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Muse Glimmer 30B

Meta AI

Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon...

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data quality60

Updated 3 h ago · first seen 11 Sept 2026

model_01M294AJ5VTF5XBGJHX7C7ADBR

Overview

Identity

Canonical model
Yesidentity confidence: mediumOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
Official checkpoints
official_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
Artifacts
None recordedSeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
Provider deployments
3
API aliases
fireworks/muse-glimmer-30bmeta/muse-glimmer-30bmuse-glimmerIdentifiers under which providers and evaluators refer to this model.
Folded evaluation variants
0Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.

Openness

Open weights— weights downloadable; 7 dimensions unknown.

Weights downloadable under a permissive or Creative Commons licence allowing commercial use; code or data may be missing.

  • Weights

    Yes

  • Inference code

    —

  • Training code

    —

  • Training data

    —

  • Dataset

    —

  • Commercial use

    —

  • Redistribution

    —

  • Derivatives

    —

dimensions marked null are unknown, not false

Key facts

Release date

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Openrouter id

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Architecture

Hugging Face repo

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Capabilities

Modalities

Modalities
imagetext
Input
imagetext
Output
text

Capabilities

  • Tool calling

    Yes

    OpenRouter public model & pricing listing · T2

  • Structured output

    Yes

    OpenRouter public model & pricing listing · T2

  • Reasoning

    Yes

    OpenRouter public model & pricing listing · T2

  • Vision

    Yes

    OpenRouter public model & pricing listing · T2

  • Audio

    Unavailable

  • Fine-tuning available

    Unavailable

Context window

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Max output

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Benchmarks14

Compare with another model →

Comparable same task and conditions · Partially comparable same task, conditions differ (effort, temperature, judge) · Not comparable different variant or metric

Benchmark results grouped by comparability group
Benchmark · groupBest scoreTrustConfigurationResultsvs leaderEvaluatedSource
Terminal-Benchagentic · accuracy · variant=v2.1 · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisvariantv2.1reasoning_efforthighconditions differ across rows → partially comparable2non-primary groupobs. 12 Sept 2026artificialanalysis.aiT2
Terminal-Benchagentic · accuracy · variant=v4.0 · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisvariantv4.0reasoning_efforthighconditions differ across rows → partially comparable2non-primary groupobs. 12 Sept 2026artificialanalysis.aiT2
SciCodecoding · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoning_efforthighconditions differ across rows → partially comparable2−18.2 ptvs Claude Fable 5.1obs. 12 Sept 2026artificialanalysis.aiT2
Artificial Analysis Intelligence Indexcomposite · indexIndependentreasoning_efforthighversion4.3conditions differ across rows → partially comparable2−35.3vs Claude Fable 5.1obs. 12 Sept 2026artificialanalysis.aiT2
Humanity's Last Examknowledge · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoning_efforthighconditions differ across rows → partially comparable2−37.2 ptvs Claude Fable 5.1obs. 12 Sept 2026artificialanalysis.aiT2
MMMU-Promultimodal · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoning_efforthighconditions differ across rows → partially comparable2−12.5 ptvs gpt-6-astraobs. 12 Sept 2026artificialanalysis.aiT2
GPQA Diamondreasoning · accuracy · variant=Diamond · evaluator=Artificial AnalysisIndependentevaluatorArtificial AnalysisvariantDiamondreasoning_efforthighconditions differ across rows → partially comparable1non-primary groupobs. 12 Sept 2026artificialanalysis.aiT2
GPQA Diamondreasoning · accuracy · variant=GPQA Diamond · evaluator=Artificial AnalysisIndependentevaluatorArtificial AnalysisvariantGPQA Diamond1−12.7 ptvs gpt-6-astraobs. 11 Sept 2026artificialanalysis.aiT2

Current rows only, grouped by benchmark → canonical metric → comparability group (task configuration). Effort variants folded into this model appear as rows of the same group. 14 current rows in total. “vs leader” compares with the current leader of the benchmark's primary group only; other groups are not directly comparable. Comparability rules →

