Lyria 3.5 vs Muse Spark 1.1

Benchmark Performance

Available Benchmarks

No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
Google Deepmind · activeLyria 3.5Verified Sep 3, 2026
Meta · previewMuse Spark 1.1Verified Sep 3, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldLyria 3.5Muse Spark 1.1
DeveloperGoogle DeepmindMeta
FamilyLyriaMuse Spark
ModelLyria 3.5Muse Spark 1.1
VersionLyria 3.5Muse Spark 1.1
Lifecycleactivepreview
Released2026-07-292026-07-09
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Video, Audio
Output modalitiesText, AudioText
Context window1,048,5761,000,000
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)Unknown
Capabilitiesgeneration, image-to-music, lyrics, music-generation, synthid, vocalschat, computer-use, generation, reasoning, research, structured_outputs, tools

13 comparable fields · 10 material differences · Pair passes the primary-source comparison gate

Lyria 3.5 Capabilities

generationimage-to-musiclyricsmusic-generationsynthidvocals
Input price
Output price
Serving providers2
Canonical IDlyria-3.5

Muse Spark 1.1 Capabilities

chatcomputer-usegenerationreasoningresearchstructured outputstools
Input price
Output price
Serving providers0
Canonical IDmeta-llama/muse-spark-1.1

Internal Comparison Graph

Related Comparisons

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APairBContext
vsfamily variantsaudio, image, text, video
vscross-developer peersaudio
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vscross-developer peersaudio, image, text, video
vscross-developer peersaudio, image, text, video
vscross-developer peersaudio, image, text, video
vscross-developer peersaudio, image, text, video
vscross-developer peersaudio, image, text
vscross-developer peersimage, text
vscross-developer peersaudio, text
vscross-developer peersaudio, text
vscross-developer peersaudio, 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 and Muse Spark 1.1 supports 1,000,000.

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 recorded 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 not 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?+

Neither has a larger sourced maximum output. Lyria 3.5 is 65,536 and Muse Spark 1.1 is —.

Do Lyria 3.5 and Muse Spark 1.1 support reasoning and tool use?+

Lyria 3.5: none of these features are definitively sourced. 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.

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