Lyria 3.5 vs Kimi K2.7 Code

At a Glance

Compare
Lyria 3.5Google DeepMind
Kimi K2.7 CodeMoonshot AI
Intelligence, Cost, and Efficiency
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#9 of 44$0.042 per LiveBench case
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.68Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$3.21Openrouter · Sep 22, 2026
Context windowMaximum documented tokens1,049K262K
Model facts checkedSep 22, 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

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldLyria 3.5Kimi K2.7 Code
DeveloperGoogle DeepMindMoonshot AI
FamilyLyriaKimi K2 7
ModelLyria 3.5Kimi K2.7 Code
VersionLyria 3.5Kimi K2.7 Code
Lifecycleactiveactive
Released2026-07-292026-06-11
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image, Video
Output modalitiesText, AudioText
Context window1,049K262K
Total parametersUnknown1T
Active parametersUnknown32B
LicenseUnknownmodified-mit
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessGoogle AI (Standard), Google Gemini (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesgeneration, image-to-music, lyrics, music-generation, synthid, vocalsagents, chat, coding, reasoning, tools, vision

Lyria 3.5 Capabilities

generationimage-to-musiclyricsmusic-generationsynthidvocals
Serving providers2
Canonical IDlyria-3.5

Kimi K2.7 Code Capabilities

agentschatcodingreasoningtoolsvision
Serving providers4
Canonical IDmoonshotai/Kimi-K2.7-Code

Primary Evidence

Sources and Freshness

Questions

Lyria 3.5 vs Kimi K2.7 Code FAQs

Is Lyria 3.5 or Kimi K2.7 Code better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Lyria 3.5 and Kimi K2.7 Code, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Lyria 3.5 or Kimi K2.7 Code?+

Only Kimi K2.7 Code has a directly sourced input price: $0.68 per million tokens. Only Kimi K2.7 Code has a directly sourced output price: $3.21 per million tokens.

Which has a larger context window, Lyria 3.5 or Kimi K2.7 Code?+

Lyria 3.5 has the larger sourced context window. Lyria 3.5 supports 1,049K and Kimi K2.7 Code supports 262K.

Which performs better in benchmarks, Lyria 3.5 or Kimi K2.7 Code?+

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 Kimi K2.7 Code be self-hosted?+

Kimi K2.7 Code is the only model in this pair currently marked as self-hostable. Lyria 3.5 is not marked open weight; Kimi K2.7 Code is open weight.

Can Lyria 3.5 and Kimi K2.7 Code understand images?+

Lyria 3.5 is documented with image input; Kimi K2.7 Code is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Lyria 3.5 or Kimi K2.7 Code?+

Neither has a larger sourced maximum output. Lyria 3.5 is 66K and Kimi K2.7 Code is —.

Do Lyria 3.5 and Kimi K2.7 Code support reasoning and tool use?+

Lyria 3.5: image input. Kimi K2.7 Code: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Lyria 3.5 or Kimi K2.7 Code?+

Lyria 3.5 has 2 sourced provider routes; Kimi K2.7 Code has 4, so Kimi K2.7 Code has broader tracked availability.

Which offers better value, Lyria 3.5 or Kimi K2.7 Code?+

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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