Meta·#35 of 274 on the current board·cheaper than #34 GPT-5.4 Mini
Muse Glimmer 30B is a language model from Meta. It ranks #35 of 274 on ModelCap's current board, with a thinly supported Index score of 71.9, partly measured on public benchmarks and partly modeled. Its score range of 57.8–86.0 spans positions #8–#86. It is listed at $0.30 per million input tokens and $1.20 per million output tokens across 4 providers. It reads up to 131K tokens of context, and its weights are openly downloadable.
Figures as of 25 Sept 2026, 03:53 UTC · download the public dataset · methodology
Provider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.
Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.
Limited evidence. Measured on only 1 public benchmark, with limited support, so the score is less certain than a broadly measured one. What this label means
No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.
BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.
These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.
The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.
Not yet published: the base model it derives from, pinned evaluation dataset versions.
A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.
Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.
This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.
OpenRouter weekly popularity across the full model catalogue.
Independent providers versus the model's open or closed cohort, weighted by uptime.
Hugging Face 30-day downloads for open models; neutral for closed models.
Time since first public availability, on a six-month half-life.
meta/muse-glimmer-30bOpen weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.
Models within 3 places of #35 on the ModelCap Index.
Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).
The same publisher's other current models with a public Index position.
Muse Glimmer 30B holds ModelCap Index position #35 of 274 ranked language models as of 25 September 2026, with an Index score of 71.9 (interval 57.8–86.0); evidence: measured · blended. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.
Muse Glimmer 30B is listed at $0.30 per 1M input tokens and $1.20 per 1M output tokens as of 25 September 2026; the lowest output price among 4 providers is $1.10 on Phala. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $1.20 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.
Muse Glimmer 30B has a published context window of 131K tokens (131,072) and a maximum output of 16K tokens. Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.
Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is meta-models/Muse-Glimmer-30B on Hugging Face.
4 providers serve Muse Glimmer 30B through OpenRouter as of 25 September 2026: Phala, DeepInfra, Fireworks and Together. Each provider's price, context limit, quantization and measured uptime are in the providers table above.
Muse Glimmer 30B has published results on 1 public board tracked by ModelCap as of 25 September 2026: WildClawBench OpenClaw 47.6 (#16 of 34). Each board page ranks every tracked model on that benchmark. The ModelCap Index scores the results its methodology admits, with published uncertainty, and shows reference boards without scoring them.
Muse Glimmer 30B first appeared in the catalogue on 9 Aug 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.