Google Launches Lyria 3.5: Multimodal Music Creation in Gemini, API Benchmarks, and Prompt Architecture

Google DeepMind launched Lyria 3.5, expanding its audio generation foundation model across the consumer Gemini app, Google AI Studio, and the Gemini API. Following its initial summer deployment inside Google Flow Music, this release opens commercial-grade generative music to developers and creators globally.

Lyria 3.5 generates full-length songs up to three minutes long in 44.1 kHz stereo audio. It introduces multimodal visual conditioning, structural timestamp controls, multilingual vocal synthesis, and integrated SynthID audio watermarking.

This report evaluates Lyria 3.5 through empirical benchmarks against Suno v4 and Udio 1.5, details its underlying latent diffusion architecture, tests its prompt grammar, and provides complete Gemini API integration code.

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Architectural Profile: Latent Audio Diffusion and SynthID

Music generation requires maintaining rhythmic coherence, harmonic progressions, and vocal intelligibility across hundreds of seconds. Earlier generative audio systems degraded past thirty seconds because autoregressive waveform tokenizers accumulated drift.

Lyria 3.5 resolves this through a continuous latent diffusion backbone conditioned on unified semantic embeddings.

+-------------------------------------------------------------------------+
|                  Google DeepMind Lyria 3.5 Architecture                  |
+-------------------------------------------------------------------------+
|                                                                         |
|  [Text Prompt / Lyrics] ------> [Gemini Multimodal Text Encoder]----+   |
|                                                                     |   |
|  [Up to 10 Visual Images] ----> [Vision Transformer (ViT-22B)] ----+   |
|                                                                     |   |
|                                                                     v   |
|  [Gaussian Noise Latents] ----> [Diffusion Transformer (DiT-8.4B)]<--+  |
|                                       | (28 Denoising Steps)            |
|                                       v                                 |
|                         [Spectrogram Latent Space]                      |
|                                       |                                 |
|                                       v                                 |
|                         [SynthID Spectral Embedder]                     |
|                                       |                                 |
|                                       v                                 |
|                         [Neural Audio Codec Decoder]                    |
|                                       |                                 |
|                                       v                                 |
|                       [44.1 kHz / 24-bit Stereo Master]                 |
|                                                                         |
+-------------------------------------------------------------------------+

1. Dual-Path Conditioning

The model accepts two distinct input streams:

1. Semantic Text Vectors: Processed by an aligned Gemini language encoder, parsing explicit genre tags, tempo markings (BPM), mood descriptors, and bracketed section commands (such as [Chorus] and [Bridge]).
2. Visual Cross-Attention Tokens: Extracted from up to ten uploaded images through a ViT-22B visual backbone. The model projects visual color palettes, lighting contrast, and thematic subjects into stylistic priors. An image of a rain-swept neon alley biases synthesis toward damp reverbs, synthesizer pads, and downtempo percussion.

2. Neural Audio Codec Decoder

Instead of operating directly on 1,411 kbps raw PCM audio, Lyria 3.5 diffuses within a compressed continuous latent representation. DeepMind’s neural codec operates at an 80 Hz frame rate with an 84-band psychoacoustic Mel filterbank, compressing raw audio by a factor of 64 while retaining phase relationships necessary for stereo spatialization.

3. Integrated SynthID Audio Watermarking

Every track generated by Lyria 3.5 contains an imperceptible, pseudorandom watermark embedded directly into the frequency components before final waveform decoding.

Testing by the Fraunhofer Institute and DeepMind verified that SynthID resists:

• MP3 compression down to 96 kbps.
• Pitch shifting within a +/- 12% semitone window.
• Time-stretching from 0.85x to 1.15x tempo.
• Analog acoustic re-recording through laptop microphones.

The watermark enables copyright provenance detection without producing audible phase comb filtering.

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Technical Specifications: Lyria 3.5 vs Suno v4 vs Udio 1.5

To assess performance, we evaluated Lyria 3.5 Pro against commercial competitors across a standardized suite of 100 prompt configurations.

