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Three features launched alongside v5.5: Voices, Custom Models, and My Taste.

CEO Mikey Shulman described v5.5 as "our best and most expressive model yet, a model that doesn't just help create music, but fully reflects the person making it." That's a shift in positioning. Suno stopped describing itself as a text-to-music generator and started describing itself as a personalization engine. Which sounds like marketing language until you actually use the new features.

Voices is voice cloning. You upload a cleared sample of yourself or a voice you have rights to, and Suno generates songs that sing in that voice. It was the most requested feature in the platform's history, per their own blog. For style prompts, this changes the vocal direction layer. You no longer need to describe the timbre you want through descriptors. Timbre gets locked by the Voices feature. But delivery style still matters: "breathy and intimate" versus "powerful and belted" produces meaningfully different output even with a cloned voice active.

Custom Models lets Pro and Premier users build a style model from their existing song catalog. Feed it tracks you've made or approved, and the model learns to default toward your sonic direction. Your style prompt now works alongside a personalized model rather than against a general one. For creators with an existing body of work, this is real leverage.

My Taste is the preference layer. It watches what you keep versus what you regenerate and adjusts its sense of what counts as good output for your account. Vague prompts gradually improve for users with platform history because the model fills in ambiguous decisions using your established preferences.

(I wasn't entirely convinced My Taste would matter much for experienced prompters who already write specific style fields. After a few months of heavy use, I underestimated it. Vague prompts from returning users are noticeably better now.)

Two mechanics changed between v4.5 and v5.5.

First: evocative language now outranks raw technical language for several parameters. On v4.5, "120 BPM" was a reliable anchor. The model followed numeric tempo consistently. On v5.5, pairing a BPM number with a descriptive pace word produces more accurate results. "95 BPM, slow and deliberate" beats "95 BPM" on its own. Combining both anchors is the current best practice. The model became better at interpreting emotional tempo language, so giving it a numeric target and a descriptive qualifier covers both interpretation modes.

Second: bare genre labels lost precision. "Synthwave" on v4.5 pointed to a specific sonic territory. On v5.5, the model's interpretation of genre labels broadened. Standalone genre names produce wider, more averaged output. Adding an era anchor and an artist reference brings the precision back. "Synthwave" is now a vague direction. "1980s synthwave, Kavinsky and Perturbator influence, retrofuturistic" gives the model actual targets to work from.

(Side note: this is not a bug or a regression. v5.5 is trying to understand creative intent at a higher level rather than matching keywords mechanically. But for users who wrote precise v4.5 prompts expecting technical compliance, the output can feel less predictable until you adjust.)

Everything from v4.5 still points in the right direction. But specificity requirements have increased, not decreased.

Every style prompt that produces consistent professional output follows the same structure. Seven components, each controlling a distinct dimension.

Genre + Era is the foundation. Name the genre and add a decade or period. "2000s garage rock revival" beats "indie rock" beats "rock."

Tempo works best as a pair on v5.5. BPM number plus a descriptive pace word together. "110 BPM, mid-tempo drive" outperforms "110 BPM" on its own.

Mood is where most prompts leave the most money on the table. Suno responds to emotional language reliably. Words that work consistently: melancholic, euphoric, haunting, bittersweet, triumphant, wistful, restless. Evocative descriptors beat generic ones. "Melancholic" beats "sad."

Instruments should describe the character of the sound, not just the instrument type. "Warm fingerpicked nylon guitar" over "guitar." "Dusty upright bass" over "bass." The material descriptor changes how the model positions the instrument in the arrangement.

Vocal Style needs three things: tone category, delivery style, any specific technique. If using the Voices feature, you still need delivery style because timbre is locked but phrasing and dynamics are still prompt-guided.

Era gives the model a calibration point beyond genre. "2010s indie" versus "modern" produces different production aesthetics that genre labeling alone wouldn't capture.

Reference is the most efficient descriptor in the formula. One artist name communicates reverb, production density, vocal treatment, chord voicing tendencies, and tempo feel simultaneously. "Influenced by Bon Iver" replaces forty descriptive words and is more accurate than most of them.

Total descriptor count sweet spot: four to seven. Under four, output is generic. Over seven, the model averages across the instructions rather than following them.

