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Meta · Efficient

Muse Spark 1.2 Contributor

Added Aug 5, 2026

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Use in the app

A much cheaper opt-in version of Muse Spark 1.2 with the same multimodal coding and agentic capabilities. Prompts and outputs sent to this Contributor model may be used by Meta for training and to improve its products; use the standard Muse Spark 1.2 model if you do not want your data used for training.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
  • Audio input
  • Video input
  • Parallel tools
Input
Text, Images, Video, Audio
Output
Text
Reasoning effort
minimal · low · medium · high · xhigh
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.20
Cache read
$0.002

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
2.8K tokens
Longer than 96% of 473 models

Side by side

Muse Spark 1.2 Contributor compared

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

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Muse Spark 1.2 ContributorMetaThis modelMuse WonderMetaRemove
ProviderMetaMeta
Discovery tierEfficientFrontier
AddedAug 5, 2026Oct 12, 2026
Limits
Context window1M1.05MBest
Max output65.54K—
Average output2.8K tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.10Best$4.00
Output$0.20Best$20.00
Cache read$0.002Best$0.20
Performance
Throughput288.8 tok/sBest156.0 tok/s
Time to first token2.1 sBest3.4 s
Request success (24h)99.7%Best99.5%
Benchmark indexes
Intelligence Index39.652.4Best
Coding Index72.2—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesYes
Audio inputYesYes
Video inputYesYes
Parallel toolsYesNo
Reasoning effortminimal, low, medium, high, xhighminimal, low, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • Muse Spark 1.2 Contributor
  • Muse Wonder

Intelligence Index

  • Muse Spark 1.2 Contributor39.6
  • Muse Wonder52.4

Muse Spark 1.2 Contributor tested at xhigh reasoning effort

Muse Spark 1.2 Contributor: Better than 84% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Muse Spark 1.2 Contributor: 72.2
    • Muse Wonder: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Muse Spark 1.2 Contributor: 40.6%
    • Muse Wonder: 68.1%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Muse Spark 1.2 Contributor: 15.7%
    • Muse Wonder: 39.1%Top

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Muse Spark 1.2 Contributor: —
    • Muse Wonder: 10.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Muse Spark 1.2 Contributor: 1,327 Elo
    • Muse Wonder: 1,659 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Muse Spark 1.2 Contributor: 1,475 Elo
    • Muse Wonder: 1,770 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Muse Spark 1.2 Contributor: 17.4%
    • Muse Wonder: 30.6%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Muse Spark 1.2 Contributor: 79.0%
    • Muse Wonder: 80.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Muse Spark 1.2 Contributor: 7.2%
    • Muse Wonder: 22.2%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Muse Spark 1.2 Contributor: 45.5%
    • Muse Wonder: 50.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • Muse Spark 1.2 Contributor: 90.4%
    • Muse Wonder: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Muse Spark 1.2 Contributor: 7.1%
    • Muse Wonder: 52.0%Top

  • SciCode

    Scientific computing in Python

    • Muse Spark 1.2 Contributor: 57.4%
    • Muse Wonder: 60.3%Top

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Muse Spark 1.2 Contributor: 79.0%
    • Muse Wonder: 80.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Muse Spark 1.2 Contributor 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.

Throughput
288.8 tok/s
Faster than 91% of 260 models
Time to first token
2.1 s
Quicker than 73% of 260 models
Prompt cache hits
28.8%
About $0.0718 per 1M input tokens with caching
Request success (24h)
99.7%
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 96.2%
  • 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-spark-1.2-contributor",
    "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-1.2-contributor",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.