Comparative Analysis of Suno and Udio AI Music Generation Systems
Suno and Udio are two prominent AI-driven music generation platforms that emerged in 2023–2024, both capable of turning text prompts into full songs with vocals and instrumentation. Suno (by Suno Inc.) launched earlier with a system that can produce songs (including lyrics, vocals, and backing music) from text, while Udio (by Uncharted Labs) is a newer entrant created by ex-Google DeepMind researchers, acclaimed for its high audio quality. This report examines publicly available information about their codebases, model architectures, and underlying technologies, highlighting key similarities and differences.

by Andre Paquette

Suno: Open-Source Foundations
Suno's technology builds on an open-source foundation. The company's first major model, Bark, was released as a public GitHub project in early 2023. Bark is described as the first open-source, transformer-based "text-to-audio" model, and it is available under the MIT License for commercial use.
The Bark codebase is written in Python (PyTorch), with its training code architecturally inspired by Andrej Karpathy's NanoGPT (a minimalist GPT implementation). This indicates that Bark uses a GPT-like transformer architecture under the hood.
In fact, Suno's team has acknowledged borrowing code from NanoGPT and building a "foundation model" for audio generation from scratch. The open-source release of Bark has enabled the research community to inspect and use the model freely, making Suno relatively transparent about this part of their stack.
Suno's Transformer Pipeline (Bark)
Semantic Text Model
A causal transformer (about 80 million parameters) that takes text input and produces a sequence of semantic tokens capturing the content/meaning of the text. This can be thought of as a high-level audio content representation (similar to a transcript or intended phonemes, including timing and intonation cues). It has a vocabulary of 10,000 tokens for the semantic layer.
Coarse Acoustic Model
A second causal transformer (~80M params) that takes the semantic tokens and predicts the first set of low-frequency audio codec tokens (the "coarse" audio representation). Specifically, Bark's coarse model generates the first 2 codebooks needed by a neural audio codec.
Fine Acoustic Model
A third transformer (~80M) which is a non-causal model (an autoencoder-like transformer) that refines the audio by predicting the remaining codec tokens (the "fine" detail) given the coarse tokens. This model fills in the higher-frequency or subtle details in the audio by outputting the last 6 codebooks of the codec representation.
Audio Decoder (Codec)
After obtaining all the codec tokens (8 codebooks in total, 2 from the coarse model + 6 from the fine model), Bark uses a pretrained decoder to convert these tokens into a waveform. Suno uses Meta AI's EnCodec as the neural audio codec.
Suno's Pipeline Approach
Text to Semantic Tokens
The input text is processed to create semantic tokens that capture meaning and intonation.
Semantic to Coarse Audio
These semantic tokens are transformed into a basic audio representation.
Coarse to Fine Audio
The basic audio is refined with higher-frequency details.
Tokens to Waveform
All tokens are decoded into the final audio waveform.
This pipeline (text → semantic tokens → coarse audio tokens → fine audio tokens → waveform) is reminiscent of recent research like Google's AudioLM/MusicLM, which also use semantic representations and codec tokens. Indeed, Bark's design draws on similar principles: the use of self-supervised audio token training enabled Suno to train on untranscribed audio.
Suno's Training Approach
Collect Audio Data
Train on large amounts of "in-the-wild" audio
Self-Supervised Learning
Learn general audio generation patterns
Text Conditioning
Add text inputs to guide generation
Fine-Tuning
Optimize for specific audio types like speech and music
Instead of relying solely on limited parallel text/audio data, the team trained Bark's models on large amounts of "in-the-wild" audio to learn general audio generation, and only then conditioned on text. This approach yielded a more natural sounding speech/music model, since it learned from real audio of people speaking and singing (capturing prosody, accents, etc.) rather than being narrowly trained on clean studio TTS recordings.
Chirp: Suno's Instrument Model
Specialized for Instruments
In addition to Bark (which primarily handles vocals and general audio), Suno has mentioned a second model called Chirp that focuses on generating instrumental music.
Shared Architecture
According to Suno, Bark specializes in singing/voices and Chirp in instrumental backing tracks. Both models reportedly share the same underlying architecture and were trained on massive music datasets to capture patterns of melody, harmony, rhythm, and genre.
Combined Output
In practice, Suno's platform generates songs by producing a vocal track with Bark (singing the lyrics) and an accompaniment track with Chirp, then mixing them.
Suno's Programming Languages & Tools
Python Implementation
Suno's models are implemented in Python, using PyTorch deep learning framework.
NanoGPT Foundation
The reliance on Karpathy's NanoGPT codebase for training suggests standard transformer training loops in PyTorch.
EnCodec Integration
The decoding of EnCodec tokens likely uses Meta's open-source EnCodec implementation (PyTorch-based as well).