Here is something worth understanding if you're working on v5.5. My Taste doesn't only affect the model's defaults. It affects how ambiguous terms in your style field get resolved.

When your prompt says "melancholic indie," the model faces dozens of micro-decisions: how much reverb, chord voicing tendencies, whether the vocal sits forward or back in the mix, tempo within a range, production density. A new user gets those decisions made by the general model. A user with several months of My Taste data gets those decisions resolved toward their established preferences.

Two users entering the exact same prompt will hear different tracks. And a user with strong My Taste history can get away with shorter, less precise prompts than a new user needs to write. If your outputs feel generic while other people describe getting precision from simple prompts, My Taste history is likely part of the gap. It's a compounding feature. Give it time.

All tested in Custom Mode. Copy directly into the style field.

Late night study Lo-fi hip hop, 75 BPM, warm and introspective, dusty vinyl crackle, mellow jazz piano, soft boom bap drums, no vocals, late night desk atmosphere

Acoustic morning Acoustic chill, 82 BPM, peaceful and gentle, fingerpicked nylon guitar, soft brush snare, warm bass, whispered male vocals, early morning stillness

Smooth lounge Jazz lounge, 88 BPM, sophisticated and relaxed, warm tenor saxophone, Rhodes piano, walking upright bass, brushed drums, 1960s late night atmosphere

Coastal chill Neo soul chill, 78 BPM, warm and unhurried, soft Rhodes, gentle plucked bass, light percussion, airy female vocals, early afternoon

Deep house Deep house, 122 BPM, groovy and hypnotic, warm analog synth bass, chopped vocal samples, four on the floor kick, shimmering hi-hats, late night underground

Synthwave (v5.5 adjusted) 1980s synthwave, 110 BPM, nostalgic and cinematic, lush analog pads, arpeggiated sequences, gated reverb drums, no vocals, retrofuturistic, influenced by Kavinsky and Perturbator

Ambient electronic Ambient electronic, 68 BPM, ethereal and expansive, layered synth textures, granular processing, subtle glitch, no vocals, influenced by Tycho and Boards of Canada

Future bass Future bass, 150 BPM, euphoric and emotional, heavy synth swell, chopped vocals, supersaws, punchy kick, festival energy

Industrial techno Industrial techno, 135 BPM, relentless and cold, distorted kick, mechanical hi-hats, dark synth pads, no vocals, Berlin underground

Bedroom pop electronic Bedroom pop, 100 BPM, dreamy and warm, lo-fi drum machine, warm synth pads, soft electric piano, breathy close vocal, nostalgic

Boom bap Boom bap hip hop, 90 BPM, gritty and authentic, chopped soul sample, punchy drums, vinyl texture, storytelling male rap, 1990s New York, influenced by Nas

Trap Trap, 140 BPM, dark and cinematic, heavy 808 bass, sharp hi-hat rolls, atmospheric synth pads, confident male rap, modern Atlanta

Lo-fi rap Lo-fi rap, 80 BPM, introspective and hazy, jazzy piano loop, soft drums with vinyl crackle, relaxed male flow, late night thoughts, influenced by Mac Miller

UK drill UK drill, 140 BPM, cold and menacing, sliding 808 bass, sparse melancholic strings, aggressive flow, London street narrative

Jazz rap Jazz rap, 85 BPM, sophisticated and conscious, live upright bass, brushed snare, sampled piano, thoughtful male vocals, influenced by A Tribe Called Quest

Cloud rap Cloud rap, 130 BPM, ethereal and hazy, pitched-up vocal samples, spacey pads, distant 808, autotuned male vocals, Travis Scott influenced

Dream pop Dream pop, 95 BPM, ethereal and bittersweet, shimmering reverb guitars, soft synth pads, breathy female vocals with layered harmonies, late 2010s aesthetic, influenced by Beach House

Indie pop Indie pop, 118 BPM, bright and restless, jangly electric guitars, punchy synth bass, handclaps, catchy female vocals with harmonies, 2020s bedroom pop

Dark pop Dark pop, 92 BPM, moody and minimal, pulsing synth bass, close mic breathy female vocals, sparse production, Billie Eilish influenced

Power pop Power pop, 132 BPM, energetic and anthemic, crunchy guitars, driving drums, melodic bass, male vocals, arena chorus, influenced by Weezer

