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Meta · Open weights

Muse Glimmer 30B

Added Aug 11, 2026Open weights

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Use in the app

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

Muse Glimmer 30B compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Muse Glimmer 30BMetaThis modelGemini 3 Flash (Preview)GoogleRemove
ProviderMetaGoogle
Discovery tierOpen weightsEfficient
AddedAug 11, 2026Dec 17, 2025
Limits
Context window131.07K1.05MBest
Max output—65.54K
Average output682 tokens592 tokens
Pricing, USD per 1M tokens
Input$0.35Best$0.50
Output$1.50Best$3.00
Cache read$0.04Best$0.05
Performance
Throughput69.2 tok/s211.2 tok/sBest
Time to first token0.9 sBest1.3 s
Request success (24h)100.0%Best99.8%
Benchmark indexes
Intelligence Index17.517.9Best
Coding Index49.0—
Math Index—55.7
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
Audio inputNoYes
Video inputNoYes
Reasoning effortminimal, low, medium, high, xhighminimal, low, medium, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Muse Glimmer 30B
  • Gemini 3 Flash (Preview)

Intelligence Index

  • Muse Glimmer 30B17.5
  • Gemini 3 Flash (Preview)17.9

Muse Glimmer 30B tested at high reasoning effort

Muse Glimmer 30B: Better than 46% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Muse Glimmer 30B: 49.0
    • Gemini 3 Flash (Preview): —

  • Math Index

    Composite of the math evaluations

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 55.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Muse Glimmer 30B: 6.8%
    • Gemini 3 Flash (Preview): —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Muse Glimmer 30B: 0.8%
    • Gemini 3 Flash (Preview): —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Muse Glimmer 30B: 477 Elo
    • Gemini 3 Flash (Preview): —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Muse Glimmer 30B: 783 Elo
    • Gemini 3 Flash (Preview): —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Muse Glimmer 30B: 10.0%
    • Gemini 3 Flash (Preview): —

  • AA-LCRv1.1

    Reasoning across long documents

    • Muse Glimmer 30B: 83.3%Top
    • Gemini 3 Flash (Preview): 55.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Muse Glimmer 30B: 22.0%Top
    • Gemini 3 Flash (Preview): 15.0%

  • GPQA Diamond

    Graduate-level science questions

    • Muse Glimmer 30B: 83.5%Top
    • Gemini 3 Flash (Preview): 81.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 88.2%

  • IFBench

    Following precise output instructions

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 55.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Muse Glimmer 30B: 0.5%
    • Gemini 3 Flash (Preview): —

  • SciCode

    Scientific computing in Python

    • Muse Glimmer 30B: 44.9%
    • Gemini 3 Flash (Preview): —

  • LiveCodeBench

    Recent competitive programming problems

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 79.7%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 55.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 31.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Muse Glimmer 30B: —
    • Gemini 3 Flash (Preview): 43.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Muse Glimmer 30B: 83.3%Top
    • Gemini 3 Flash (Preview): 55.3%

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 256 models
Time to first token
0.9 s
Quicker than 96% of 256 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.

Hourly request success · last 72 hoursLowest hour 95.4%
  • 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.

Read the quickstart
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

Other Meta models, starting with the same family.