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Meta · Balanced

Muse Spark 1.1

Added Jul 9, 2026

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Use in the app

Meta's Muse Spark 1.1 is a long-context multimodal reasoning model served by the Meta Model API.

  • Reasoning
  • Vision
  • Audio input
  • Video input
Input
Text, Images, Video, Audio
Output
Text
Reasoning effort
minimal · low · medium · high · xhigh
Discovery tier
Balanced

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
65.5K
Average output (7 days)
460 tokens
Longer than 45% of 465 models

Side by side

Muse Spark 1.1 compared

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

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Muse Spark 1.1MetaThis modelGemini 3.6 FlashGoogleRemove
ProviderMetaGoogle
Discovery tierBalancedEfficient
AddedJul 9, 2026Jul 21, 2026
Limits
Context window1M1.05MBest
Max output65.54K65.54K
Average output460 tokens932 tokens
Pricing, USD per 1M tokens
Input$1.25$0.75Best
Output$4.25$3.75Best
Cache read$0.15$0.075Best
Cache write—$0.0417
Performance
Throughput—234.0 tok/s
Time to first token—7.9 s
Request success (24h)—99.4%
Benchmark indexes
Intelligence Index33.734.0Best
Coding Index71.3Best69.2
Capabilities
ReasoningYesYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
PDF inputNoYes
Audio inputYesYes
Video inputYesYes
Reasoning effortminimal, low, medium, high, xhighminimal, low, medium, high

Public evaluations

Benchmarks

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

  • Muse Spark 1.1
  • Gemini 3.6 Flash

Intelligence Index

  • Muse Spark 1.133.7
  • Gemini 3.6 Flash34.0

Muse Spark 1.1 tested at xhigh reasoning effort

Muse Spark 1.1: Better than 76% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Muse Spark 1.1: 71.3Top
    • Gemini 3.6 Flash: 69.2

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Muse Spark 1.1: 38.8%
    • Gemini 3.6 Flash: 53.0%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Muse Spark 1.1: 13.7%
    • Gemini 3.6 Flash: 16.4%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Muse Spark 1.1: 850 Elo
    • Gemini 3.6 Flash: 955 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Muse Spark 1.1: 1,211 Elo
    • Gemini 3.6 Flash: 1,286 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Muse Spark 1.1: 14.4%
    • Gemini 3.6 Flash: 17.4%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Muse Spark 1.1: 77.7%
    • Gemini 3.6 Flash: 80.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Muse Spark 1.1: 7.2%
    • Gemini 3.6 Flash: 10.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Muse Spark 1.1: 46.2%Top
    • Gemini 3.6 Flash: 40.8%

  • GPQA Diamond

    Graduate-level science questions

    • Muse Spark 1.1: 89.8%
    • Gemini 3.6 Flash: 92.8%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Muse Spark 1.1: 6.1%
    • Gemini 3.6 Flash: 7.1%Top

  • SciCode

    Scientific computing in Python

    • Muse Spark 1.1: 58.8%Top
    • Gemini 3.6 Flash: 53.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Muse Spark 1.1: 77.7%
    • Gemini 3.6 Flash: 80.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Muse Spark 1.1 was tested at xhigh reasoning effort; compare scores measured at the same effort.Artificial Analysis

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-1.1",
    "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-1.1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

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