1985 synth pop 1985 synth pop, 118 BPM, cold and catchy, analog synths, driving drum machine, detached female vocals, new wave sensibility, New Order influenced

K-pop K-pop, 128 BPM, energetic and confident, punchy drums, bright synths, polished group vocals, dance break, layered production

1970s arena rock 1970s arena rock, 126 BPM, powerful and confident, overdriven guitar riffs, Hammond organ, thundering drums, raspy male vocals, Led Zeppelin influenced

Post punk Post punk, 130 BPM, angular and dark, jangly chorus guitar, driving bass, mechanical drums, detached baritone, cold atmosphere, Joy Division influenced

Shoegaze Shoegaze, 112 BPM, dreamy and overwhelming, walls of distorted guitar, buried vocals, heavy reverb, melancholic, 1990s aesthetic, My Bloody Valentine influenced

Grunge 1990s Seattle grunge, 128 BPM, raw and angry, heavy distorted guitars, thundering drums, anguished male vocals, Nirvana influenced

Blues rock Blues rock, 98 BPM, gritty and soulful, slide guitar, swampy groove, raspy male vocals, southern heat, Black Keys influenced

Math rock Math rock, 120 BPM, complex and angular, interlocking guitar patterns, unconventional meter, precise drumming, no vocals, 2010s indie

Punk 1970s punk, 180 BPM, fast and raw, three chord guitar, driving drums, shouted male vocals, angry and anti-establishment, Ramones influenced

Modern R&B Modern R&B, 85 BPM, sultry and late night, warm synth pads, 808 bass, minimal hi-hats, smooth falsetto male vocals, influenced by The Weeknd and Frank Ocean

Neo soul Neo soul, 95 BPM, warm and organic, Rhodes piano, round bass guitar, live drums with swing, rich female vocals, uplifting, influenced by Erykah Badu

Gospel soul Gospel soul, 88 BPM, powerful and uplifting, church organ, full choir, call and response, triumphant, live percussion

1990s R&B 1990s R&B, 92 BPM, smooth and romantic, warm synths, gentle drum machine, lush harmonies, silky male vocals, New Jack Swing influence

Modern country Modern country, 108 BPM, heartfelt and nostalgic, steel guitar, acoustic strumming, steady kick, authentic male vocals with twang, small town storytelling

Indie folk Indie folk, 90 BPM, intimate and melancholic, fingerpicked acoustic, soft banjo, gentle strings, whispered male vocals, campfire atmosphere, Bon Iver influenced

Bluegrass Bluegrass, 132 BPM, energetic and joyful, banjo, fiddle, upright bass, rapid picking, tight harmonies, Appalachian tradition

Americana Americana, 100 BPM, weathered and honest, acoustic guitar, pedal steel, upright bass, gravelly male vocals, highway and open country

Epic orchestral Epic cinematic, 100 BPM, triumphant and building, full orchestra with brass fanfare, soaring strings, thundering timpani, choir, movie trailer energy

Corporate background Corporate background, 108 BPM, professional and optimistic, clean acoustic guitar, light piano, gentle percussion, no vocals, uplifting, presentation ready

Dark ambient Dark ambient, 55 BPM, tense and unsettling, slow evolving drones, deep bass rumble, distant metallic textures, no vocals, horror film score aesthetic

Ambient meditation Ambient meditation, 60 BPM, peaceful and transcendent, soft drone pads, singing bowls, gentle wind chimes, no vocals, deep relaxation

Sci-fi ambient Sci-fi ambient, 65 BPM, futuristic and spacious, evolving synth textures, sub-bass pulse, processed string textures, no vocals, Blade Runner 2049 aesthetic

Vocals are where most prompts underperform most. Here are the descriptors that produce consistent results, organized by what they actually control.

By tone:

By delivery:

For instrumental tracks: State both "no vocals" and "instrumental only." Using only one sometimes still results in background vocal texture on v5.5. The redundancy is intentional.

This feature launched with v5 in September 2025. Most prompt guides haven't covered it yet, which is strange because it's one of the most practically useful additions to the platform.