GPU Optimization
For inference and service deployment, Suno runs these models on GPUs (the team achieved significant speed optimizations, e.g. 2× faster on GPU and 10× on CPU in one update), making generation feasible within tens of seconds.
Suno's System Components
1
User Interface
Suno's web app and Discord bot serve as the UI, but under the hood they call into these PyTorch models (wrapped perhaps in an API).
2
Core Models
The overall system includes Bark and Chirp for audio generation.
3
Safety Measures
Suno built a classifier to detect Bark-generated audio as a safety measure against misuse.
4
Lyric Generation
They have also integrated an LLM (ChatGPT) to help users generate lyrics if they only provide a concept, indicating a pipeline of multiple AI components.
Suno's Features and Capabilities
Versatile Audio Generation
Bark was released as a general text-to-audio model, not just TTS – it can produce not only speech but also music snippets, background noises, and sound effects. This broad capability comes from training on diverse audio.
Multilingual Support
In the context of full songs, Suno's system is able to generate multilingual vocals. Bark can recognize the language of input text and even attempt a native accent. Users can prompt songs in various languages, and Bark will sing in that language (English being the most polished so far).
Voice Customization
The platform also provides some voice customization: Suno offered preset voices or the ability to influence voice via a "history prompt" (feeding a sample or an ID of a voice as conditioning). This indicates Bark supports a speaker embedding to vary the voice timbre if desired – useful for switching between a female vocalist, male vocalist, etc.
Genre Versatility
On the music side, Chirp can handle a variety of genres and instrumentations. Suno's demo prompts often included explicit structure cues (e.g. "Verse, Chorus") or musical style hints.
Suno's Song Structure Control

Default Structure
Suno's default lyric structuring resulted in a verse/chorus format
User Control
Users can coerce a stronger "hook" by repeating a chorus in the prompt
Section Markers
The system automatically tries to give the song a structure, but users have some control through the prompt
Users can specify sections or use musical notation cues like putting lyrics in quotes or adding music note symbols to indicate singing. Suno's output duration has been approximately up to 2 minutes at most, often around 1 minute for many prompts, likely due to model context length limits or design choice.
Suno's Scalability
240M
Total Parameters
Across all model components
2x
GPU Speed Improvement
Through optimization efforts
10x
CPU Speed Improvement
Making local running possible
Scalability-wise, Suno runs in the cloud and also lets users run Bark locally (with enough VRAM). The model sizes (on the order of a few hundred million parameters total) are relatively lightweight, which helps with speed and potential real-time use cases. In fact, Suno stated that with further optimizations, models like Bark could eventually run faster than real-time for applications like voice assistants.
Suno's Research and Development
1
Founding
Suno's team of founders are engineers (formerly at Kensho) who are also musicians, and their motivation was to push audio generation to do music, not just speech.
2
Bark Release
Bark's release was a milestone demonstrating that relatively small transformers with the right training can generate coherent speech and song from text.
3
Iterative Improvement
Subsequent versions (Suno v2, v3, etc.) focused on improving quality and song realism. By early 2024, Suno had a v3 model running in their app, which was considered state-of-the-art until Udio appeared.
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Content Safeguards
They implemented filters to avoid explicit copyrighted melodies: Suno's CEO stated they use automated filters and safeguards to ensure outputs are novel and not copies of training songs.
Suno's Business Model and Legal Challenges
Tiered Access
Suno has both free and paid tiers, suggesting that heavy users (or those who want faster generation) could subscribe – the service aspect means Suno had to invest in serving infrastructure and perhaps moderate the content.
Copyright Concerns
In June 2024, Suno (along with Udio) was targeted by major record labels in a lawsuit alleging the models were trained on copyrighted songs.
Originality Emphasis
In response, Suno emphasized their commitment to originality and the use of such filters to prevent memorized content leaking.
These public disclosures confirm that Suno's training set likely included a broad scrape of music data (hence the concern) and that Suno augmented their system with a form of content similarity checker to flag or avoid generating something too close to existing songs.
Summary of Suno's Technology

Open-Source Foundation
Partly open (Bark on GitHub) and built in Python/PyTorch
Multi-Model Architecture
Multi-model transformer architecture (each around 80M params)
Audio Token Mapping
Maps text to audio via discrete codec tokens
Component Integration
Uses open-source components (EnCodec codec, NanoGPT training code)
Dual Model Approach
Uniquely combines a vocal model and an instrument model (Bark and Chirp)
Suno's Strengths and Limitations
Strengths
  • Multilingual support
  • Highly realistic voice timbre (thanks to training on real audio)
  • Creative audio outputs beyond speech (music, sound effects)
  • Lightweight architecture allows reasonably fast generation
  • Open-source components enable community development
Limitations
  • Audio fidelity can exhibit artifacts
  • "Model-esque" quality (some fuzziness)
  • Less distinct instrument separation compared to human-produced audio
  • Limited song length (typically 1-2 minutes)
  • Requires significant computational resources for real-time generation
The company has continually improved these aspects, but as we'll see, Udio arrived claiming even better quality.
