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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

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TryDeepSeek V4.1 Flash ThinkingGPT 6 SolGemini 3.8 FlashAll 3

Public evaluations

Benchmarks

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

Intelligence Index

39.6

Tested at xhigh reasoning effort

Better than 84% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    72.2

    Composite of the coding evaluations

    Better than 82% of 168 scored models

Agentic work

  • AutomationBench-AA

    40.6%

    Workflow automation, penalized for guardrail violations

    Better than 57% of 173 scored models

  • AutomationBench-AA tasks completed

    15.7%

    Workflows finished end to end without a guardrail violation

    Better than 61% of 173 scored models

  • AA-Briefcasev1.1

    1,327 Elo

    Agentic knowledge work, rated head to head

    Better than 69% of 166 scored models

  • GDPval-AAv2.1

    1,475 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 77% of 187 scored models

Document reasoning

  • GDP.pdf

    17.4%

    Professional reasoning over PDF documents, all-pass rate

    Better than 68% of 171 scored models

  • AA-LCRv1.1

    79.0%

    Reasoning across long documents

    Better than 72% of 309 scored models

  • MLCR-AA

    7.2%

    Long-context reasoning over medical records

    Better than 26% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    45.5%

    Expert-level questions across academic fields

    Better than 87% of 323 scored models

  • GPQA Diamond

    90.4%

    Graduate-level science questions

    Better than 79% of 291 scored models

Coding

  • Terminal-Benchv4.0

    7.1%

    Practical software engineering in a terminal

    Better than 58% of 171 scored models

  • SciCode

    57.4%

    Scientific computing in Python

    Better than 82% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    79.0%

    Long-context reasoning, earlier grading

    Better than 72% of 309 scored models

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
242.1 tok/s
Faster than 89% of 257 models
Time to first token
1.3 s
Quicker than 89% of 257 models
Prompt cache hits
30.2%
About $0.0704 per 1M input tokens with caching
Request success (24h)
99.6%
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.