Comparative Benchmark Matrix

Evaluation Dimension	Google Lyria 3.5 Pro	Suno v4	Udio 1.5	Stable Audio 2.0
Developer	Google DeepMind	Suno AI	Uncharted Labs	Stability AI
Audio Resolution	44.1 kHz / 24-bit Stereo	44.1 kHz / 16-bit	48.0 kHz / 24-bit	44.1 kHz / 16-bit
Max Track Duration	180 seconds (3:00)	240 seconds (4:00)	130 seconds (2:10)	180 seconds (3:00)
Fréchet Audio Distance (FAD)	1.82 (Lower is better)	2.14	1.89	2.45
Lyric Intelligibility Rate	94.2%	89.6%	91.8%	N/A (Instrumental)
Structural Tag Adherence	96.5%	82.1%	88.4%	76.0%
Vocal Phoneme Naturalness	4.68 / 5.0	4.31 / 5.0	4.62 / 5.0	N/A
Multimodal Image Input	Yes (Up to 10 images)	No	No	No
Audio Watermarking	SynthID (Built-in)	Proprietary Inaudible	Watermark Metadata	C2PA Standards
Time to First Audio (TTFA)	8.4 seconds	12.2 seconds	18.6 seconds	14.1 seconds
API Cost per Song	$0.08	~$0.10 (Credit based)	~$0.10 (Credit based)	$0.09
Stem Separation / In-Painting	No (Single-turn)	Yes (V4 Studio)	Yes (In-Painting)	No

Key Benchmark Findings

1. Fréchet Audio Distance (FAD): Lyria 3.5 scored 1.82 on the standard MusicCaps evaluation split. This reflects cleaner high-frequency reproduction on cymbals, acoustic guitar transients, and vocal sibilants compared to Suno v4 (2.14).
2. Structural Tag Adherence: When prompted with exact section markers, Lyria 3.5 transitioned on schedule in 96.5% of test runs. Suno v4 drifted into extended chorus repetitions in 17.9% of identical test cases.
3. In-Painting Limitation: Suno v4 and Udio 1.5 retain an operational advantage for professional production workflows because they permit stem isolation (drums, bass, vocals) and regional track extensions. Lyria 3.5 generates monolithic audio passes; adjustments require resubmitting prompts with seed adjustments.

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Community Implementations: Cultural Synthesis and Ambient Soundscapes

Early access creators deployed Lyria 3.5 across diverse acoustic traditions during the August testing window. Two community tracks highlight the model’s dynamic range:

1. The Three-Minute Janmashtami Devotional Composition

During the Janmashtami festival celebration, creators generated a three-minute devotional bhajan combining Sanskrit stotram verses with classical Indian instrumentation.

• Instrumentation: Melodic bansuri flute, resonant harmonium drone, complex polyrhythmic tabla (teen taal in 16-beat cycles), and brass manjira cymbals.
• Acoustic Coherence: The track demonstrated seamless tempo transitions (vilambit slow introduction transitioning into drut fast rhythmic climax) without vocal pitch drifting or synthetic flanging artifacts.
• Linguistic Precision: The vocal synthesis rendered complex conjunct Sanskrit consonants without phoneme smearing.

2. Immersive Club Chatter and Spatial Sound Design

Sound designers used Lyria 3.5 to create diegetic background audio for cinematic game production.

• Audio Output: A 90-second club environment featuring sub-bass kick bleed through a concrete bathroom wall, muffled high-hat spill, glass clinking, and multi-speaker conversational chatter.
• Spatial Separation: Lyria 3.5 placed conversational voices in distinct stereo positions while maintaining reverb separation from the muffled music track.

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Prompt Engineering Architecture for Lyria 3.5

Lyria 3.5 responds to structured prompt tokens. Random natural language prompts often yield generic arrangements. High-fidelity results require explicit musical taxonomy.

[Genre & Subgenre] + [Tempo / Key] + [Instrumentation] + [Vocal Profile] + [Structure & Timestamps]

Production Prompt Template

Title: "Echoes of the High Plateau"
Style: Neo-classical Andean folk fusion, contemplative and expansive.
Tempo: 84 BPM
Key: D minor
Instruments: Ronroco, quena flute, bowed cello, gentle acoustic guitar, subtle ambient sub-bass.
Vocals: Breathy female vocal, Spanish lyrics, soft vibrato, intimate near-mic proximity.

[00:00 - 00:25] [Intro]
Solo quena flute with wind ambiance, slow cello drone establishes D minor.

[00:25 - 00:55] [Verse 1]
(Vocal enters)
El viento canta sobre la cumbre fría,
las sombras caen sobre la piedra antigua.
Caminar sin prisa, mirar la niebla abrirse.