Udio: Proprietary System by Industry Experts
Closed-Source System
Udio is a closed-source, proprietary AI music generation service launched on April 10, 2024.
Expert Founding Team
It is developed by Uncharted Labs, Inc., a startup founded by three researchers (David Ding, Charlie Nash, Yaroslav Ganin) who previously led projects at Google DeepMind.
DeepMind Background
Notably, they were involved in Google's generative AI work on Imagen (text-to-image) and an internal project codenamed Lyria.
Rapid Public Release
Udio was initially a closed beta but quickly became public due to the excitement around it.
Udio's Undisclosed Methodology
Unlike Suno, Udio's codebase is not publicly released, and the company has not published technical papers. In fact, contemporary researchers note that Udio's methodology remains undisclosed.
We must rely on official statements and educated guesses from the AI community to infer its architecture. Based on the problem domain and the founders' pedigree, Udio's core model is almost certainly a transformer-based generative model operating on audio token representations (much like Bark or Google's MusicLM).
The results Udio produces – "fully mastered, high-quality" songs with vocals and music – strongly suggest a pipeline involving a neural audio codec and an autoregressive model that generates those tokens conditioned on text.
It's very likely Udio uses a framework similar to MusicLM, which was a Google research model for text-to-music.
Udio's Likely Model Architecture
Text Encoder
Encodes text prompts into an embedding
Hierarchical Transformer
Generates audio codec tokens in multiple stages
3
Audio Codec
Converts tokens to high-quality audio
Audio Output
Produces the final mastered track
MusicLM (2023) used a hierarchical transformer: it encoded text prompts into an embedding (via a text encoder) and then generated audio codec tokens in multiple stages (coarse to fine) to produce music up to ~30s. Google's implementation used their proprietary SoundStream codec (functionally akin to EnCodec) and a text-audio contrastive model (MuLan) for conditioning.
Udio's Vocal Generation Capabilities
Expressive Vocals
We do know Udio's model can handle singing vocals with coherent lyrics (not just humming or vocables) because the app promises "expressive vocals… bring your words to life".
Lyric Integration
Achieving intelligible lyrics in generated singing is a difficult task – it implies that Udio's model explicitly takes into account the lyric text content when generating vocals.
Possible Approaches
There are two possible approaches: one is a two-model system like Suno's (one for lyrics/vocals, one for music), and the other is a single unified model that generates both vocals and instruments together.
Udio's Generation Approach
Text Input
Process user prompt with genre, style, and lyrics
Unified Generation
Generate vocals and instruments together
Audio Mixing
Balance vocals and instruments professionally
Mastering
Apply final audio processing for quality
Udio's marketing suggests a unified generation ("generate a fully mastered track from text prompts" in one go). However, the clarity of vocals and separation from music in Udio's output hints that the system might internally generate vocal and instrumental components distinctly (perhaps separate tracks, which are then mixed).
Udio's Possible Training Data
Multi-Track Recordings
It's possible Udio's training data included multi-track recordings (vocals isolated from instrumentals), allowing the model to learn to output separate stems.
Clear Audio Separation
Indeed, reviewers noted that Udio had very clear vocals and accompaniment, almost as if mixed by a human. This could be an advantage of a multi-track approach.
Unified Learning
On the other hand, handling everything in one model is simpler from an integration standpoint. Udio has not confirmed either design.
Large Model Capacity
Given modern transformers' ability to learn complex patterns, Udio might not need to explicitly split the task.
Udio's Model Size and Quality

Larger Model
What we can infer is that Udio's model is very large and highly trained – perhaps larger than Suno's 240M parameter range.
Quality Leap
The jump in quality ("not merely a step but a giant leap" in quality) suggests a higher-capacity model or more extensive training.
Parameter Scale
Udio might employ a transformer with hundreds of millions or even billions of parameters to capture the richness of audio.
It might also use longer context lengths to generate longer songs coherently. The team has indicated that they are working on extending song duration and context, implying that context length (and model memory) was a constraint in the initial version.
Udio's Input and Conditioning
1
Intuitive Interface
Udio distinguishes itself with an intuitive prompting interface. The user can input a genre/style description, optional lyrics or a thematic "subject", and even reference artists for style inspiration.
2
Prompt Processing
The model uses this textual input to shape the output. This implies that under the hood, Udio likely converts the prompt into some conditioning vectors.
3
Text Encoding
Possibly, it uses a text encoder (like a Transformer or an LLM) to handle arbitrary prompt text (including multiple fields).
