Meta · Open weights
Muse Glimmer 30B
Meta's Muse Glimmer 30B is a dense, open-weight multimodal model distilled from Muse Spark for long-horizon agents and coding workflows. It supports multi-step reasoning, reliable tool use, failure recovery, image understanding, and more than 100 languages.
- Reasoning
- Tools
- Vision
- Structured output
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- minimal · low · medium · high · xhigh
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.35
- Output
- $1.50
- Cache read
- $0.04
Reference prices as published by the model provider. API example
Specifications
- Context window
- 131.1K
- Max output
- —
- Average output (7 days)
- 682 tokens
- Longer than 61% of 473 models
Side by side
Compare models
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Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
17.5
Tested at high reasoning effort
Better than 46% of 326 scored models
Indexes
Coding Index
49.0Composite of the coding evaluations
Better than 45% of 168 scored models
Agentic work
AutomationBench-AA
6.8%Workflow automation, penalized for guardrail violations
Better than 32% of 173 scored models
AutomationBench-AA tasks completed
0.8%Workflows finished end to end without a guardrail violation
Better than 31% of 173 scored models
AA-Briefcasev1.1
477 EloAgentic knowledge work, rated head to head
Better than 19% of 166 scored models
GDPval-AAv2.1
783 EloEconomically valuable occupational tasks, rated head to head
Better than 24% of 187 scored models
Document reasoning
GDP.pdf
10.0%Professional reasoning over PDF documents, all-pass rate
Better than 38% of 171 scored models
AA-LCRv1.1
83.3%Reasoning across long documents
Better than 92% of 309 scored models
Reasoning and knowledge
Humanity’s Last Exam
22.0%Expert-level questions across academic fields
Better than 50% of 323 scored models
GPQA Diamond
83.5%Graduate-level science questions
Better than 54% of 291 scored models
Coding
Terminal-Benchv4.0
0.5%Practical software engineering in a terminal
Better than 30% of 171 scored models
SciCode
44.9%Scientific computing in Python
Better than 31% of 174 scored models
Legacy evaluations
AA-LCR (unversioned)
83.3%Long-context reasoning, earlier grading
Better than 92% of 309 scored models
Scores from Artificial Analysis, updated Oct 26, 2026. Muse Glimmer 30B was tested at high reasoning effort; compare scores measured at the same effort.Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 69.2 tok/s
- Faster than 47% of 257 models
- Time to first token
- 0.9 s
- Quicker than 95% of 257 models
- Prompt cache hits
- 55.1%
- About $0.18 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta/muse-glimmer-30b",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="meta/muse-glimmer-30b",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Meta
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