Lyria 3.5 vs Kimi K3
At a Glance
| Compare |  Lyria 3.5Google DeepMind |  Kimi K3Moonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #17 of 4670.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #30 of 44$0.194 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #21 of 3852.5 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $2.85Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $14.25Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,049K |
| Model facts checked | Sep 22, 2026View model evidence → | Aug 28, 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 | Kimi-K3 |
|---|---|---|
| Developer | Google DeepMind | Moonshot AI |
| Family | Lyria | Kimi K3 |
| Model | Lyria 3.5 | Kimi-K3 |
| Version | Lyria 3.5 | Kimi-K3 |
| Lifecycle | active | active |
| Released | 2026-07-29 | 2026-07-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text, Audio | Text |
| Context window | 1,049K | 1,049K |
| Total parameters | Unknown | 2.8T |
| Active parameters | Unknown | 104B |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | generation, image-to-music, lyrics, music-generation, synthid, vocals | chat, generation, reasoning |
Lyria 3.5 Capabilities
Kimi K3 Capabilities
Related Comparisons
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|---|---|---|---|
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Lyria 3.5Google DeepMind | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Lyria 3.5 vs Kimi K3 FAQs
Is Lyria 3.5 or Kimi K3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Lyria 3.5 and Kimi K3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Lyria 3.5 or Kimi K3?+
Only Kimi K3 has a directly sourced input price: $2.85 per million tokens. Only Kimi K3 has a directly sourced output price: $14.25 per million tokens.
Which has a larger context window, Lyria 3.5 or Kimi K3?+
Neither model has a larger sourced context window in this comparison. Lyria 3.5 is 1,049K and Kimi K3 is 1,049K.
Which performs better in benchmarks, Lyria 3.5 or Kimi K3?+
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 K3 be self-hosted?+
Kimi K3 is the only model in this pair currently marked as self-hostable. Lyria 3.5 is not marked open weight; Kimi K3 is open weight.
Can Lyria 3.5 and Kimi K3 understand images?+
Lyria 3.5 is documented with image input; Kimi K3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Lyria 3.5 or Kimi K3?+
Neither has a larger sourced maximum output. Lyria 3.5 is 66K and Kimi K3 is —.
Do Lyria 3.5 and Kimi K3 support reasoning and tool use?+
Lyria 3.5: image input. Kimi K3: reasoning and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Lyria 3.5 or Kimi K3?+
Lyria 3.5 has 2 sourced provider routes; Kimi K3 has 5, so Kimi K3 has broader tracked availability.
Which offers better value, Lyria 3.5 or Kimi K3?+
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.