4
Prompt Structure
Another possibility is prompt concatenation – e.g., constructing a structured text like: "Genre: pop; Style: Taylor Swift; Lyrics: [full lyrics]" and feeding that as one long text sequence into the model.
Udio's Lyric Processing
Lyric Integration
Feeding full lyrics into the model is crucial if the model is directly generating the singing, because it needs to know the words for each part of the song.
If Udio's model is a single-stage text-to-audio system, it likely takes a lengthy text input (which could include the lyrics themselves) and then generates audio tokens. This is computationally heavy but feasible if the model can handle a few hundred text tokens of input (for the lyrics) and then produce a few thousand audio tokens for the output.
Lyric Generation
Another possibility is that Udio first uses an NLP component to generate lyrics from a given "subject" if the user hasn't written actual lyrics. For instance, if a user only provides a theme ("a song about summer beaches") and style, Udio might call an internal lyric generator (perhaps GPT-4 or a fine-tuned model) to create some verses and chorus, then feed those into the audio model.
Udio hasn't publicly detailed this, but their prompt guidance – "it works better the more you put in: writing lyrics, exploring sound combos…" – suggests they encourage users to supply actual lyrics and detailed direction.
Udio's Language Support
Multilingual Capability
Udio advertises support for multiple languages as well, though early feedback and their own roadmap imply the v1 model was strongest in English (they mention working on more languages soon).
English Dominance
This parallels Suno: start with English dominance, then expand language support.
Ongoing Development
The team is actively working to improve multilingual capabilities in future versions.
So, likely the core model is capable of singing any supplied lyrics, with the strongest performance currently in English.
Udio's Audio Codec and Tools
Neural Audio Codec
While unconfirmed, it is very likely Udio uses a high-quality neural codec such as Meta's EnCodec or a custom variant to intermediate the audio.
State-of-the-Art Approach
Many state-of-the-art music models rely on EnCodec (e.g., Meta's MusicGen used a 32 kHz EnCodec).
Google Background
Alternatively, being ex-Google, they might have reimplemented Google's SoundStream codec or improved it.
Token-Based Generation
Either way, using a codec gives the model a discrete token space to predict, which is necessary for handling the complexity of waveforms.
If Udio uses 24 kHz or 48 kHz audio, the number of tokens per second can be quite high, requiring an efficient generation scheme (possibly two-stage coarse/fine like Bark or even multi-band generation).
Udio's Generation Process
Token Generation
Since Udio can produce ~30 second clips by default, the model must generate on the order of tens of thousands of audio tokens per sample.
Hierarchical Approach
A hierarchical generation (first generate a lower-resolution or coarse sequence, then refine) would be logical to keep this tractable. This is again analogous to MusicLM's approach.
Coarse-to-Fine Pipeline
We can reasonably surmise that Udio's architecture at least involves a coarse-to-fine token generation pipeline, even if it's encapsulated in one overarching model or done in stages.
Autoregressive Generation
There is no indication that Udio uses diffusion models for the final signal. Therefore, a safe inference is Udio employs a transformer decoder model (or series of models) generating EnCodec tokens autoregressively.
Udio's Training Data Requirements
1
1
Music Tracks
The training of such a model would have required a huge dataset of paired text descriptions and songs.
Web Scraping
Since no large public text-song dataset exists with the detail needed, Uncharted Labs likely scraped the web for lyrics and corresponding songs, or relied on music with metadata.
Legal Allegations
The RIAA lawsuit alleges "unauthorized scraping of copyrighted music to train their AI models", supporting the idea that Udio's training corpus was extensive and included popular music tracks (with lyrics).
Significant Investment
The team also raised $8.5M in funding in early 2024, which likely went into obtaining data and training this model on significant compute.
Udio's System and Features
Web Application
Udio's user-facing system is a web app (udio.com) where users input prompts and receive generated songs.
Cloud GPU Infrastructure
Under the hood, it runs on cloud GPUs (the surge of interest at launch caused their servers to strain, with frequent crashes reported due to load).
Impressive Generation Speed
The generation speed is quite impressive: about 40 seconds to get a complete, "mastered" track of 30+ seconds.
Mastered Output
A mastered track means the output isn't raw or quiet – it's loudness-normalized, balanced and ready to publish.
Udio's Unique "Vary" Feature
Iterative Refinement
Udio includes a unique "vary" feature in the UI, which allows users to tweak the result by adjusting certain parameters or regenerating parts.
Variation Generation
For example, after getting an initial song, a user can hit "vary" to maybe change the instrumentation or extend the length.
Technical Implementation
This suggests the system can take an existing generation and continue it or produce variations by re-sampling with different random seeds or prompt adjustments.
