Muse Glimmer 30B vs Phi-4 Multimodal Instruct
At a Glance
| Compare | Muse Glimmer 30BMeta | Phi-4 Multimodal InstructMicrosoft |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 21, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $1.20Deepinfra ↗ · Sep 21, 2026 | Not reported |
| Context windowMaximum documented tokens | 131K | 131K |
| Model facts checked | Sep 3, 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 | Muse Glimmer 30B | Phi-4-multimodal-instruct |
|---|---|---|
| Developer | Meta | Microsoft |
| Family | Muse Glimmer | Phi 4 Multimodal Instruct |
| Model | Muse Glimmer 30B | Phi-4-multimodal-instruct |
| Version | Muse Glimmer 30B | Phi-4-multimodal-instruct |
| Lifecycle | active | active |
| Released | 2026-08-09 | 2025-02-26 |
| Knowledge cutoff | 2026-01-04 | Unknown |
| Input modalities | Text, Image | Text, Image, Audio |
| Output modalities | Text | Text |
| Context window | 131K | 131K |
| Total parameters | 29.8B | 5.6B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | mit |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation |
Muse Glimmer 30B Capabilities
Phi-4 Multimodal Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
Muse Glimmer 30B vs Phi-4 Multimodal Instruct FAQs
Is Muse Glimmer 30B or Phi-4 Multimodal Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Glimmer 30B and Phi-4 Multimodal Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Muse Glimmer 30B or Phi-4 Multimodal Instruct?+
Only Muse Glimmer 30B has a directly sourced input price: $0.30 per million tokens. Only Muse Glimmer 30B has a directly sourced output price: $1.20 per million tokens.
Which has a larger context window, Muse Glimmer 30B or Phi-4 Multimodal Instruct?+
Neither model has a larger sourced context window in this comparison. Muse Glimmer 30B is 131K and Phi-4 Multimodal Instruct is 131K.
Which performs better in benchmarks, Muse Glimmer 30B 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 Muse Glimmer 30B or Phi-4 Multimodal Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Muse Glimmer 30B is open weight; Phi-4 Multimodal Instruct is open weight.
Can Muse Glimmer 30B and Phi-4 Multimodal Instruct understand images?+
Muse Glimmer 30B is 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, Muse Glimmer 30B or Phi-4 Multimodal Instruct?+
Neither has a larger sourced maximum output. Muse Glimmer 30B is — and Phi-4 Multimodal Instruct is —.
Do Muse Glimmer 30B and Phi-4 Multimodal Instruct support reasoning and tool use?+
Muse Glimmer 30B: reasoning, tool calling, and image input. Phi-4 Multimodal Instruct: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Muse Glimmer 30B or Phi-4 Multimodal Instruct?+
Muse Glimmer 30B has 3 sourced provider routes; Phi-4 Multimodal Instruct has 0, so Muse Glimmer 30B has broader tracked availability.
Which offers better value, Muse Glimmer 30B 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.