Muse Spark 1.1 vs Muse Glimmer 30B
Benchmark Performance
Available Benchmarks
| Benchmark | Muse Spark 1.1 | Muse Glimmer 30B |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,479.03100% of row best · rating · muse-spark-1.1; 95% CI [1473.99485495, 1484.06002682]; votes 23768; rank 14 | 1,391.9194% of row best · rating · muse-glimmer; 95% CI [1382.01378668, 1401.79860600]; votes 3696; rank 148 |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Muse Spark 1.1 | Muse Glimmer 30B |
|---|---|---|
| Developer | Meta | Meta |
| Family | Muse Spark | Muse Glimmer |
| Model | Muse Spark 1.1 | Muse Glimmer 30B |
| Version | Muse Spark 1.1 | Muse Glimmer 30B |
| Lifecycle | preview | active |
| Released | 2026-07-09 | 2026-08-09 |
| Knowledge cutoff | Unknown | 2026-01-04 |
| Input modalities | Text, Image, Video, Audio | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 131K |
| Total parameters | Unknown | 29.8B |
| Active parameters | Unknown | Unknown |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard) |
| Capabilities | chat, computer-use, generation, reasoning, research, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
13 comparable fields · 10 material differences · Pair passes the primary-source comparison gate
Muse Spark 1.1 Capabilities
Muse Glimmer 30B Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Muse Spark 1.1 vs Muse Glimmer 30B FAQs
Is Muse Spark 1.1 or Muse Glimmer 30B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.1 and Muse Glimmer 30B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Muse Spark 1.1 or Muse Glimmer 30B?+
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 Spark 1.1 or Muse Glimmer 30B?+
Muse Spark 1.1 has the larger sourced context window. Muse Spark 1.1 supports 1,000K and Muse Glimmer 30B supports 131K.
Which performs better in benchmarks, Muse Spark 1.1 or Muse Glimmer 30B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Muse Spark 1.1 or Muse Glimmer 30B be self-hosted?+
Muse Glimmer 30B is the only model in this pair currently marked as self-hostable. Muse Spark 1.1 is not marked open weight; Muse Glimmer 30B is open weight.
Can Muse Spark 1.1 and Muse Glimmer 30B understand images?+
Muse Spark 1.1 is documented with image input; Muse Glimmer 30B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Muse Spark 1.1 or Muse Glimmer 30B?+
Neither has a larger sourced maximum output. Muse Spark 1.1 is — and Muse Glimmer 30B is —.
Do Muse Spark 1.1 and Muse Glimmer 30B support reasoning and tool use?+
Muse Spark 1.1: reasoning, tool calling, and image input. Muse Glimmer 30B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Muse Spark 1.1 or Muse Glimmer 30B?+
Muse Spark 1.1 has 0 sourced provider routes; Muse Glimmer 30B has 3, so Muse Glimmer 30B has broader tracked availability.
Which offers better value, Muse Spark 1.1 or Muse Glimmer 30B?+
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.