Providers & Pricing4

All offers in the price terminal →
Provider deployments, cheapest output first
ProviderContextInput / 1MCached inOutput / 1MBatch in / outStatusObservedSource
OpenRoutercheapest outputmeta/muse-glimmer-30b:batch131.1Kout 118K$0.02—active20 min agosince 11 Sept 2026openrouter.aiT2
OpenRoutermeta/muse-glimmer-30b131.1Kout 118K$0.04—active20 min agosince 11 Sept 2026openrouter.aiT2
Fireworks AIfireworks/muse-glimmer-30b—$0.04—active5 h agosince 11 Sept 2026app.fireworks.aiT1
Together AImuse-glimmer—$0.04$0.35 / $1.5active11 h agosince 11 Sept 2026together.aiT1

USD per 1M tokens as published by each provider; native units (per-request fees, flex/priority tiers) are kept verbatim. Rows are append-only — every price change is kept in the history below. Cost of a workload →

Price history

Step lines per provider; amber markers are recorded changes. Click a marker or a row for the evidence behind that price.

Output price · USD / 1M tokens 3 providers

Output price history of Muse Glimmer 30B$0$0.50$1$1.5$2Sept 26Sept 26Sept 26Sept 26Fireworks AI: first observed → $1.5 · 11 Sept 2026OpenRouter: first observed → $1.1 · 11 Sept 2026OpenRouter: $1.1 → $0.75 · 11 Sept 2026Together AI: first observed → $1.5 · 11 Sept 2026
  • Fireworks AI
  • OpenRouter
  • Together AI
  • Together AIfirst observed $1.511 Sept 2026
  • OpenRouter$1.1 → $0.7511 Sept 2026
  • OpenRouterfirst observed $1.111 Sept 2026
  • Fireworks AIfirst observed $1.511 Sept 2026

Input price · USD / 1M tokens 3 providers

Input price history of Muse Glimmer 30B$0$0.10$0.20$0.30$0.40$0.50Sept 26Sept 26Sept 26Sept 26Fireworks AI: first observed → $0.35 · 11 Sept 2026OpenRouter: first observed → $0.30 · 11 Sept 2026OpenRouter: $0.30 → $0.175 · 11 Sept 2026Together AI: first observed → $0.35 · 11 Sept 2026
  • Fireworks AI
  • OpenRouter
  • Together AI
  • Together AIfirst observed $0.3511 Sept 2026
  • OpenRouter$0.30 → $0.17511 Sept 2026
  • OpenRouterfirst observed $0.3011 Sept 2026
  • Fireworks AIfirst observed $0.3511 Sept 2026

Versions & Artifacts0

Version history

Context windowfirst observation only

11 Sept 2026current

Max outputfirst observation only

11 Sept 2026current

Opennessfirst observation only

11 Sept 2026current

Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.

Artifacts 0

No artifact (checkpoint, quantisation, conversion or packaging) points to this model yet.

Timeline7

Full timeline →

Change history

Temporal, append-only claims: a new observation closes the previous claim instead of overwriting it. Rewind the record with the as-of picker.
1 claims · 1 propertiesShow all properties

Weights availableweights_available1

Claim history for Weights available
ValueValid from → toStatusSourceConfidenceExtractor
Yes → currentcurrentAI Atlas curated registry (YAML, versioned in git, every entry carries its source URL)T2highderived

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Provenance

Attributed facts

20

Source tiers

T220

Freshest observation

3 h ago

Conflicts

None

Source documents 4

Source documents
SourceDocumentTypeTierLast observedSnapshots
Together AI — pricingtogether.ai/pricing pricingT1· Official1 h ago1
Fireworks AI — pricingdocs.fireworks.ai/serverless/pricing.md pricingT1· Official2 h ago2
OpenRouter public model & pricing listingopenrouter.ai/api/v1/models catalogueT2· Quality secondary20 min ago10
Artificial Analysisartificialanalysis.ai/leaderboards/models leaderboardT2· Quality secondary3 h ago2

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.

Data quality (60/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →