[whatmodel.sh](../../README.md) / Muse Glimmer 30B # Muse Glimmer 30B Meta · released 2026-08 · 29.8B params · apache-2.0 **score 68.5** ±1.0 · #93 of 732 · from 21 benchmark results across 6 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Muse Glimmer 30B among every model on that task or test, worst to best. 
task                vs the others            rank  counts  tests
agent tasks         ▁▁▃▆█▄▅●▄▄▂▁       #80 of 313     30%      6
coding              ▁▁▁▃▅▇█▇●▄▃▁      #114 of 469     48%      5
chat & writing      ▁▁▁▃▆▄▆█▆●▇▃      #85 of 402*     11%      1
math                               not ranked yet       —      1
vision & documents  ▁▁▂▂▁▃▄▂▆●█▄       #67 of 153    8.0%      3
web search          ▁▁▁▅●█▃▄·▁·▁       #49 of 84*    2.8%      1
agent tasks · #80 of 313 · 30% of its score Beats [GPT-5](gpt-5.md), [Gemini 3 Flash (Preview)](gemini-3-flash-preview.md), [Grok 4.20 (0309 reasoning)](grok-4-20.md) here, though they rank higher overall. Trails [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
SWE-bench Pro (split unspecified)…   51.2%  ▂·▁▁▁▁▂▃●▇▂▁ beats 29/58    very hard · avg 8.0%    9.1%  sharp · at its level · small run
SWE-bench Verified (maker)           76.0%  ▁▁▁▁▁▁▂▄█●▁▁ beats 111/138  hard · avg 23%          5.9%  sharp · at its level · small run
SWE-bench Pro public set, Qwen-re…   51.2%  ▂·····▂·▂●▃▅ beats 5/12     hard · avg 21%          4.7%  at its level
OSWorld-Verified (maker)             65.9%  █▃·▂▅▂●▂▅▃▆▆ beats 13/27    hard · avg 26%          4.5%  at its level · small run
MCP-Atlas (public set) (maker)       75.5%  ▂·▁▂▂·▁▃█▆●▂ beats 31/39    hard · avg 29%          3.1%  at its level · small run
DeepSearchQA (maker)                 74.6%  ▄·▄●···▄▄··█ beats 2/6      medium · avg 42%        2.8%  at its level · small run
coding · #114 of 469 · 48% of its score Beats [Grok 4.3](grok-4-3.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
Arena WebDev (Code Arena)           1355.1  ▁▁▃▂●█▃▆▅▅▁▂ beats 28/129   avg 981                  19%  sharp
Luau Leetcode fully solved (Torpe…   68.3%  ▁▁▁▁·▁▂▃●▃█▇ beats 19/49    hard · avg 17%          9.5%  sharp · at its level
SWE-bench Pro (split unspecified)…   51.2%  ▂·▁▁▁▁▂▃●▇▂▁ beats 29/58    very hard · avg 8.0%    9.1%  sharp · at its level · small run
SWE-bench Verified (maker)           76.0%  ▁▁▁▁▁▁▂▄█●▁▁ beats 111/138  hard · avg 23%          5.9%  sharp · at its level · small run
SWE-bench Pro public set, Qwen-re…   51.2%  ▂·····▂·▂●▃▅ beats 5/12     hard · avg 21%          4.7%  at its level
chat & writing · #85 of 402 · 11% of its score Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1424.9  ▁▁▁▂▃▃▃▆▆●█▃ beats 279/385  avg 1260       11%  blunt
math · 0.0% of its score 
test               result  vs the others in its run  difficulty        counts  why
AIME 2026 (maker)   94.7%  ▁▁▁▂▂▂▆█●▂·▁ beats 39/52  medium · avg 48%       —  not counted yet: no other model in the …
vision & documents · #67 of 153 · 8.0% of its score Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5](gpt-5.md) here, though they rank higher overall. Trails [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Qwen3.6 Flash](qwen3-6-flash.md), [Qwen3.5 35B-A3B](qwen3-5-35b-a3b.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
CharXiv (reasoning questions) (ma…   78.8%  ▂▅▃▅▅▆▆▅▆●▆▂ beats 75/101  medium · avg 42%    3.5%  small run
OmniDocBench 1.5 overall (higher …   75.8%  ▂··▂●▂··▂▂█▅ beats 2/13    medium · avg 54%    2.9%  small run
ScreenSpot-Pro (maker)               75.4%  ▂▃▅▆██●▂···▂ beats 20/24   medium · avg 52%    1.7%  blunt · at its level · small run
web search · #49 of 84 · 2.8% of its score Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Qwen3.7 Max](qwen3-7-max.md), [GPT-5.2](gpt-5-2.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall. 
