Roboflow

Muse Spark 1.3 vs Muse Spark Open

Muse Spark 1.3 and Muse Spark Open score 73.9% vs 69.4% averaged across five Vision Evals tasks (#17 and #25 of 65 models), so Muse Spark 1.3 leads overall.

  • Muse Spark 1.3 wins 4 of the 5 tasks, with its widest lead on reasoning (73.3% vs 63.8%).
  • Muse Spark Open is cheaper: $0.0059 vs $0.0084 overall average cost per sample.
  • Muse Spark Open is faster: 7.4s vs 25.5s per sample.

Compare Muse Spark 1.3 vs Muse Spark Open live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Muse Spark 1.3 vs Muse Spark Open on Vision Evals

Score by taskMuse Spark 1.3Muse Spark Open
Identification
92.7
82.3
Reasoning
73.3
63.8
OCR
70.4
67.6
Counting
74.3
75.7
Object Detection
58.6
57.8

Roboflow Vision Evals. Full breakdown below.

Muse Spark 1.3 vs Muse Spark Open Comparison Table

Evals updated October 26, 2026Pricing updated October 27, 2026

PropertyMuse Spark 1.3Muse Spark Open
OrganizationMetaMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Oct 2026
Context Window1.0M1.0M
ParametersUnknown816B total / 42B active
LicenseProprietaryCustom
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$1.25$1.25
Output $/1M$4.25$4.25
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
73.9%
69.4%
Avg cost / sample$0.0084$0.0059
Avg speed / sample25.50s7.37s
By task
Object Detection (low)
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
57.8%
±0.6, Mean of 3 runs, range 57.0 to 58.2
$0.0073
Object Detection (high)
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
60.9%
±0.8, Mean of 3 runs, range 60.3 to 61.9
$0.013
Counting (low)
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
75.7%
±2.7, Mean of 3 runs, range 73.0 to 78.4
$0.0036
Counting (high)
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
75.2%
±4.1, Mean of 3 runs, range 71.6 to 79.7
$0.0073
Identification (low)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
82.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0027
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
80.2%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0056
OCR (low)
70.4%
$0.0086
67.6%
$0.0061
by category
Single value
62.6%
Transcription
91.8%
Structured JSON
88.0%
Text localization
33.3%
Single value
60.0%
Transcription
89.0%
Structured JSON
85.5%
Text localization
29.5%
OCR (high)
69.8%
$0.015
68.6%
$0.014
by category
Single value
62.2%
Transcription
86.8%
Structured JSON
87.2%
Text localization
35.2%
Single value
58.3%
Transcription
91.4%
Structured JSON
88.2%
Text localization
32.2%
Reasoning (low)
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
63.8%
±1.0, Mean of 3 runs, range 62.9 to 64.9
$0.0049
Reasoning (high)
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012
65.1%
±3.0, Mean of 3 runs, range 62.3 to 68.2
$0.012

Muse Spark 1.3 vs Muse Spark Open: Overview

Muse Spark 1.3

Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.

The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.

Muse Spark Open

Muse Spark Open is an open-weight multimodal model released by Meta on October 15, 2026. Its sparse mixture-of-experts architecture has 816 billion total parameters and activates 42 billion per token. It accepts text, images, audio and video, produces text, and supports a context window of 1,048,576 tokens. Meta provides BF16 and NVFP4 checkpoints for self-hosting, alongside access through hosted inference partners.

The model is designed for coding, tool use and multi-step reasoning over long tasks. Its visual inputs support workflows involving screenshots, documents and other image content. Vision Evals measures its text understanding, counting, identification, reasoning and object detection at Low and High effort. The published weights use the custom Muse Model License, with commercial use subject to its terms and the accompanying usage policy.

Frequently Asked Questions

On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on 4 of the five vision tasks and averages 73.9% (#17 of 65) against 69.4% (#25 of 65) for Muse Spark Open. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Identification benchmark at low effort, Muse Spark 1.3 leads with 92.7% against 82.3%. This is the widest gap between the two models across the benchmark's tasks.

Muse Spark Open is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0059 per sample against $0.0084. Muse Spark 1.3 is priced at $1.25 per 1M input tokens and $4.25 per 1M output; Muse Spark Open is priced at $1.25 per 1M input tokens and $4.25 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Muse Spark Open is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 25.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.