Conditional Continuation
Internally, this could mean the model supports conditional continuation (feeding its own output as a prompt to continue for more seconds) or simply that they allow easy re-generation with slight prompt changes.
Udio's design focus is on usability and quick iteration, whereas Suno's early interface was mostly a prompt-and-wait with less post-generation control.
Udio's Content Filtering and Safeguards
Copyright Protection
From day one, Udio emphasized they have automated copyright filters in place to analyze generated music and ensure it's not plagiarizing any existing song.
Originality Verification
David Ding (Udio's CEO) stated that all outputs are checked to confirm they don't match known copyrighted material, and the system is tuned to generate original compositions.
3
Technical Implementation
Technically, this likely involves comparing the audio (or its embeddings) to a database of popular songs (perhaps using audio fingerprinting or neural similarity search) and blocking or adjusting any output that is too close.
Content Moderation
Additionally, Udio will have to manage lyric content (to avoid hateful or explicit output if the AI generates lyrics on its own). Being a closed platform, they can enforce filters on user-provided lyrics as well.
Udio's Voice Cloning Concerns
Potential Misuse
Udio, like Suno, is also aware of the potential for misuse in voice cloning or deepfake vocals. Although the primary aim is music, any system that generates vocals could be misused to impersonate artists.
Udio hasn't detailed their approach to this, but they are likely working with industry advisors to navigate these issues.
Industry Collaboration
As AI music generation becomes more mainstream, companies like Udio must balance creative capabilities with ethical considerations around artist impersonation.
This is particularly important given the legal scrutiny these platforms are already facing regarding copyright issues.
Udio's Audio Quality
32kHz
Possible Sample Rate
Higher than Bark's 24kHz
8+
Possible Codebooks
For detailed audio representation
40s
Generation Time
For 30 seconds of audio
The hallmark of Udio's tech, as noted by early users and reviewers, is the audio quality. The vocals are notably crisp and clear, and the instrumental is well-separated (you can distinctly hear the instruments and voice as if professionally mixed). Rolling Stone and other media quickly called Udio's output the most realistic AI music to date.
Udio's Quality Advantage
Quality Comparison
In direct comparisons, Udio's audio was sharper and more polished than Suno's output, which was itself a recent breakthrough.
This suggests Udio's model either uses a higher-fidelity audio representation (perhaps a 32 kHz or 44.1 kHz codec, whereas Bark was 24 kHz), or more codebooks (capturing more detail), or simply has been trained to better reproduce audio texture (maybe through a larger model that minimizes artifacts).
Technical Factors
  • Higher sample rate
  • More detailed codec representation
  • Larger model capacity
  • Better training on audio textures
  • Improved mixing algorithms
Udio's Current Limitations
Composition Quality
Udio's v1 is not perfect, however. The team admits there are "rough edges" and that sometimes the composition or coherence might fail.
Musical Originality
Some users noted that while the production quality is high, the musical composition (melody originality, interesting chord progressions) could be hit-or-miss, occasionally yielding banal or meandering tunes.
Style Averaging
This mirrors the challenge of training on a wide variety of music: the model can end up averaging out into a generic style unless prompted very specifically.
Prompt Dependency
Udio encourages users to guide it strongly (with detailed prompts and lyrics) to get the best results.
Udio's Length Limitations and Extensions
1
Initial Length
The initial default length is around 30–45 seconds for a prompt, which is a limitation for those wanting full-length songs.
2
Extension Feature
However, Udio's interface allows extending the song length easily, likely by prompting the model to continue from where it left off.
3
Future Improvements
The team is explicitly working on "longer samples" support in upcoming versions, aiming for multi-minute outputs without needing manual extension.
4
Additional Enhancements
They are also improving the model's multilingual abilities and adding "next-generation controllability".
Udio's Future Controllability
Mix Control
The latter could imply future features to let users control aspects like the mix, specific instruments on/off, or to edit lyrics after the fact.
Current Output
Currently, Udio outputs a single mixed track to the user; providing stems (separate vocal/instrument tracks) is not publicly advertised, but it's a possible future feature given the model likely has them internally.
Potential Features
Future versions might allow more granular control over individual elements of the generated music, enhancing the creative possibilities.
These enhancements would further differentiate Udio in the competitive AI music generation space, giving users more creative control while maintaining the high audio quality that has become its hallmark.
Udio's Technology Stack and Integrations
Python/PyTorch
Although Udio's backend is closed, it's safe to assume it also runs on a Python/PyTorch (or JAX/TF) stack, given the AI research background of the team.
Google Technologies
Many ex-Google startups initially prototype in JAX/Flax (since they know it from research) but eventually might convert to PyTorch for broader developer support.
Cloud Infrastructure
Udio might also utilize Google Cloud TPUs or GPUs for inference.
Web Interface
The front-end is a typical web interface (with even a Chrome extension available for quick access).