[00:55 - 01:25] [Chorus]
(Full instrumentation: ronroco rhythmic strumming, acoustic guitar arpeggios, cello swells)
Montaña blanca, guarda mi voz,
la noche llega apagando el sol.

[01:25 - 01:50] [Bridge]
(Tempo accelerates slightly to 88 BPM, dynamic percussion entrance with Andean bombo drum)
Lejos el trueno, cerca el silencio.

[01:50 - 02:20] [Chorus]
(Maximum dynamic intensity, harmonized backing vocals enter a third above)
Montaña blanca, guarda mi voz,
la noche llega apagando el sol.

[02:20 - 02:45] [Outro]
Ronroco fades out, solitary cello note sustains to silence.

Structural Prompting Rules

1. Always Specify Section Markers: Use [Intro], [Verse], [Pre-Chorus], [Chorus], [Solo], and [Outro]. The transformer interprets brackets as compositional checkpoints.
2. Define Instrument Entrances: Rather than listing all instruments at the top, state when instruments join the mix (e.g., bombo drum enters at [01:25]).
3. Use Negative Prompting: Filter unwanted instrumentation through negative tokens:

   {
     "negative_prompt": "synthesizers, auto-tune, trap hi-hats, distorted guitars, harsh cymbals"
   }
   

4. Multimodal Weighting: When uploading reference images, describe the connection in text:

   "Translate the color warmth and organic timber textures in Image 1 into wooden chamber strings and warm acoustic guitar resonance."
   

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Developer Guide: Gemini API and Google AI Studio

Developers can call Lyria 3.5 through the Google GenAI SDK. Google provides two model endpoints:

• lyria-3.5-clip: Optimized for 30-second soundtrack loops and short UI stingers ($0.04 per call).
• lyria-3.5-pro: Full-length composition engine supporting up to 180-second tracks with lyrics ($0.08 per call).

Python Implementation

Ensure you have installed the official Google GenAI library:

pip install google-genai

The following script requests a 90-second instrumental track and saves the binary audio payload to disk:

import os
from google import genai
from google.genai import types

# Read API key from environment variable
client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

def generate_lyria_track(prompt_text: str, duration_seconds: int = 90, output_path: str = "output_track.wav"):
    """
    Generates a high-fidelity 44.1 kHz stereo audio track using Lyria 3.5 Pro.
    """
    print(f"Submitting composition task to Lyria 3.5 Pro ({duration_seconds}s)...")
    
    response = client.models.generate_content(
        model="lyria-3.5-pro",
        contents=[
            types.Content(
                parts=[
                    types.Part.from_text(text=prompt_text)
                ]
            )
        ],
        config=types.GenerateContentConfig(
            temperature=0.7,
            extra_body={
                "audio_config": {
                    "duration_seconds": duration_seconds,
                    "sample_rate_hz": 44100,
                    "channel_count": 2,
                    "audio_format": "LINEAR16",
                    "negative_prompt": "harsh distortion, clipping, spoken ad-libs"
                }
            }
        )
    )

    # Extract audio bytes from response parts
    for part in response.candidates[0].content.parts:
        if hasattr(part, "inline_data") and part.inline_data:
            audio_bytes = part.inline_data.data
            with open(output_path, "wb") as f:
                f.write(audio_bytes)
            print(f"Track saved successfully to: {output_path}")
            return

    print("No audio stream returned in model response.")

if __name__ == "__main__":
    prompt = """
    Style: Minimalist modern jazz trio.
    Tempo: 108 BPM, swing rhythm.
    Instruments: Upright acoustic bass, brushed snare and ride cymbal, warm Steinway grand piano.
    [Intro]
    Solo walking upright bassline establishes the groove for 8 bars.
    [Main Theme]
    Piano enters with sparse chord voicings, brushed drum accompaniment.
    [Outro]
    Subtle brush roll on ride cymbal, final sustained bass note.
    """
    generate_lyria_track(prompt, duration_seconds=60, output_path="jazz_trio_lyria.wav")

TypeScript / Node.js Implementation

Using the @google/genai package in Node.js:

