Lyria 3.5 vs Phi-4-multimodal-instruct
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Lyria 3.5 | Phi-4-multimodal-instruct |
|---|---|---|
| Developer | Google Deepmind | Microsoft |
| Family | Lyria | Phi 4 Multimodal Instruct |
| Model | Lyria 3.5 | Phi-4-multimodal-instruct |
| Version | Lyria 3.5 | Phi-4-multimodal-instruct |
| Lifecycle | active | active |
| Released | 2026-07-29 | 2025-02-26 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Audio |
| Output modalities | Text, Audio | Text |
| Context window | 1,048,576 | 131,072 |
| Total parameters | Unknown | 5,574,460,384 |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Unknown |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Unknown |
| Capabilities | generation, image-to-music, lyrics, music-generation, synthid, vocals | chat, generation |
12 comparable fields · 11 material differences · Pair passes the primary-source comparison gate
Lyria 3.5 Capabilities
Phi-4-multimodal-instruct Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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Lyria 3.5Google Deepmind | vs |  stable-audio-3-mediumStability AI | cross-developer peersaudio |
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 Molmo2-8BAi2 | vs | Phi-4-multimodal-instructMicrosoft | cross-developer peersimage, text |
| vs | Phi-4-multimodal-instructMicrosoft | cross-developer peersimage, text | |
Phi-4-mini-instructMicrosoft | vs | Phi-4-multimodal-instructMicrosoft | family variantstext |
Phi-4-mini-reasoningMicrosoft | vs | Phi-4-multimodal-instructMicrosoft | family variantstext |
Phi-4-multimodal-instructMicrosoft | vs | Phi-4-reasoningMicrosoft | family variantstext |
Phi-4-multimodal-instructMicrosoft | vs | phi-4Microsoft | family variantstext |
Lyria 3.5Google Deepmind | vs | InklingThinking Machines Lab | cross-developer peersaudio |
Primary Evidence
Sources and Freshness
Questions
Lyria 3.5 vs Phi-4-multimodal-instruct FAQs
Is Lyria 3.5 or Phi-4-multimodal-instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Lyria 3.5 and Phi-4-multimodal-instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Lyria 3.5 or Phi-4-multimodal-instruct?+
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 Phi-4-multimodal-instruct?+
Lyria 3.5 has the larger sourced context window. Lyria 3.5 supports 1,048,576 and Phi-4-multimodal-instruct supports 131,072.
Which performs better in benchmarks, Lyria 3.5 or Phi-4-multimodal-instruct?+
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 Phi-4-multimodal-instruct be self-hosted?+
Phi-4-multimodal-instruct is the only model in this pair currently marked as self-hostable. Lyria 3.5 is not marked open weight; Phi-4-multimodal-instruct is open weight.
Can Lyria 3.5 and Phi-4-multimodal-instruct understand images?+
Lyria 3.5 is not documented with image input; Phi-4-multimodal-instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Lyria 3.5 or Phi-4-multimodal-instruct?+
Neither has a larger sourced maximum output. Lyria 3.5 is 65,536 and Phi-4-multimodal-instruct is —.
Do Lyria 3.5 and Phi-4-multimodal-instruct support reasoning and tool use?+
Lyria 3.5: none of these features are definitively sourced. Phi-4-multimodal-instruct: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Lyria 3.5 or Phi-4-multimodal-instruct?+
Lyria 3.5 has 2 sourced provider routes; Phi-4-multimodal-instruct has 0, so Lyria 3.5 has broader tracked availability.
Which offers better value, Lyria 3.5 or Phi-4-multimodal-instruct?+
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.