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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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 Sol vs Muse Spark 1.1 Comparison Table
Evals updated October 13, 2026Pricing updated October 18, 2026
| Property | GPT-6.1 Sol | Muse Spark 1.1 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jul 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $10.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
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 / sample | 12.82s | 7.15s |
| By task | ||
| Object Detection (low) | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 | 60.6% ±1.9, Mean of 3 runs, range 58.4 to 62.1 |
| Object Detection (high) | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 | 60.0% ±0.5, Mean of 3 runs, range 59.6 to 60.7 |
| Counting (low) | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 | 76.6% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Counting (high) | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 | 76.1% ±0.7, Mean of 3 runs, range 75.7 to 77.0 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 75.8% | 68.8% |
| by category |
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| OCR (high) | 75.8% | 67.5% |
| by category |
|
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| Reasoning (low) | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 |
| Reasoning (high) | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 |
GPT-6.1 Sol vs Muse Spark 1.1: Overview
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 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.