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Meta · Frontier

Muse Spark 1.3

Added Sep 2, 2026

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Use in the app

Meta's Muse Spark 1.3 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, instruction following, and million-token retrieval. It accepts text, images, audio, video, and files, supports tool calling and structured output, and always reasons before answering.

  • 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
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)
1.2K tokens
Longer than 79% of 473 models

Side by side

Muse Spark 1.3 compared

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

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Muse Spark 1.3MetaThis modelMuse Spark OpenMetaRemove
ProviderMetaMeta
Discovery tierFrontierFrontier
AddedSep 2, 2026Oct 15, 2026
Limits
Context window1.05M1.05M
Average output1.2K tokens1.3K tokens
Pricing, USD per 1M tokens
Input$1.25$1.25
Output$4.25$4.25
Cache read$0.15$0.15
Performance
Throughput292.3 tok/s—
Time to first token7.8 s—
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index48.1Best38.9
Coding Index75.8—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesNo
Audio inputYesNo
Video inputYesNo
Reasoning effortminimal, low, medium, high, xhighlow, 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
  • Muse Spark Open

Intelligence Index

  • Muse Spark 1.348.1
  • Muse Spark Open38.9

Muse Spark 1.3 tested at max reasoning effort

Muse Spark 1.3: Better than 95% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Muse Spark 1.3: 75.8
    • Muse Spark Open: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Muse Spark 1.3: 57.9%Top
    • Muse Spark Open: 49.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Muse Spark 1.3: 29.8%Top
    • Muse Spark Open: 20.9%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Muse Spark 1.3: 8.9%
    • Muse Spark Open: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Muse Spark 1.3: 1,581 EloTop
    • Muse Spark Open: 1,279 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Muse Spark 1.3: 1,681 EloTop
    • Muse Spark Open: 1,401 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Muse Spark 1.3: 26.6%Top
    • Muse Spark Open: 18.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Muse Spark 1.3: 83.0%Top
    • Muse Spark Open: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Muse Spark 1.3: 13.9%Top
    • Muse Spark Open: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Muse Spark 1.3: 48.7%Top
    • Muse Spark Open: 43.9%

  • GPQA Diamond

    Graduate-level science questions

    • Muse Spark 1.3: 93.5%Top
    • Muse Spark Open: 89.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Muse Spark 1.3: 33.3%Top
    • Muse Spark Open: 8.1%

  • SciCode

    Scientific computing in Python

    • Muse Spark 1.3: 58.8%Top
    • Muse Spark Open: 58.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Muse Spark 1.3: 83.0%Top
    • Muse Spark Open: 78.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Muse Spark 1.3 was tested at max 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
292.3 tok/s
Faster than 91% of 260 models
Time to first token
7.8 s
Quicker than 15% of 260 models
Prompt cache hits
1.5%
About $1.23 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 85.7%
  • 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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.