generation Creates full songs with vocals and instrumentals from text descriptions.
Lyrics support Input your own lyrics with structure markers (Verse, Chorus, etc.) for precise song composition.
Style flexibility Supports a wide range of genres from cinematic orchestral to pop, rock, electronic, and more.
Professional audio quality Multiple bitrate and sample rate options for studio-quality output.
Prompt Enhancer Built-in tool to automatically improve your music descriptions.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Music style description (genre, mood, instruments, etc.) |
| lyrics | Yes | Song lyrics with optional structure markers |
| bitrate | No | Audio bitrate: 32000, 60000, 64000, 128000, 256000 (default: 256000) |
| sample_rate | No | Audio sample rate: 16000, 24000, 32000, 44100 (default: 44100) |
Use structure markers to guide song composition:
Example:
(Instrumental intro with drums building up) (Verse) The sun rises on the broken ground Silence screams without a sound (Chorus) I stand alone against the gale Through the storm, I will prevail
| Output | Cost |
|---|---|
| Per song | $0.15 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/minimax/music-2.5 with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Music 2.5 below.
set -euo pipefail
: "${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-2.5" \
-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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/minimax/music-2.5";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"lyrics": "example",
"bitrate": 256000,
"sample_rate": 44100
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
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-2.5", 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 = 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", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Music 2.5 is a MiniMax model for audio generation, exposed as a REST API on WaveSpeedAI. MiniMax Music 2.5 is a full-dimensional breakthrough in AI music generation with high-fidelity audio, humanized vocals, and precise creative control. 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-2.5.
Music 2.5 starts at $0.075 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-2.5.
Median end-to-end generation time on WaveSpeedAI is around 144 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.