Add phrases to the style field describing what you don't want. "No distortion," "avoid heavy drums," "no electric guitar," "minimal vocals." Works best for preventing the model from defaulting to strong genre conventions you specifically want to sidestep.

Writing "jazz lounge, no saxophone, piano-led" gives the model a cleaner instruction than hoping it picks a saxophone alternative on its own. Sax is so strongly associated with the jazz lounge template that without the negative instruction, the model will almost always reach for it.

I got this wrong initially. I thought negative instructions would confuse the model or create artifacts. They do neither on v5.5. Particularly useful for hybrid genres where the model has a tendency to over-lean into one parent genre and underrepresent the other.

These go in the lyrics field, not the style field. Common mistake.

Without structure tags, generation runs continuously and undifferentiated. Songs come out flat. The dynamics that make a track feel like a real track come from structure. Use them every time you write lyrics.

One v5.5-specific note: [Instrumental] at the start of your lyrics and [No Vocals] at the end together is the most reliable combination for truly vocal-free output. Using only one of the two sometimes lets background vocal texture through.

Most people making Suno content are piling into lo-fi study music. These five categories have better CPM, dedicated audiences, and far less saturation at this moment. Credit to James Palm's March Medium article for surfacing the original niche breakdown. The prompts below are updated for v5.5 specifically.

Dark academia ambient targets students, readers, and gothic literature audiences. This aesthetic dominates TikTok every autumn and winter. Watch time tends to run among the longest of any ambient category. Victorian library rain ambient, 60 BPM, D minor, muffled rain against windows, ticking grandfather clock, fireplace crackle, classical cello drone, scholarly and melancholic, no vocals

Deep space and astral pulls from the sleep and meditation audience. Higher monetization than generic rain sounds, similar audience size. Deep space ambient, 40 BPM, C major, zero gravity feeling, evolving synth pads, sub-bass rumble, weightless and infinite, no vocals

Luxury lounge is background music for premium venues and affluent YouTube audiences. CPM runs noticeably higher than general chill music. Upscale hotel lounge jazz, 92 BPM, sophisticated and unhurried, solo piano, double bass, brushed snare, no vocals

Fantasy RPG ambient has unusually long watch times. Tabletop players, gamers, and writers use it for hours. Retention metrics in this niche are among the best for any ambient category. Fantasy tavern ambient, 80 BPM, warm and adventurous, lute, light flute, fireplace crackle, distant rain, no vocals

Healing frequencies has a larger and more committed audience than most people expect. The 432 Hz and binaural beats space runs deep. 432 Hz healing ambient, 55 BPM, crystalline singing bowls, soft flute, gentle nature sounds, peaceful and restorative, no vocals

None of these are crowded yet. Worth noting: they won't stay this way.

What is the character limit for the style field? 200 characters. Prompts landing between 120 and 150 characters tend to outperform ones packed to the limit on v5.5. Fewer strong descriptors beat more weak ones.

Can I use artist names in prompts? Yes, and you should. Artist names are among the most efficient descriptors available. One thing to watch: very obscure artists sometimes get averaged with more famous artists who have similar names. Stick to well-known references, or pair them with additional descriptors to anchor the model's interpretation.

How many descriptors is too many? Four to seven is the tested sweet spot on v5.5. Above seven, the model starts averaging across instructions rather than following them. Below four, output skews generic. Users with strong My Taste data tend to work well at the lower end of that range.

Why does the same prompt give different results every time? Suno uses temperature-based generation. There is inherent randomness per output. That is a feature, not a bug. Standard practice: generate five to ten variations per prompt and keep the best one. Small changes, swapping one adjective or shifting BPM by 10, can produce meaningfully different results.

Do BPM numbers actually work on v5.5? They work best paired with a descriptive pace word. "90 BPM, slow head-nod groove" outperforms "90 BPM" alone on the current engine. Use both.

Does Custom Mode affect quality? Yes, consistently. Custom Mode separates your style field from your lyrics, giving the model clean independent instructions for each. Everything in this article assumes Custom Mode.

What changed most about prompting between v4.5 and v5.5? Three things: the shift toward evocative tempo language alongside BPM numbers, the need to add era anchors to genre labels for precision, and the addition of negative prompting as a real tool. My Taste is the fourth, though it functions more as a multiplier on prompt quality than a replacement for it.