test                  result  vs the others in its run  difficulty        counts  why
DeepSearchQA (maker)   74.6%  ▄·▄●···▄▄··█ beats 2/6    medium · avg 42%    2.8%  at its level · small run
other tests (9) 
test                                result  vs the others in its run  difficulty           counts  why
CPTUB average (SpeakLeash, 0-5 ju…   88.3%  ▁▂▂▂▃█▃▆▅▄▂● beats 95/95  medium · avg 65%       4.0%  typical
WildClawBench (maker)                47.6%  ▂·▄▄▆▂▄█●▂▂▂ beats 15/20  very hard · avg 14%    3.8%  at its level · small run
RobloxQA v2.0 multiple choice (To…   89.9%  ▁▁▃▁●▄▃▃▆▂▂▂ beats 14/49  easy · avg 77%         3.7%  typical
IFBench (aggregation unspecified)…   77.0%  ▂▆▂▄▄▄▂▄▄▆●▇ beats 75/86  medium · avg 41%       2.5%  at its level · small run
Gaia2 (maker)                        43.3%  █·····█····● beats 2/2    hard · avg 17%         2.5%  at its level · small run
SkillsBench (with skills) (maker)    44.3%  █·········●█ beats 1/2    hard · avg 17%         2.3%  at its level · small run
WMT25 Terminology Translation (te…   90.1%  ▂·▂··▂·▄▂·●▆ beats 8/12   medium · avg 63%       1.6%  noisy
WMT24++                              77.7%  █·●·▅▅▃···▃▃ beats 3/10   easy · avg 70%         0.9%  blunt
WMT26 General MT (GEMBA-ESA, LLM …   78.2%  ▄·▄▄▄·▄█●▄█▄ beats 7/12   medium · avg 65%       0.7%  blunt
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
meta-models/Muse-Glimmer-30B model card…       12   45% ▇▇▇▇▇
LMArena leaderboard dataset                     2   30% ▇▇▇▇
Roblox LLM Leaderboard                          2   13% ▇▇
Qwen/Qwen3.8-27B model card                     1    5% ▇
SpeakLeash CPTUB                                1    4% ▇
Cohere                                          3    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 68.5
Licences: meta-models/Muse-Glimmer-30B model card ([Apache-2.0](../../licenses/speed-meta-muse-glimmer-30b-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · Qwen/Qwen3.8-27B model card ([apache-2.0](../../licenses/Qwen--Qwen3.8-27B.txt)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · Cohere ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
Not counted (1) 
source                  test       score  why
meta-models/Muse-Glim…  AIME 2026  94.7%  too few models have it yet
## How to run it ### Through an API api (Fireworks AI) · $0.64/M blended · $0.35 in / $1.5 out per 1M tokens · [fireworks.ai](https://fireworks.ai/docs/) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/fireworks-ai/models/accounts/fireworks/models/muse-glimmer-30b.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/meta/muse-glimmer-30b.toml) ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Muse Glimmer 30B (Q4_K_M, needs ~16.8 GB, from [huggingface.co/meta-models](https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/tree/70bf1b61ac09f91b24d39038091b41c582bc5d7a)) ```sh llama cli -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M ``` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Muse Glimmer 30B (Q4_K_M, needs ~16.8 GB, from [huggingface.co/meta-models](https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/tree/70bf1b61ac09f91b24d39038091b41c582bc5d7a)) ```sh lms get https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: 4bit (~19.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/Muse-Glimmer-30B-4bit -y` · 8bit (~33.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/Muse-Glimmer-30B-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Muse Glimmer 30B (Q4_K_M, needs ~18.38 GB, from [ollama.com](https://ollama.com/library/muse-glimmer/tags)) ```sh ollama run muse-glimmer:30b ``` Other versions: Q8_0 (~36.26 GB) `ollama run muse-glimmer:30b-q8_0` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Muse Glimmer 30B (4bit, needs ~19.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Muse-Glimmer-30B-4bit/tree/3e7677d7a40d348a3daba263a2b1c0aa41910710)) ```sh mlx_lm.chat --model mlx-community/Muse-Glimmer-30B-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Muse-Glimmer-30B-4bit ``` Other versions: 8bit (~33.4 GB, Mac only) `mlx_lm.chat --model mlx-community/Muse-Glimmer-30B-8bit` Model repository and documentation: [huggingface.co/meta-models](https://huggingface.co/meta-models/Muse-Glimmer-30B) ## Published speed measurements Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine. 
hardware                      precision              speed  engine and settings
NVIDIA GeForce RTX 5090       K-Quant-17GB (M…  74.9 tok/s  llama.cpp (version not stated) · …
Apple M4 Max (MacBook; GPU …  K-Quant-17GB (M…  23.7 tok/s  ExecuTorch (version not stated) ·…
Apple M5 Max (MacBook; GPU …  K-Quant-17GB (M…  26.6 tok/s  ExecuTorch (version not stated) ·…
Each speed links to its source, which has the full settings. [interactive version](https://whatmodel.sh/model/muse-glimmer-30b) · [JSON](https://whatmodel.sh/api?format=json&all=1)