Lyria 3.5 vs Muse Spark 1.1
At a Glance
| Compare |  Lyria 3.5Google DeepMind |  Muse Spark 1.1Meta |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1,049K | 1,000K |
| Model facts checked | Oct 19, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Lyria 3.5 | Muse Spark 1.1 |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Lyria | Muse Spark |
| Model | Lyria 3.5 | Muse Spark 1.1 |
| Version | Lyria 3.5 | Muse Spark 1.1 |
| Lifecycle | active | preview |
| Released | 2026-07-29 | 2026-07-09 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Audio |
| Output modalities | Text, Audio | Text |
| Context window | 1,049K | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Unknown |
| Capabilities | generation, image-to-music, lyrics, music-generation, synthid, vocals | chat, computer-use, generation, reasoning, research, structured_outputs, tools |
Lyria 3.5 Capabilities
Muse Spark 1.1 Capabilities
Related Comparisons
All audio comparisons →| A | Pair | B | Context |
|---|---|---|---|
Lyria 3.5Google DeepMind | vs |  Phi-4-multimodal-instructMicrosoft | cross-developer peersaudio, image, text |
 MiniMax-Music3MiniMax | vs | Lyria 3.5Google DeepMind | cross-developer peersaudio |
Lyria 3.5Google DeepMind | vs |  stable-audio-3-mediumStability AI | cross-developer peersaudio |
Lyria 3.5Google DeepMind | vs |  InklingThinking Machines Lab | cross-developer peersaudio, image, text |
Primary Evidence
Sources and Freshness
Questions
Lyria 3.5 vs Muse Spark 1.1 FAQs
Is Lyria 3.5 or Muse Spark 1.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Lyria 3.5 and Muse Spark 1.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Lyria 3.5 or Muse Spark 1.1?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Lyria 3.5 or Muse Spark 1.1?+
Lyria 3.5 has the larger sourced context window. Lyria 3.5 supports 1,048,576 tokens and Muse Spark 1.1 supports 1,000,000 tokens.
Which performs better in benchmarks, Lyria 3.5 or Muse Spark 1.1?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Lyria 3.5 or Muse Spark 1.1 be self-hosted?+
Both models have the same sourced self-hosting status: unsupported. Lyria 3.5 is not marked open weight; Muse Spark 1.1 is not marked open weight.
Can Lyria 3.5 and Muse Spark 1.1 understand images?+
Lyria 3.5 is documented with image input; Muse Spark 1.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Lyria 3.5 or Muse Spark 1.1?+
Lyria 3.5 has a sourced maximum output of 65,536 tokens; Muse Spark 1.1's maximum output is not reported. A larger limit cannot be determined.
Do Lyria 3.5 and Muse Spark 1.1 support reasoning and tool use?+
Lyria 3.5: image input. Muse Spark 1.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Lyria 3.5 or Muse Spark 1.1?+
Lyria 3.5 has 2 sourced provider routes; Muse Spark 1.1 has 0, so Lyria 3.5 has broader tracked availability.
Which offers better value, Lyria 3.5 or Muse Spark 1.1?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.