Gemini 3.1 Pro vs Lyria 3.5

Model Markets Rankings

Intelligence, Cost, and Efficiency

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RankingGemini 3.1 ProLyria 3.5
CostLower is better · Published-token output estimate#22 of 36$0.161 per LiveBench caseUnrankedNot in the 36-model eligible cohort

Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.

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 · previewGemini 3.1 ProVerified Aug 29, 2026
Google DeepMind · activeLyria 3.5Verified Sep 3, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldGemini 3.1 ProLyria 3.5
DeveloperGoogle DeepMindGoogle DeepMind
FamilyGemini 3Lyria
ModelGemini 3.1 ProLyria 3.5
VersionGemini 3.1 ProLyria 3.5
Lifecyclepreviewactive
ReleasedUnknown2026-07-29
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, Audio, DocumentText, Image
Output modalitiesTextText, Audio
Context window1,049K1,049K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)Google AI (Standard), Google Gemini (Standard)
Capabilitieschat, generation, reasoning, toolsgeneration, image-to-music, lyrics, music-generation, synthid, vocals

13 comparable fields · 7 material differences · Pair passes the primary-source comparison gate

Gemini 3.1 Pro Capabilities

chatgenerationreasoningtools
Input price$2.00
Output price$12.00
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.1-pro-preview

Lyria 3.5 Capabilities

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

Internal Comparison Graph

Related Comparisons

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Primary Evidence

Sources and Freshness

Questions

Gemini 3.1 Pro vs Lyria 3.5 FAQs

Is Gemini 3.1 Pro or Lyria 3.5 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and Lyria 3.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.1 Pro or Lyria 3.5?+

Only Gemini 3.1 Pro has a directly sourced input price: $2.00 per million tokens. Only Gemini 3.1 Pro has a directly sourced output price: $12.00 per million tokens.

Which has a larger context window, Gemini 3.1 Pro or Lyria 3.5?+

Neither model has a larger sourced context window in this comparison. Gemini 3.1 Pro is 1,049K and Lyria 3.5 is 1,049K.

Which performs better in benchmarks, Gemini 3.1 Pro or Lyria 3.5?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Gemini 3.1 Pro or Lyria 3.5 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini 3.1 Pro is not marked open weight; Lyria 3.5 is not marked open weight.

Can Gemini 3.1 Pro and Lyria 3.5 understand images?+

Gemini 3.1 Pro is documented with image input; Lyria 3.5 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.1 Pro or Lyria 3.5?+

Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and Lyria 3.5 is 66K.

Do Gemini 3.1 Pro and Lyria 3.5 support reasoning and tool use?+

Gemini 3.1 Pro: reasoning, tool calling, and image input. Lyria 3.5: image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.1 Pro or Lyria 3.5?+

Gemini 3.1 Pro has 2 sourced provider routes; Lyria 3.5 has 2, a tie.

Which offers better value, Gemini 3.1 Pro or Lyria 3.5?+

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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