Muse Glimmer 30B

Muse Glimmer 30B is a reasoning model from Meta in the Muse Glimmer family. 18 benchmarks count toward its index, in 7 categories.

  • Open weights
  • Reasoning
  • Input: text · image
  • Released 2026-08
RankedEnough results for an index.
IndexAll categories, out of 100.53.0±4.3Median 51.0
CoverageIndex weight that has results.95%
SpeedOutput tokens per second.90/sMedian 67/s
First tokenSeconds to the first output token.0.8 sMedian 1.3 s
Input / 1MUS dollars per 1M input tokens.$0.30batch $0.24Median $0.60
Output / 1MUS dollars per 1M output tokens.$1.20batch $0.96 Batch price per 1M output tokens.Median $2.55
ContextMaximum tokens in one request.131KMedian 262K
EloLMArena rating and rank.—Median 1422

Details

From OpenRouter
API IDThe model ID on OpenRouter.
meta/muse-glimmer-30b
Max outputMaximum output tokens per request.
16K
Knowledge cutoffLast date of training data.
—
ToolsTool calls on OpenRouter.
Yes
JSON outputOutput follows a given JSON schema.
Yes
Cache read / 1MUS dollars per 1M cached tokens.
$0.04
WeightsLink to the published weights.
Hugging Face (opens in a new tab) ↗
RetiresDate OpenRouter removes the model.
—

Where to run

APIs that serve this model.
2 providers
ProviderTypeMaker, cloud, host or router.InputOutputContext
VultrCloud$0.25$1.00131K
OpenRouterRouter$0.30$1.20131K

US dollars per 1M tokens, list price. From models.dev (opens in a new tab) (MIT).

Run locally

29.78B parameters
16-bit
63.5 GB
8-bit
34.2 GB
4-bit
20.0 GB
Weights
Hugging Face (opens in a new tab) ↗

Memory need with a short context. The 4-bit copy fits in 24 GB. Compare local models

Capabilities

Index per category, out of 100.
Out of 100
AgenticMulti-step tasks with tools.5 counted53.3110 / 218
CodingCode writing and repair.3 counted54.4102 / 209
ReasoningLogic problems and puzzles.2 counted53.297 / 227
MultimodalTasks with images and text.3 counted54.057 / 84
KnowledgeFacts and expert knowledge.3 counted48.7145 / 268
MultilingualTasks in many languages.None counted——
InstructionTasks with strict prompt rules.1 counted61.0—
MathMath problems.1 counted53.5—

Results

18 counted
BenchmarkThe benchmark name.ResultThe score from the source.PlacePlace among results on this benchmark.
Instruction Following BenchmarkInstruction77.0%22 / 57
Artificial Analysis GPQA DiamondKnowledge83.5%88 / 199
MCP AtlasAgentic75.5%23 / 52
Critical Physics TasksReasoning2.6%102 / 214
Scientific Code BenchmarkCoding43.6%17 / 35
ScreenSpot ProMultimodal75.4%10 / 20
Artificial Analysis MMMU-ProMultimodal74.3%58 / 113
AIME 2026Math94.7%18 / 34
Artificial Analysis SciCodeCoding44.9%73 / 114
GDPval-AA normalizedAgentic14.2%89 / 133
OSWorld-VerifiedAgentic65.9%27 / 37
DeepSearchQAAgentic74.6%18 / 24
CharXiv ReasoningMultimodal78.8%37 / 49
SWE-bench ProCoding51.2%68 / 83
OmniDocBench 1.5Multimodal75.8%—

Sources

BenchLMCC BY-NC 4.0OpenRouterTerms restrict reusemodels.devMIT

History

5 releases
Index53.8 → 53.0
23 Oct 202612 Nov 2026
Output / 1M$1.20 → $1.20
23 Oct 202612 Nov 2026

Questions

How much does Muse Glimmer 30B cost?
$0.30 per 1M input tokens, $1.20 per 1M output tokens.
How good is Muse Glimmer 30B?
Its index is 53.0 out of 100. That is place 117 of 283 ranked models.
What is Muse Glimmer 30B best at?
Reasoning: 53.2 out of 100, place 97 of 227.
How fast is Muse Glimmer 30B?
90 output tokens per second, 0.8 s to the first token.
What is the context window of Muse Glimmer 30B?
131K tokens.
Is Muse Glimmer 30B open source?
Yes. Its weights are published.
When was Muse Glimmer 30B released?
August 2026.

Badge

Muse Glimmer 30B on Inference360: place 117 of 283, index 53.0
HTML
<a href="https://inference360.ai/models/muse-glimmer-30b"><img src="https://inference360.ai/badges/muse-glimmer-30b.svg" alt="Muse Glimmer 30B on Inference360: place 117 of 283, index 53.0" height="20"></a>
Markdown
[![Muse Glimmer 30B on Inference360: place 117 of 283, index 53.0](https://inference360.ai/badges/muse-glimmer-30b.svg)](https://inference360.ai/models/muse-glimmer-30b)

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