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Meta · Frontier

Muse Spark Open

Added Oct 15, 2026

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Use in the app

Built with Muse Spark Open. Meta's open-weight 816B-A42B model for coding and agentic workflows. Text and image input, text output, tool calling and structured output. Always reasons at low, medium, high, or xhigh effort. Custom Muse Model License and Usage Policy apply.

  • Reasoning
  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Reasoning effort
low · medium · high · xhigh
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$1.25
Output
$4.25
Cache read
$0.15

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
—
Average output (7 days)
1.3K tokens
Longer than 80% of 473 models

Side by side

Muse Spark Open compared

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

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Muse Spark OpenMetaThis modelMuse Spark 1.2MetaRemove
ProviderMetaMeta
Discovery tierFrontierFrontier
AddedOct 15, 2026Aug 5, 2026
Limits
Context window1.05MBest1M
Max output—65.54K
Average output1.3K tokens172 tokens
Pricing, USD per 1M tokens
Input$1.25$1.25
Output$4.25$4.25
Cache read$0.15$0.15
Benchmark indexes
Intelligence Index38.939.6Best
Coding Index—72.2
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Parallel toolsNoYes
Reasoning effortlow, medium, high, xhighminimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Muse Spark Open
  • Muse Spark 1.2

Intelligence Index

  • Muse Spark Open38.9
  • Muse Spark 1.239.6

Muse Spark Open tested at xhigh reasoning effort

Muse Spark Open: Better than 83% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Muse Spark Open: —
    • Muse Spark 1.2: 72.2

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Muse Spark Open: 49.7%Top
    • Muse Spark 1.2: 40.6%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Muse Spark Open: 20.9%Top
    • Muse Spark 1.2: 15.7%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Muse Spark Open: 1,279 Elo
    • Muse Spark 1.2: 1,327 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Muse Spark Open: 1,401 Elo
    • Muse Spark 1.2: 1,475 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Muse Spark Open: 18.6%Top
    • Muse Spark 1.2: 17.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Muse Spark Open: 78.3%
    • Muse Spark 1.2: 79.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Muse Spark Open: 7.8%Top
    • Muse Spark 1.2: 7.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Muse Spark Open: 43.9%
    • Muse Spark 1.2: 45.5%Top

  • GPQA Diamond

    Graduate-level science questions

    • Muse Spark Open: 89.0%
    • Muse Spark 1.2: 90.4%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Muse Spark Open: 8.1%Top
    • Muse Spark 1.2: 7.1%

  • SciCode

    Scientific computing in Python

    • Muse Spark Open: 58.4%Top
    • Muse Spark 1.2: 57.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Muse Spark Open: 78.3%
    • Muse Spark 1.2: 79.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Muse Spark Open was tested at xhigh 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.

Prompt cache hits
0.0%
About $1.25 per 1M input tokens with caching

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.

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-spark-open",
    "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-spark-open",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.