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GPT-6.1 Sol vs Muse Spark 1.1

Compare GPT-6.1 Sol and Muse Spark 1.1 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-6.1 Sol
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MetaMuse Spark 1.1
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Models in this comparison

GPT-6.1 Sol vs Muse Spark 1.1 on Vision Evals

GPT-6.1 Sol scores higher on 4 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6.1 Sol leads 80.8% to 60.6%.

Overall, GPT-6.1 Sol averages 81.7% (#5 of 63) against 74.4% (#15 of 63) for Muse Spark 1.1.

GPT-6.1 Sol is cheaper ($0.0068 vs $0.0075 per sample), while Muse Spark 1.1 is faster (7.2s vs 12.8s per sample).

GPT-6.1 SolMuse Spark 1.1

GPT-6.1 Sol vs Muse Spark 1.1 Comparison Table

Evals updated October 13, 2026Pricing updated October 18, 2026

PropertyGPT-6.1 SolMuse Spark 1.1
OrganizationOpenAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Jul 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
81.7%
74.4%
Avg cost / sample$0.0068$0.0075
Avg speed / sample12.82s7.15s
By task
Object Detection (low)
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
60.6%
±1.9, Mean of 3 runs, range 58.4 to 62.1
$0.0097
Object Detection (high)
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
60.0%
±0.5, Mean of 3 runs, range 59.6 to 60.7
$0.015
Counting (low)
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
76.6%
±1.3, Mean of 3 runs, range 75.7 to 78.4
$0.0042
Counting (high)
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
76.1%
±0.7, Mean of 3 runs, range 75.7 to 77.0
$0.0079
Identification (low)
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
91.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0033
Identification (high)
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0065
OCR (low)
75.8%
$0.0069
68.8%
$0.0076
by category
Single value
58.3%
Transcription
95.7%
Structured JSON
89.5%
Text localization
74.4%
Single value
60.4%
Transcription
90.1%
Structured JSON
86.1%
Text localization
33.9%
OCR (high)
75.8%
$0.016
67.5%
$0.015
by category
Single value
56.5%
Transcription
96.0%
Structured JSON
90.4%
Text localization
76.7%
Single value
60.4%
Transcription
81.6%
Structured JSON
85.7%
Text localization
31.4%
Reasoning (low)
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
74.2%
±1.7, Mean of 3 runs, range 72.2 to 75.5
$0.0061
Reasoning (high)
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042
76.6%
±1.3, Mean of 3 runs, range 75.5 to 78.2
$0.013

GPT-6.1 Sol vs Muse Spark 1.1: 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.

On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. 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 1.1

Muse Spark 1.1 is a natively multimodal reasoning model from Meta Superintelligence Labs, released on July 9, 2026, as a significant upgrade to the original Muse Spark. The model accepts text, image, video, PDF, and audio as input and produces text output. It operates with a 1-million-token context window (1,048,576 tokens per the Meta Model API documentation) and is designed specifically for agentic tasks that require planning, tool use, computer use, and multi-agent orchestration. The model runs in a "Thinking" mode, where adjustable reasoning effort is applied before generating a response. It can function both as a main agent gathering context, forming plans, and delegating to parallel subagents and as a subagent that adheres to assigned tasks and escalates when needed. It is trained to decide autonomously when to write automation scripts versus interact directly with a user interface.

Muse Spark 1.1 supports a range of multimodal capabilities including visual perception, image and video captioning, visual-to-code generation, and document analysis. The model was evaluated under Meta's Advanced AI Scaling Framework across frontier risk categories including chemical and biological threats, cybersecurity, and loss-of-control scenarios. Parameter count, architecture details, and training data composition are not publicly disclosed. The model is proprietary and closed-weight, accessible to consumers through the Meta AI app and to developers via the Meta Model API, which launched in public preview alongside this release.