Roboflow

GPT-6 Sol vs Muse Spark 1.3

Compare GPT-6 Sol and Muse Spark 1.3 side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.

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OpenAIGPT-6 Sol
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MetaMuse Spark 1.3
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Models in this comparison

OpenAI

GPT-6 Sol vs Muse Spark 1.3 on Vision Evals

GPT-6 Sol scores higher on 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6 Sol leads 74.7% to 58.6%.

Overall, GPT-6 Sol averages 76.9% (#12 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3.

GPT-6 Sol is both cheaper ($0.0074 vs $0.0084 per sample) and faster (10.2s vs 25.5s per sample).

GPT-6 SolMuse Spark 1.3

GPT-6 Sol vs Muse Spark 1.3 Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyGPT-6 SolMuse Spark 1.3
OrganizationOpenAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Sep 2026
Context Window1.1M1.0M
ParametersundisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$1.25
Output $/1M$10.00$4.25
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationDemoNot listed
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
76.9%
73.9%
Avg cost / sample$0.0074$0.0084
Avg speed / sample10.20s25.50s
By task
Object Detection (low)
74.7%
±0.7, Mean of 3 runs, range 74.1 to 75.5
$0.011
58.6%
±0.7, Mean of 3 runs, range 58.0 to 59.4
$0.011
Object Detection (high)
76.4%
±1.2, Mean of 3 runs, range 75.0 to 77.4
$0.022
56.6%
±2.4, Mean of 3 runs, range 54.5 to 59.3
$0.017
Counting (low)
76.6%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0039
74.3%
±2.0, Mean of 3 runs, range 73.0 to 77.0
$0.0049
Counting (high)
77.5%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.0070
75.7%
±3.4, Mean of 3 runs, range 73.0 to 79.7
$0.0094
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0027
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0036
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0063
OCR (low)
66.7%
$0.0077
70.4%
$0.0086
by category
Single value
45.6%
Transcription
88.3%
Structured JSON
83.4%
Text localization
65.7%
Single value
62.6%
Transcription
91.8%
Structured JSON
88.0%
Text localization
33.3%
OCR (high)
68.8%
$0.018
69.8%
$0.015
by category
Single value
47.4%
Transcription
91.0%
Structured JSON
85.3%
Text localization
68.7%
Single value
62.2%
Transcription
86.8%
Structured JSON
87.2%
Text localization
35.2%
Reasoning (low)
72.6%
±3.6, Mean of 3 runs, range 69.5 to 76.8
$0.0037
73.3%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.0064
Reasoning (high)
77.7%
±1.0, Mean of 3 runs, range 76.8 to 78.8
$0.0061
73.1%
±1.0, Mean of 3 runs, range 72.2 to 74.2
$0.012

GPT-6 Sol vs Muse Spark 1.3: Overview

GPT-6 Sol

GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.

The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.

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. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on 3 of the five vision tasks and averages 76.9% (#12 of 63) against 73.9% (#16 of 63) for Muse Spark 1.3. 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 Sol leads with 74.7% against 58.6%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 per sample against $0.0084. GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Muse Spark 1.3 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.

GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 10.2s per inference against 25.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.