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Meta · Efficient

Muse Spark 1.3 Contributor

Added Sep 2, 2026

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Use in the app

Meta's Muse Spark 1.3 Contributor is a frontier multimodal reasoning model for long-horizon coding and agentic workflows, with strong gains in computer use, browsing, professional tool use, codebase understanding, and million-token retrieval. It accepts text, images, audio, video, and files, supports tool calling and structured output, and always reasons before answering. Prompts and outputs may be used by Meta for training and to improve its products.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
  • Audio input
  • Video input
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
—
Average output (7 days)
1.2K tokens
Longer than 77% of 473 models

Side by side

Muse Spark 1.3 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 1.3 ContributorMetaThis modelMuse Spark 1.2MetaRemove
ProviderMetaMeta
Discovery tierEfficientFrontier
AddedSep 2, 2026Aug 5, 2026
Limits
Context window1.05MBest1M
Max output—65.54K
Average output1.2K tokens172 tokens
Pricing, USD per 1M tokens
Input$0.10Best$1.25
Output$0.20Best$4.25
Cache read$0.002Best$0.15
Performance
Throughput193.8 tok/s—
Time to first token3.6 s—
Request success (24h)99.4%—
Benchmark indexes
Intelligence Index—39.6
Coding Index—72.2
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesYes
Audio inputYesYes
Video inputYesYes
Parallel toolsNoYes
Reasoning effortminimal, low, 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 1.3 Contributor
  • Muse Spark 1.2

Intelligence Index

  • Muse Spark 1.3 Contributor—
  • Muse Spark 1.239.6
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 72.2

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 40.6%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 15.7%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 1,327 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 1,475 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 17.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 79.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 7.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 45.5%

  • GPQA Diamond

    Graduate-level science questions

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 90.4%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 7.1%

  • SciCode

    Scientific computing in Python

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 57.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Muse Spark 1.3 Contributor: —
    • Muse Spark 1.2: 79.0%

Scores from Artificial Analysis, updated Oct 26, 2026. 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
193.8 tok/s
Faster than 84% of 260 models
Time to first token
3.6 s
Quicker than 44% of 260 models
Prompt cache hits
63.7%
About $0.0376 per 1M input tokens with caching
Request success (24h)
99.4%
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 89.4%
  • 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-spark-1.3-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-1.3-contributor",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.