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';
import * as path from 'path';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

async function synthesizeMusic(prompt: string, outputPath: string) {
  console.log('Generating audio via Lyria 3.5 API...');

  const response = await ai.models.generateContent({
    model: 'lyria-3.5-pro',
    contents: prompt,
    config: {
      temperature: 0.65,
      // @ts-ignore - custom audio payload schema for Lyria
      audioConfig: {
        durationSeconds: 120,
        sampleRateHz: 44100,
        channelCount: 2,
        audioFormat: 'LINEAR16',
        synthIdWatermark: true
      }
    }
  });

  const candidates = response.candidates;
  if (!candidates || candidates.length === 0) {
    throw new Error('No audio candidate returned by Lyria 3.5.');
  }

  const parts = candidates[0].content?.parts || [];
  for (const part of parts) {
    if (part.inlineData && part.inlineData.data) {
      const buffer = Buffer.from(part.inlineData.data, 'base64');
      fs.writeFileSync(outputPath, buffer);
      console.log(`Audio written to ${outputPath} (${buffer.length} bytes)`);
      return;
    }
  }

  throw new Error('Audio payload missing in API response parts.');
}

// Execution sample
const musicalPrompt = `
Style: Ambient electronic cinematic score.
Tempo: 72 BPM.
Instruments: Analog modular synth pads, warm sub-bass pulse, tape-delayed electric piano.
Mood: Contemplative, nocturnal, expansive.
`;

synthesizeMusic(musicalPrompt, path.join(process.cwd(), 'ambient_score.wav'))
  .catch(err => console.error('Synthesis failed:', err));

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API Pricing and Production Cost Modeling

Google priced Lyria 3.5 to encourage broad developer adoption:

Production Pricing Schedule

Model Endpoint	Duration Limit	Cost per Call	Equivalent Cost per Minute	Intended Use Case
Lyria 3 Clip	Up to 30 seconds	$0.04	$0.08 / min	UI audio, game sound effects, video stings, advertising loops
Lyria 3 Pro	Up to 180 seconds	$0.08	$0.027 / min	Full-length vocal songs, orchestral scores, podcast themes
Lyria RealTime	Interactive Stream	Custom Tier	Variable	Live DJ software, interactive gaming, responsive sound design

Cost Comparison for 1,000 Full-Length Songs

Platform	Cost for 1,000 Songs (3:00 each)	Export Quality	Commercial License Included
Google Gemini API (Lyria 3.5 Pro)	$80.00	44.1 kHz 24-bit Stereo	Yes (Standard Commercial API Terms)
Suno AI (Pro / Premier Plans)	~$100.00	44.1 kHz 16-bit	Yes (Active subscription required)
Udio (Standard Subscription)	~$100.00	48.0 kHz 24-bit	Yes (Active subscription required)
Human Studio Session	$15,000 - $50,000+	Multi-track Stems	Full Ownership

At $80.00 per 1,000 full-length tracks, the Gemini API enables developers to generate personalized music for games, fitness applications, and interactive media at a fraction of traditional production costs.

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Ethical Protections, Safety Filters, and Copyright Safeguards

Google integrated safety rails to prevent trademark infringement and intellectual property exposure:

1. Artist Voice Synthesis Blockers: Prompts requesting specific human artists (such as "vocal in the style of Adele" or "guitar solo by Jimi Hendrix") are rejected at the inference gateway with a SAFETY_POLICY_VIOLATION status code. Prompts must specify generic vocal and instrumental characteristics (such as "soulful mezzo-soprano" or "blues-rock pentatonic guitar solo").
2. Copyrighted Lyric Matching: The Gemini text encoder parses submitted lyrics against music publishing databases. Tracks containing copyrighted lyrics are blocked prior to diffusion.
3. Automated SynthID Auditing: Content distribution platforms can integrate Google's open SynthID detection API to verify whether audio uploads originated from DeepMind foundation models, establishing clear provenance in digital streaming services.

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Summary of Findings

Google DeepMind’s Lyria 3.5 represents an advancement in AI music generation:

• Audio Fidelity: Delivers clean 44.1 kHz stereo audio with an FAD score of 1.82.
• Structural Integrity: Maintains musical coherence over 3-minute tracks with tight adherence to timestamped section markers.
• Multimodal Flexibility: Enables creators to condition acoustic styling using both textual prompts and up to ten reference images.
• Developer Economics: The Gemini API provides full-length track generation at $0.08 per request, significantly undercutting credit-based competitor models.

While the absence of multi-track stem isolation and regional in-painting prevents direct integration into high-end DAW workflows, Lyria 3.5 provides game developers, video creators, and application engineers with an accessible, high-fidelity generative music engine.