Suno has released v5, the latest version of its AI music generation tool, which demonstrates significant technical improvements in audio quality and arrangement complexity over its predecessor. Despite these advancements, the model continues to struggle with producing vocals and musical styles that possess genuine emotional depth and the imperfections characteristic of human artists.
Key Takeaways
- Suno's new v5 model offers clearer audio, better instrument separation, and more complex song structures compared to the previous v4.5+ version.
- The AI demonstrates a better understanding of isolated sounds and stereo effects, indicating a more sophisticated internal model of music production.
- Despite technical gains, v5 struggles to accurately replicate specific genres, eras, or the intentional imperfections found in lo-fi or raw musical styles.
- Vocals generated by v5, while clearer, are often described as overly polished and lacking the unique emotional qualities of human performances.
Technical Improvements in Audio Fidelity
The latest update to Suno's AI music platform, version v5, introduces noticeable enhancements to the overall sound quality of its generated tracks. Users will find a reduction in audio artifacts and a much cleaner separation between different instruments within a song.
In previous versions, it was common for melodic elements like guitar, bass, and synthesizers to blend together into a muddled sound. Suno v5 addresses this by producing cleaner mixes where individual instruments are more distinct and occupy their own space in the audio field.
Henry Phipps, a product manager at Suno, highlighted the model's improved capabilities during a demonstration. He pointed to a generated track featuring a flute-like synthesizer with a ping-pong delay effect. Phipps noted this was a new development not heard in previous models.
"What that says to me is that the model understands that this is an isolated sound that’s being affected and needs to be reproduced faithfully in different parts of the stereo field," he explained.
This suggests that Suno's AI is not simply applying effects in a traditional way. Instead, it is identifying specific sounds and approximating how they should sound with effects like stereo delay, based on its training data.
Complex Arrangements and Song Structures
Another significant area of improvement in Suno v5 is the complexity of its musical arrangements. The new model generates songs with more variation and musical flourishes, preventing them from becoming overly repetitive.
The previous model, v4.5+, often defaulted to a standard verse-chorus-verse structure. In contrast, v5 frequently introduces more sophisticated elements like pre-choruses, post-choruses, and multiple bridge sections or breakdowns.
From Simple to Complex
While v4.5+ typically followed basic song formulas, Suno v5 demonstrates an ability to build a track with a more defined arc, creating a sense of progression rather than just repeating distinct sections.
This increased complexity can lead to more engaging and dynamic musical pieces. The AI appears to have a more developed sense of how a song should evolve from beginning to end, a crucial element of musical composition.
Challenges in Genre and Style Replication
While Suno claims v5 has a better understanding of genre, testing reveals that its performance can be inconsistent. When prompted with specific and nuanced genre descriptions, the AI's output does not always align with the user's intent.
For example, a prompt for "modern avant R&B with glitchy, but funky drums" produced results that were generally downtempo but lacked the specific experimental edge requested. Both v5 and its predecessor got close but did not fully capture the intended style.
The Quest for Imperfection
The AI particularly struggles when asked to create music with intentional flaws, such as lo-fi indie rock. A prompt asking for "early ‘90s lo-fi indie rock recorded on a 4-track cassette recorder with off key vocals and slightly out of tune guitars" resulted in polished, modern-sounding rock tracks.
Instead of the loose, slacker-rock sound associated with bands like Pavement, Suno v5 produced tracks with clean, powerful guitar chords more reminiscent of later bands like Arctic Monkeys. The model seems unable to replicate the raw, unpolished aesthetic that defines certain genres.
This difficulty extends to era-specific prompts. When asked for "late 1970s krautrock," v5 often delivered music with an '80s synthpop feel, while the older v4.5+ model came closer to the authentic sound, aside from the vocals.
The Uncanny Valley of AI Vocals
The most significant limitation of Suno v5 remains its vocal generation. While the company initially described the vocals as having "human-like emotional depth," this language has been revised to "natural, authentic." However, many users find the output still falls short of this description.
The Missing Element of Human Flaw
The core issue is the AI's tendency to produce technically perfect but emotionally sterile vocal performances. Every vocal is perfectly on pitch, layered with harmonies, and coated in reverb, even when the user explicitly requests a dry, raw performance.
Phipps acknowledged the challenge, stating that the models do not yet understand specific descriptions of recording techniques or effects. He explained that the vocal performance is primarily influenced by the lyrics and the general mood of the prompt.
"The way the vocal is performed is most influenced by the lyrics and the general mood," Phipps said.
This means that asking for "no reverb" or "unprocessed vocals" is often ignored by the system. In one test, a request for a solo a cappella female vocal with no effects resulted in a track with heavy reverb, harmonies, and even a bass-like vocal accompaniment.
Ultimately, the AI lacks the ability to replicate the very imperfections that convey powerful emotion in human singing. The crack in a singer's voice, a slight off-key warble, or the sound of an exhausted breath are elements that carry immense emotional weight. Suno v5 can mimic sadness or joy on a superficial level, but it cannot feel the emotion connected to the words it sings, resulting in a performance that is technically impressive but emotionally hollow.