Udio's Development Approach
Rapid Iteration
Udio's quick iteration cycle (they rolled out improvements within weeks of launch) suggests a modern MLOps setup where they can train new model versions and deploy to production rapidly.
Model as Service
They are effectively treating the model as a service – unlike Suno's partially open model, Udio's model weights are a closely guarded asset.
Potential API
There is no public API (beyond their web app) yet, but being a platform, they might introduce one for developers if there's demand (with proper licensing).
To date, no leaks of Udio's code or model have surfaced. The only "leaks" were some audio snippets that circulated before official launch, demonstrating its capabilities.
Udio's Academic Recognition
The academic community recognizes Udio as a state-of-the-art example of proprietary text-to-song systems, in the same vein as Suno. A recent research paper on music generation noted that "industry-developed systems such as … Suno, Udio… have demonstrated promising results in song-level audio generation, though their technical details remain undisclosed."
This underscores that Udio is at the cutting edge, but from a technical standpoint we have more hypotheses than hard facts about its architecture.
Summary of Udio's Technology

Cutting-Edge Quality
State-of-the-art audio generation
Transformer Architecture
Likely using advanced transformer models
Rich Text Conditioning
Processes detailed prompts with style and lyrics
Content Safeguards
Implements copyright and content filters
Efficient Generation
Produces high-quality audio in ~40 seconds
Udio's Strengths and Weaknesses
Strengths
  • High audio quality ("studio quality" output)
  • Ease of use and intuitive interface
  • Artist style reference capability
  • Convincing vocals that sing provided lyrics
  • Quick turnaround of results
  • Built-in safeguards (copyright detection filters)
Weaknesses
  • Sometimes produces dull or nonsensical compositions if not guided
  • Limited in length (needs improvement for full songs)
  • Closed service, users depend on Udio's platform
  • No way to self-host or modify the model
  • Less community development compared to open alternatives
Nonetheless, Udio is rapidly evolving, with the team actively expanding its capabilities in subsequent versions.
Core Architecture Comparison: Design Philosophy
Suno: Open and Modular
Suno's approach is partly open and modular – they provided the community with Bark (text-to-audio) as a foundation model and built their full song system by combining specialized components (one model for vocals, one for music).
Udio: End-to-End Proprietary
Udio's approach is end-to-end but proprietary – a single integrated service where all components are behind closed doors, optimized for a seamless user experience.
Development Approach
Udio's team being from Big Tech R&D arguably took a more monolithic model approach (one big model generating everything) with heavy emphasis on quality, whereas Suno's startup built on a compositional approach (gluing together open pieces and gradually improving them).
Programming Languages and Frameworks Comparison
Suno
  • Implemented in Python using PyTorch
  • Designed to be lightweight and hackable by developers
  • Available via HuggingFace Transformers
  • Transparent about using PyTorch/NanoGPT code
  • Can run on commodity hardware
Udio
  • Exact stack isn't public
  • Likely Python-based with PyTorch or JAX
  • Probably uses GPU acceleration and CUDA
  • May include C++ or ONNX runtime optimizations
  • Likely requires more optimized serving infrastructure
Model Architecture Comparison
1
Transformer Backbone
Both platforms ultimately leverage Transformer neural networks as the backbone for sequence generation. Suno's Bark (and Chirp) use autoregressive Transformers operating on discrete token sequences (text tokens or audio tokens). Udio very likely uses the same class of model – an autoregressive transformer decoder that produces audio tokens conditioned on text.
2
Modularity Differences
A key difference is in modularity: Suno explicitly breaks the task into three stages (semantic, coarse, fine) for voice (and similarly a separate path for music), which makes each sub-model smaller (80M) and specialized. Udio might be using a single large model to generate all required tokens (maybe in a single stage), or a two-stage hierarchical model (coarse and fine).
3
Model Size
If we consider numbers: Bark's total parameters ~240M; Udio's could easily be several times that (possibly in the billions). This would allow it to "carry" more context and detail per step, hence crisper audio.
4
Output Quality
The effect is noticeable in output: Udio's audio has fewer artifacts (often artifacts occur in Bark when the fine stage can't perfectly guess the high-frequency details – Udio's model might simply generate them more accurately).
Training Data and Techniques Comparison
Large Training Corpora
Both systems required large training corpora of audio with corresponding text. Neither has published a detailed account of their dataset (and for proprietary reasons likely won't).
Suno's Approach
Suno's Bark was trained on "audio of real people from broad contexts" (which could include podcasts, audiobooks, perhaps singing data). To make it sing, they likely included a lot of audio that contains singing and lyrics – possibly public domain music or karaoke versions plus lyrics alignment.
Udio's Approach
Udio, being a later entrant, might have scraped more directly from popular music (as alleged by RIAA), meaning their model could have seen and learned from many famous songs and their lyrics.
