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minimax-music-v2

MINIMAX-MUSIC

MiniMax Music 2.0 transforms text prompts into high-fidelity, diverse musical compositions, blending advanced AI composition, sound design, and arrangement to deliver studio-quality tracks in seconds.

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Avg Run Time: 120.000s

Model Slug: minimax-music-v2

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Output

Example Result

Preview and download your result.

Each execution costs $0.0300. With $1 you can run this model about 33 times.

API & SDK

Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

Readme

Table of Contents
Overview
Technical Specifications
Key Considerations
Tips & Tricks
Capabilities
What Can I Use It For?
Things to Be Aware Of
Limitations

Overview

MiniMax Music 2.0, developed by MiniMax, is an advanced AI model designed to transform text prompts into high-fidelity, diverse musical compositions. Building on the capabilities of its predecessor (MiniMax Music 1.5), this model integrates cutting-edge AI composition, sound design, and arrangement technologies to deliver studio-quality tracks in seconds. It is positioned as a voice generator, capable of producing both instrumental and vocal music, and is tailored for creators seeking rapid, professional-grade audio generation.

The model leverages a sophisticated understanding of musical genres, emotional context, and lyrical cadence to generate coherent, expressive, and musically rich outputs. MiniMax Music 2.0 stands out for its ability to generate full-length songs with natural-sounding vocals and complex arrangements, supporting a wide range of genres and languages. Its unique blend of rapid generation speed, high audio fidelity, and flexible prompt handling makes it a valuable tool for musicians, content creators, and businesses looking to automate or enhance their music production workflows.

Technical Specifications

  • Architecture: Proprietary deep learning model, likely based on transformer or diffusion architectures with specialized modules for music and voice synthesis (specifics not publicly disclosed)
  • Parameters: Not officially published for version 2.0; previous versions suggest a large-scale model
  • Resolution: Audio sample rates up to 44100 Hz (CD quality); supports high-bitrate output (e.g., 128 kbps and above)
  • Input/Output formats: Text prompts (with optional lyrics input); outputs in standard audio formats such as WAV and MP3
  • Performance metrics: Generates full-length tracks (up to 4 minutes); low-latency generation (seconds per track); supports multi-language lyrics and diverse genre outputs

Key Considerations

  • The quality of the generated music is highly dependent on the specificity and clarity of the input prompt; detailed prompts yield more targeted results
  • For best results, provide both a descriptive prompt and lyrics if vocal output is desired
  • Adjusting parameters such as sample rate and bitrate can impact both quality and generation speed
  • Overly vague or conflicting prompts may result in less coherent or generic outputs
  • Iterative refinement—regenerating with adjusted prompts—can significantly improve final results
  • Prompt engineering is crucial: specifying genre, mood, tempo, and instrumentation leads to more predictable outcomes
  • There is a trade-off between generation speed and output complexity; higher quality or longer tracks may take slightly longer to generate

Tips & Tricks

  • Use clear, genre-specific language in prompts (e.g., "upbeat electronic dance track with female vocals and catchy chorus")
  • For vocal tracks, provide concise, well-structured lyrics; the model aligns melody and rhythm to the given text
  • Experiment with emotional modifiers (e.g., "sad ballad," "energetic rock anthem") to influence mood and vocal delivery
  • Adjust voice modifiers and effects (such as pitch, speed, echo, or robotic effects) to tailor the vocal character
  • Utilize iterative prompting: generate a draft, review, and refine the prompt or lyrics for improved results
  • For instrumental music, omit lyrics and focus on describing instrumentation, tempo, and atmosphere
  • To achieve specific production styles, mention references (e.g., "in the style of 80s synthpop" or "cinematic orchestral score")

Capabilities

  • Generates full-length, studio-quality music tracks from text prompts, including both instrumental and vocal compositions
  • Supports a wide range of genres, from pop, rock, and jazz to electronic, classical, and traditional music
  • Can synthesize natural-sounding vocals in multiple languages, aligning melody and rhythm to provided lyrics
  • Offers advanced voice controls, including emotion, pitch, speed, and vocal effects (e.g., echo, robotic, lo-fi)
  • Delivers rapid generation with low latency, suitable for real-time creative workflows
  • Adapts to diverse creative needs, from background music to complete songs with custom lyrics
  • Maintains high audio fidelity and professional arrangement quality across outputs

What Can I Use It For?

  • Professional music production for advertising, film, and video game soundtracks, as documented in industry blogs and technical articles
  • Rapid prototyping of jingles, theme songs, and background music for marketing and branding projects
  • Creative songwriting and demo creation for musicians and independent artists, as shared in community forums and GitHub repositories
  • Automated content generation for podcasts, YouTube videos, and social media, based on user experiences and reviews
  • Educational tools for teaching music theory, composition, and arrangement, as discussed in technical discussions
  • Personal creative projects, such as generating custom birthday songs or unique audio gifts, as highlighted in user showcases
  • Industry-specific applications, including automated voiceovers and musical cues for smart devices and interactive media

Things to Be Aware Of

  • Some users report that highly complex or ambiguous prompts may produce less coherent or musically focused results
  • The model’s vocal synthesis is generally praised for naturalness, but may occasionally sound synthetic or lack emotional nuance in certain languages or genres
  • Performance benchmarks indicate fast generation times, but resource requirements may increase with longer or higher-quality tracks
  • Consistency across multiple generations can vary; iterative refinement is often necessary for optimal results
  • Positive feedback highlights the model’s versatility, ease of use, and ability to quickly generate professional-sounding music
  • Common concerns include occasional artifacts in vocal tracks, limited fine-grained control over arrangement details, and the need for post-processing in some cases
  • Experimental features, such as advanced voice cloning or multi-language support, may be subject to ongoing updates and improvements

Limitations

  • The model may struggle with highly intricate musical structures or unconventional genres not well represented in its training data
  • Fine control over specific arrangement elements (e.g., precise instrument placement, advanced mixing) is limited compared to manual production
  • Not optimal for scenarios requiring human-level emotional depth or nuanced vocal performance in all languages and styles

Pricing

Pricing Detail

This model runs at a cost of $0.030 per execution.

Pricing Type: Fixed

The cost remains the same regardless of which model you use or how long it runs. There are no variables affecting the price. It is a set, fixed amount per run, as the name suggests. This makes budgeting simple and predictable because you pay the same fee every time you execute the model.