Muse Glimmer 30B released
Meta · Model release
Source confirmed
Data as of September 24, 2026 · How the score is built
CurrentOpen WeightReasoning
Released Aug 10, 2026131K context
Muse Glimmer 30B
Decision readingMuse Glimmer 30B scores 41.7 out of 100 and ranks #108 of 194. This profile shows 14 source-displayable benchmark rows; its strongest eligible category is Multimodal & Grounded at #42. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Aug 10, 2026 — see all recent releases
Muse Glimmer 30B will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
Share or export
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
41.7/100
field median 50.2#108 of 194 ranked models
Public
#108of 194
Verified —
Price
Self-hosted; infrastructure cost varies
input median $0.97No comparable first-party hosted token rate
Speed
Not measured
field median 91 tok/sTime to first token not measured
Context
131Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Multimodal & Grounded ranks #42. Particularly strong for screenshots, documents, charts, and grounded multimodal workflows.
Validate before choosing
14 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 14 displayable benchmark rows
Follow model changesCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #72 of 105Percentile 32ndWeight 22%4 benchmarksVerified | 27.6 | 4 benchmarks | Verified | |||
| CodingRank #69 of 135Percentile 49thWeight 20%4 benchmarksVerified | 36.3 | 4 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalRank #42 of 50Percentile 16thWeight 12%4 benchmarksVerified | 46.3 | 4 benchmarks | Verified | |||
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingRank #55 of 124Percentile 56thWeight 5%1 benchmarkVerified | 77.7 | 1 benchmark | Verified | |||
| MathRank Not rankedWeight 5%1 benchmarkVerified | 75.4 | 1 benchmark | Verified |
14 of 483 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic4/4 verified
- Coding4/4 verified
- ReasoningNot measured
- Multimodal4/4 verified
- KnowledgeNot measured
- MultilingualNot measured
- Inst. Following1/1 verified
- Math1/1 verified
Verified sourceProvisionalNot measured
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Muse Glimmer 30B category percentile values
- Agentic32nd percentile
- Coding49th percentile
- ReasoningNot eligible
- Multimodal16th percentile
- KnowledgeNot eligible
- MultilingualNot eligible
- Instruction following56th percentile
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#72/105
- Coding#69/135
- ReasoningNot ranked
- Multimodal#42/50
- KnowledgeNot ranked
- MultilingualNot ranked
- Inst. Following#55/124
- MathNot ranked
Top decileTop quartileMid-fieldNot eligible
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score51.2% | Versus best verified row Best verified: Claude Opus 5.5 · 89.9% | Gap38.7 behind | Weight26% ref. weight | Provider exact |
| SciCodeScientific Code Benchmark | Score43.6% | Versus best verified row Best verified: Sakana Fugu · 60.1% | Gap16.5 behind | Weight10% ref. weight | Provider exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score76% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap20 behind | WeightDisplay only | Provider exact |
| Terminal-Bench 2.1 | Score51.7% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap41.1 behind | WeightDisplay only | Provider exact |
Agentic4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| OSWorld-Verified | Score65.9% | Versus best verified row Best verified: Qwen3.8 Max · 86.1% | Gap20.2 behind | Weight7% ref. weight | Provider exact |
| MCP Atlas | Score75.5% | Versus best verified row Best verified: Muse Spark 1.1 · 88.1% | Gap12.6 behind | WeightDisplay only | Provider exact |
| DeepSearchQA | Score74.6% | Versus best verified row Best verified: Atria Dawn Preview · 96.0% | Gap21.4 behind | WeightDisplay only | Provider exact |
| skillsBench | Score44.3% | Versus best verified row Best verified: Qwen3.8 Max · 70.2% | Gap25.9 behind | WeightDisplay only | Provider exact |
Multimodal4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMU-ProMassive Multi-discipline Multimodal Understanding Pro | Score74% | Versus best verified row Best verified: Gemini 3.5 Flash · 83.6% | Gap9.6 behind | WeightWeighted 40% | Provider exact |
| CharXivCharXiv Reasoning | Score78.8% | Versus best verified row Best verified: Qwen3.8 Max · 93.5% | Gap14.7 behind | WeightWeighted 20% | Provider exact |
| ScreenSpot Pro | Score75.4% | Versus best verified row Best verified: GPT-6 Astra · 92.7% | Gap17.3 behind | WeightDisplay only | Provider exact |
| OmniDocBench 1.5 | Score75.8% | Versus best verified row Best verified: Qwen3.8 Max · 92.1% | Gap16.3 behind | WeightDisplay only | Provider exact |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score77% | Versus best verified row Best verified: MAI-Thinking-1 · 85% | Gap8 behind | WeightWeighted 70% | Provider exact |
Math1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME26AIME 2026 | Score94.7% | Versus best verified row Best verified: GLM-5.2 · 99.2% | Gap4.5 behind | WeightWeighted 25% | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 14 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
30b · 30B
Radar
Full release historyMuse Glimmer 30B release history
Radar confirmed these at the source. Use Muse Glimmer 30B in your work? Explore Radar to follow supported changes and choose your alerts.
Radar
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 131K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Meta publishes the full-precision and two 4-bit Muse Glimmer 30B checkpoints under Apache 2.0 on Hugging Face. The K-Quant-17GB release targets 24 GB hardware, while K-Quant-Dynamic targets 32 GB; Meta also ships a DFlash speculative-decoding drafter and a dedicated perception encoder.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Muse Glimmer 30B ranks #108 of 194 on the public leaderboard with a score of 41.73/100. It does not yet have enough sourced coverage for a verified position.
Muse Glimmer 30B is a open weight model with a 131K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Meta publishes the full-precision and two 4-bit Muse Glimmer 30B checkpoints under Apache 2.0 on Hugging Face. The K-Quant-17GB release targets 24 GB hardware, while K-Quant-Dynamic targets 32 GB; Meta also ships a DFlash speculative-decoding drafter and a dedicated perception encoder.
14 of 483 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Multimodal & Grounded at #42, while its lowest eligible position is Agentic at #72. particularly strong for screenshots, documents, charts, and grounded multimodal workflows.
Last updated September 24, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Muse Glimmer 30B perform overall in AI benchmarks?
Muse Glimmer 30B ranks #108 out of 194 models on the public BenchAlign leaderboard, with a score of 41.73/100. Its evidence status is Estimated, and this profile shows 14 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Muse Glimmer 30B good for coding and programming?
Muse Glimmer 30B ranks #69 out of 135 eligible models for coding and programming, with a public category score of 36.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Muse Glimmer 30B good for mathematics?
Muse Glimmer 30B has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Muse Glimmer 30B good for agentic tool use and computer tasks?
Muse Glimmer 30B ranks #72 out of 105 eligible models for agentic tool use and computer tasks, with a public category score of 27.6/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Muse Glimmer 30B good for multimodal and grounded tasks?
Muse Glimmer 30B ranks #42 out of 50 eligible models for multimodal and grounded tasks, with a public category score of 46.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Muse Glimmer 30B good for instruction following?
Muse Glimmer 30B ranks #55 out of 124 eligible models for instruction following, with a public category score of 77.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Muse Glimmer 30B open source?
Muse Glimmer 30B is an open-weight model from Meta. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Muse Glimmer 30B have full benchmark coverage on BenchLM?
No. Muse Glimmer 30B currently has 30 source-displayable rows across 483 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Muse Glimmer 30B?
Muse Glimmer 30B has a reported context window of 131K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.