Overfitting Prevention
Both Suno and Udio face the risk of overfitting on training songs – hence both implemented filters to avoid copying.
Training Methodology Comparison
Suno's Innovation
Suno's approach to training was also innovative in that they did unsupervised pre-training (audio-only) then fine-tuned to text, effectively leveraging self-supervised learning to compensate for limited paired data.
Udio's Approach
Udio's team has not stated if they did something similar; given the complexity, it's likely they also did some form of staged training (perhaps first train model to generate audio from a bag-of-words or tags, then condition it on detailed text).
Research Leverage
Udio's quick jump to quality might indicate they benefited from prior Google research like MusicLM which had already experimented with large-scale text-music training.
Quality vs. Coherence
A comparison by an independent tester noted that Udio produces clearer audio, but Suno's musical compositions could sometimes be more interesting or coherent in structure.
Inputs and Prompting Comparison
Suno
Suno's input is typically just the lyrics or script that you want sung/spoken. In their Discord bot, you'd supply lyrics (optionally with a brief description of style or a voice preset). If you gave only a concept, the bot would first generate lyrics via ChatGPT.
Thus, Suno's system in practice often treated the lyric text as the primary input to Bark/Chirp. The musical style was inferred either from the lyrics or from minimal cues (like adding "[chorus]" or notes emoji).
Udio
Udio's input is more multi-faceted: it explicitly asks for genre/style descriptions, a subject or lyrics, and reference artists. This makes Udio's prompting more similar to natural language descriptions (like "a classical piano ballad with lyrics about winter") combined with actual lyrics text if available.
This gives users more control in Udio: they can specify an artist's style (and Udio, having been trained on many artists, will mimic nuances of that style). Suno didn't openly advertise style mimicry by artist name, although users could attempt it; it was less direct.
Input Processing Comparison
1
1
Text Tokenization
Suno's Bark uses a simple text tokenizer (10k vocab) for input text
2
2
Lyric Integration
Both systems can accept user-provided lyrics
Lyric Generation
Different approaches when lyrics aren't provided
Style Control
Udio offers more explicit style controls
Udio likely uses a similar text tokenizer (possibly WordPiece or SentencePiece model) for prompts. Any special structure (like separating genre vs lyrics) might be handled by a convention (like a token or separator in the prompt).
Outputs: Audio Quality Comparison
Quality Difference
Audio fidelity is one of the biggest differentiators noted. Udio currently has the edge in output quality – users and reviewers consistently mention that Udio's songs sound more "professional" or "real" than Suno's.
Vocal Clarity
The vocals from Udio are clearer (less of the slight robotic undertone that Bark might produce at times), and the instruments are better defined.
Instrument Separation
For example, Udio can produce a rock song where the guitar, bass, and drums sound distinct, whereas Suno's output might blur these together or have a more lo-fi mixing.
Technical Factors in Audio Quality
High-Frequency Detail
Suno's Bark was already a breakthrough, but being a smaller model, it sometimes struggles with high-frequency detail (cymbals, "sss" sounds in vocals) leading to subtle artifacts. Udio's model, possibly due to more parameters or training, minimizes that.
Sample Rate
We can also consider sample rate: Bark uses 24 kHz audio; if Udio uses 44.1 kHz, that alone could improve brightness of the music (though doubling sample rate also dramatically increases tokens to generate).
Audio Mastering
Udio explicitly says the track is mastered, so it likely normalizes the volume and adds any needed post-processing. Suno's output might be quieter or less compressed by default (relying on the user to adjust if needed).
Perceived Quality
These differences in audio post-processing can affect perceived quality as well.
Output Length Comparison
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Suno
At the time of comparison, Suno could sometimes generate up to ~2 minutes but often produced 1 minute or less unless you engineered prompts for multiple verses.
2
Udio
Udio's default was ~30 seconds but with easy extension; users have managed to extend Udio songs to a couple of minutes by using the vary/extend features repeatedly.
3
Current Limitations
Neither platform could do a full 3-4 minute structured song in one go initially.
4
Future Development
Both teams are working on extending that: Udio explicitly in v1->v2 updates, Suno presumably in Bark v3+ or by stitching outputs.
Lyrical Accuracy Comparison
Suno's Lyric Handling
Suno's Bark, when given exact lyrics, will follow them fairly well, though occasionally it might drop or slur a word if the word is uncommon or difficult.
One of Suno's achievements is multilingual clarity – it can sing in say Spanish or Chinese with reasonable pronunciation (likely gleaned from audio data).
Udio's Lyric Handling
Udio, given lyrics, also follows them, but if it's generating lyrics itself, the intelligibility can drop. For example, some Udio-generated songs without user lyrics have vocals that sound like singing but one can't quite make out meaningful words – they might be AI-invented words or highly generic phrases.
