Granite Embedding 311m Multilingual r2 vs Lyria 3.5
At a Glance
| Compare | Lyria 3.5Google DeepMind | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 33K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 23, 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 | granite-embedding-311m-multilingual-r2 | Lyria 3.5 |
|---|---|---|
| Developer | IBM | Google DeepMind |
| Family | Granite Embedding 311m Multilingual R2 | Lyria |
| Model | granite-embedding-311m-multilingual-r2 | Lyria 3.5 |
| Version | granite-embedding-311m-multilingual-r2 | Lyria 3.5 |
| Lifecycle | active | active |
| Released | 2026-04-29 | 2026-07-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Embedding | Text, Audio |
| Context window | 33K | 1,049K |
| Total parameters | 311.7M | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Hugging Face (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | embeddings | generation, image-to-music, lyrics, music-generation, synthid, vocals |
Granite Embedding 311m Multilingual r2 Capabilities
Lyria 3.5 Capabilities
Primary Evidence
Sources and Freshness
Questions
Granite Embedding 311m Multilingual r2 vs Lyria 3.5 FAQs
Is Granite Embedding 311m Multilingual r2 or Lyria 3.5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 311m Multilingual r2 and Lyria 3.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 311m Multilingual r2 or Lyria 3.5?+
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, Granite Embedding 311m Multilingual r2 or Lyria 3.5?+
Lyria 3.5 has the larger sourced context window. Granite Embedding 311m Multilingual r2 supports 33K and Lyria 3.5 supports 1,049K.
Which performs better in benchmarks, Granite Embedding 311m Multilingual r2 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 Granite Embedding 311m Multilingual r2 or Lyria 3.5 be self-hosted?+
Granite Embedding 311m Multilingual r2 is the only model in this pair currently marked as self-hostable. Granite Embedding 311m Multilingual r2 is open weight; Lyria 3.5 is not marked open weight.
Can Granite Embedding 311m Multilingual r2 and Lyria 3.5 understand images?+
Granite Embedding 311m Multilingual r2 is not 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, Granite Embedding 311m Multilingual r2 or Lyria 3.5?+
Neither has a larger sourced maximum output. Granite Embedding 311m Multilingual r2 is — and Lyria 3.5 is 66K.
Do Granite Embedding 311m Multilingual r2 and Lyria 3.5 support reasoning and tool use?+
Granite Embedding 311m Multilingual r2: none of these features are definitively sourced. Lyria 3.5: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 311m Multilingual r2 or Lyria 3.5?+
Granite Embedding 311m Multilingual r2 has 1 sourced provider route; Lyria 3.5 has 2, so Lyria 3.5 has broader tracked availability.
Which offers better value, Granite Embedding 311m Multilingual r2 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.