Roboflow

GPT-6.1 Sol vs Muse Spark Open

GPT-6.1 Sol and Muse Spark Open score 81.7% vs 69.4% averaged across five Vision Evals tasks (#6 and #25 of 65 models), so GPT-6.1 Sol leads overall.

  • GPT-6.1 Sol wins all 5 tasks, with its widest lead on object detection (80.8% vs 57.8%).
  • Muse Spark Open is cheaper: $0.0059 vs $0.0068 overall average cost per sample.
  • Muse Spark Open is faster: 7.4s vs 12.8s per sample.

Compare GPT-6.1 Sol 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.

GPT-6.1 Sol vs Muse Spark Open on Vision Evals

Score by taskGPT-6.1 SolMuse Spark Open
Object Detection
80.8
57.8
Reasoning
83.7
63.8
OCR
75.8
67.6
Identification
89.6
82.3
Counting
78.8
75.7

Roboflow Vision Evals. Full breakdown below.

GPT-6.1 Sol vs Muse Spark Open Comparison Table

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

PropertyGPT-6.1 SolMuse Spark Open
OrganizationOpenAIMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Oct 2026
Context Window1.1M1.0M
Parametersundisclosed816B total / 42B active
LicenseProprietaryCustom
Commercial usecommercial use per vendor termsSee license terms
Pricing per 1M tokens
Input $/1M$2.00$1.25
Output $/1M$10.00$4.25
Vision Tasks
CaptioningDemoSupported
ClassificationDemoSupported
Object DetectionDemoSupported
OCRDemoSupported
Visual Question AnsweringDemoSupported
Promptable Concept SegmentationDemoNot listed
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
81.7%
69.4%
Avg cost / sample$0.0068$0.0059
Avg speed / sample12.82s7.37s
By task
Object Detection (low)
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
57.8%
±0.6, Mean of 3 runs, range 57.0 to 58.2
$0.0073
Object Detection (high)
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
60.9%
±0.8, Mean of 3 runs, range 60.3 to 61.9
$0.013
Counting (low)
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
75.7%
±2.7, Mean of 3 runs, range 73.0 to 78.4
$0.0036
Counting (high)
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
75.2%
±4.1, Mean of 3 runs, range 71.6 to 79.7
$0.0073
Identification (low)
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
82.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0027
Identification (high)
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
80.2%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0056
OCR (low)
75.8%
$0.0069
67.6%
$0.0061
by category
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
Single value
60.0%
Transcription
89.0%
Structured JSON
85.5%
Text localization
29.5%
OCR (high)
75.8%
$0.016
68.6%
$0.014
by category
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Single value
58.3%
Transcription
91.4%
Structured JSON
88.2%
Text localization
32.2%
Reasoning (low)
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
63.8%
±1.0, Mean of 3 runs, range 62.9 to 64.9
$0.0049
Reasoning (high)
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042
65.1%
±3.0, Mean of 3 runs, range 62.3 to 68.2
$0.012

GPT-6.1 Sol vs Muse Spark Open: Overview

GPT-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

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, GPT-6.1 Sol performed better. It scores higher on all five vision tasks and averages 81.7% (#6 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 Object Detection benchmark at low effort, GPT-6.1 Sol leads with 80.8% against 57.8%. 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 $0.0068. GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.