Udio claims multi-language too, but it might not have been as thoroughly proven in v1 (English was the main showcase).
Both systems, when provided clear lyrics text, can output very intelligible singing.
Open-Source Components and Research Lineage
Suno's Open Approach
Suno clearly leveraged and contributed to open source. Bark itself is on GitHub and based on existing ideas like EnCodec (by Meta) and GPT-style transformers.
Research Contributions
Suno did not work in isolation – they benefited from Meta's and Google's research (citing those internally) and gave back by open-sourcing Bark.
Udio's Closed Approach
Udio, by contrast, kept their implementation private, but the research lineage is evident: Udio can be seen as a direct successor to Google's MusicLM and related academia.
Google Background
The founders' involvement in Imagen (text-to-image) suggests familiarity with diffusion models, but they chose an approach more akin to MusicLM (text-to-audio) for Udio.
Related Research Models
OpenAI's Jukebox
Jukebox (2020) could produce songs with vocals but was a huge model and slow.
Meta's MusicGen
MusicGen (2023) was efficient but limited to short instrumental clips.
Suno's Contribution
Suno's team has given interviews (like the Latent Space podcast) discussing their methods.
Udio's Approach
Udio's team has been quieter publicly (likely intentionally, due to competitive and legal sensitivities).
Suno and Udio leapfrogged these earlier models, with Udio especially pushing the envelope.
Scalability and Real-Time Considerations
2-3s
Suno Processing Time
Per second of audio on GPU
40s
Udio Generation Time
For ~30s of audio
1200
Udio Monthly Limit
Songs per user during beta
Neither system currently runs in real-time on a CPU or mobile device – they require significant compute. The scalability of serving these models is challenging: Udio encountered server overload due to many user requests, showing that even if one sample is 40s, handling hundreds of concurrent users can strain resources.
Unique Features: Suno
Non-Musical Audio
The ability to generate non-musical audio (e.g. pure speech, or sound effects) is a side-benefit of Bark's training. Suno could be used to make a spoken podcast or a talking AI character, not just singing.
Community Sharing
Suno allowed community sharing of generated songs on their platform (suno.com has a feed of user-generated songs). This social aspect means Suno had to build a front-end to browse and listen to AI songs.
Third-Party Integration
Suno's Bark being open has been integrated by third-party developers into other applications (for instance, some have used Bark for voiceovers in games or combined it with visual generators).
These features demonstrate Suno's commitment to community engagement and the versatility of their open-source approach.
Unique Features: Udio
Artist Style Transfer
One notable feature is the "inspiring artists" style transfer. You can prompt Udio with "in the style of [Artist/Band]" and it will attempt to emulate that style (not the exact melody, but the genre and vocal stylistics).
Vary/Extend Feature
Udio's vary/extend feature is a form of iterative refinement tool that Suno's platform didn't explicitly have. A Suno user who wanted a longer song or slight variation would have to manually edit the prompt or run it again; Udio provides a button to do that behind the scenes.
Iterative Creativity
This kind of feature shows Udio's focus on real-time creativity and iterative use, almost treating the AI like a collaborator that you can ask "give me more of that" or "change it up a bit".
Voice Cloning and Copyright Concerns
Suno's Approach
Suno's Bark intentionally does not make voice cloning easy – it doesn't allow specifying a custom voice very easily (the voice library is generic) and the creators said it's "not straightforward to voice clone known people with Bark".
Both systems integrate copyright and moderation filters as mentioned. Both also have to address the "deepfake voice" issue if users try to make the AI sing in the voice of real artists.
Udio's Approach
Udio hasn't stated their stance, but since users can name an artist style, one could conceivably generate a song that sounds like a famous singer – which is legally sensitive.
Udio likely also tries to avoid directly cloning a specific vocalist's exact voice (they might not provide a feature like "use Freddie Mercury's actual voice").
The lawsuits by record labels also encompass the concern of voice model training on artists' performances. Technically, protecting against that may involve filtering artist names or having the model not replicate a singer's voice too closely.
Conclusion: Two Approaches to AI Music Generation

Different Philosophies
Suno vs Udio can be thought of as analogous to an open research project vs a closed industrial project tackling the same problem
Architectural Approaches
Suno uses smaller, modular transformers while Udio likely uses a larger integrated model
Quality Trade-offs
Udio prioritizes audio quality while Suno emphasizes openness and flexibility
Future Evolution
Both systems continue to advance rapidly, pushing the entire field forward
As these technologies mature, we may see them converge (perhaps Suno will incorporate some of Udio's advancements, or Udio might open up parts of its system). Both are being closely watched by the AI and music communities – and even by legal entities – as they pioneer how AI can create music.