Suno v4.5

Prompting, Style Control & Brackets

• Many users say Suno often ignores complex style prompts, especially mixed or niche genres; it tends to pick one genre or go generic.
• Some report v4.5 adheres better if you use short genre labels plus rich natural-language descriptions and structural markers like [intro] [chorus] [bridge] [instrumental].
• Text in brackets is generally not sung and can “steer” arrangement (e.g. [dramatic synths, pulsing techno bass], [bass drop], [whispered], [Interrupt]).
• There’s consensus that it pays more attention to lyrics than to style tags; mismatched lyrics/style often results in the style being ignored.

Genre Fidelity & Language Quality

• Several demo genres are criticized as mis-labeled: “Cajun synthpop chant” sounding like country, “Acid House” not acid, “Jungle” more like liquid DnB, many electronic styles feeling generic or wrong.
• Others find mainstream or well-represented genres impressively accurate (e.g. French ska, klezmer, some jazz).
• Non-English output is a major weak spot: users report gibberish or wrong vowels in Urdu/Hindi and other languages; others say Spanish, French, and custom Japanese lyrics can work well.

Lyrics, Vocals & Audio Artifacts

• Lyrics are widely seen as Suno’s weakest aspect: clichéd, “cringe”, poor rhythmic fit, wrong stresses and syllable counts; some speculate it uses a weaker LLM.
• Users often prefer generating lyrics with another model and feeding them into Suno; v4.5’s “Remi” lyric option is described as more unhinged/creative.
• Vocals retain a synthetic “vocaloid/tinny” quality versus competitors; high frequencies are described as “washy” or now “damped”.
• Classical phrasing, meter, and some vocal pronunciations remain unreliable.

UI, UX & Access

• The new genre-exploration UI is praised as fun and mobile-friendly, but others find it hard to read, jittery, or slow on some desktops; some actions (titles, downloads) are non-obvious, especially on mobile.
• Requests include easier downloading, better tutorials/prompting guides, and an API.

Use Cases: Toy, Tool, and Function

• Casual users enjoy it as a toy for joke songs, genre mashups, alternative covers, wedding gags, or sleep/background music.
• Some musicians use it to prototype songs, generate vocals over human-made instrumentals, or quickly realize ideas that would be too expensive to produce traditionally.
• A notable thread highlights “functional music”: emotionally supportive tracks (e.g. therapy/grounding, meditation, educational rap, kindergarten songs) that would never be commercially commissioned.

Impact on Musicians & Motivation

• Some composers feel energized and use Suno as part of a serious workflow; others say AI music has “killed” their motivation, given how quickly acceptable results can be generated without years of study.
• There’s an extended debate over whether AI music is just another tool (like DAWs and presets) vs. something that directly displaces creative labor in a qualitatively new way.

Legal, IP & Ethics

• Commenters note ongoing lawsuits from major labels and collecting societies over unlicensed training; views differ on whether Suno’s use is “obviously illegal” or plausibly transformative fair use.
• Fair use conditions (profit vs non-profit, market substitution, transformative use) are argued from multiple angles; consensus is that it’s legally unresolved.
• Some see using others’ catalogs as “misuse of collective IP”; others dismiss IP as a legal fiction or note that human songwriters are also influenced by existing music.
• Billing language like “commercial use rights for songs made while subscribed” feels to some like “you’ll own nothing”; others worry about Content ID collisions.

Originality, Taste & Cultural Role

• Critics argue Suno averages the training distribution, yielding competent but “cookie-cutter” music lacking true surprise; especially obvious to trained ears in rhythm, harmony, and structure.
• Defenders reply that most human pop is also formulaic, and for many listeners Suno is already indistinguishable from low–mid-tier commercial music, especially as anonymous background audio.
• There’s deep disagreement on whether AI music can build artist-like followings and cultural impact, or will remain an anonymous commodity while human artists remain central for emotionally meaningful work.

Feature Gaps & Future Directions

• Frequently requested: open-source models, multi-track stems, MIDI/sheet-music or DAW project export, finer-grained per-instrument control, more robust cover/transform (“track2track”) workflows, better spatial control and genre purity.
• Some argue the real opportunity is not “one-prompt full songs” but interactive, stem-level collaboration tools for musicians—“vibe coding” inside something that feels like a simplified DAW.