Muse Glimmer 30B — Benchmarks

Benchmark scores for Muse Glimmer 30B aggregated from public leaderboards (4 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.

No llmrun Composite Score for Muse Glimmer 30B: the score is only published for a model measured on at least 4 of the boards in the score panel, spanning at least 2 benchmark categories and including a reasoning or coding board — or on 3 boards that narrow its score to a 90% range of at most 15.6 points. Either way the results must come from at least two different benchmarks. Its individual scores below stand on their own — see how the llmrun Composite Score gets calculated.

Muse Glimmer 30B reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

CritPt
CritPt (Critical Points) tests graduate-level physics problem-solving on research-style questions spanning mechanics, electromagnetism, quantum mechanics, and more. It was created by physicists to probe whether a model can reason through multi-step physics rather than pattern-match textbook answers.
2.6%
2.6%
median 0.9%open leader 26.6%
Overall best: Gemini 4 Carbon (high) 36.0%
64th percentile · #31 of 84 open · #155 of 244 all
#31 of 84
#155 of 244
Lab-reported
Epoch AI · Aug 2026

Muse Glimmer 30B coding benchmarks

Writing, editing, and fixing real code — measured by whether the resulting program actually runs and passes tests.

SciCode
SciCode asks a model to write real scientific-computing code — implementing multi-step numerical methods drawn from physics, chemistry, biology, and math research — and checks the output against reference solutions. It was built by a team of scientists and ML researchers to test coding ability on genuine research problems, not typical software tasks.
44.9%
44.9%
median 40.1%open leader 60.9%
Overall best: Claude Opus 5.5 Max 66.9%
61st percentile · #29 of 72 open · #151 of 217 all
#29 of 72
#151 of 217
Lab-reported
Epoch AI · Aug 2026

Other results (1)leaderboard-only, not part of the llmrun Score

Leaderboard-only, not part of the llmrun Score

LMArena WebDev
Code Arena (formerly WebDev Arena) is Arena's head-to-head coding leaderboard: two models each build a real web app or UI from the same prompt, and people vote blind on which result is better. The rating shown is a relative Bradley-Terry score, not a percentage — compare it only to other models on this board.
1355.1
1355.1
median 1415.2open leader 1665.0
Overall best: Claude Opus 5.5 (Max) 1809.8
26th percentile · #41 of 55 open · #117 of 146 all
#41 of 55
#117 of 146
Arena
Arena · Oct 2026

Muse Glimmer 30B instruction following benchmarks

How precisely a model obeys explicit, verifiable instructions — format, length, and content constraints — and, more broadly, how much people prefer its responses head-to-head in blind human voting.

LMArena Text
Arena (arena.ai, formerly LMArena / Chatbot Arena) ranks models by millions of blind, head-to-head human votes on real prompts. The Arena Score shown here is a Bradley-Terry rating computed with Arena's default style control, so higher means people preferred that model's answers more often — it's a relative rating, not a percentage, so compare it only to other models on this board.
1424.9
1424.9
median 1321.4open leader 1488.2
Overall best: Gemini 4 Carbon (High) 1527.5
84th percentile · #30 of 188 open · #122 of 408 all
#30 of 188
#122 of 408
Arena
Arena · Oct 2026

Scores aggregated from public benchmark sources (Epoch AI: 2 boards, Arena: 2 boards) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.