GPT-6 Sol vs Muse Spark Open
GPT-6 Sol and Muse Spark Open score 76.9% vs 69.4% averaged across five Vision Evals tasks (#13 and #25 of 65 models), so GPT-6 Sol leads overall.
- GPT-6 Sol wins 4 of the 5 tasks, with its widest lead on object detection (74.7% vs 57.8%).
- Muse Spark Open is cheaper: $0.0059 vs $0.0074 overall average cost per sample.
- Muse Spark Open is faster: 7.4s vs 10.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6 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 Sol vs Muse Spark Open on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6 Sol vs Muse Spark Open Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6 Sol | Muse Spark Open |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Oct 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | 816B total / 42B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.25 |
| Output $/1M | $10.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Supported |
| Visual Question Answering | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 76.9% | 69.4% |
| Avg cost / sample | $0.0074 | $0.0059 |
| Avg speed / sample | 10.20s | 7.37s |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 66.7% | 67.6% |
| by category |
|
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| OCR (high) | 68.8% | 68.6% |
| by category |
|
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| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 |
GPT-6 Sol vs Muse Spark Open: Overview
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. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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 Sol performed better. It scores higher on 4 of the five vision tasks and averages 76.9% (#13 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 Sol leads with 74.7% 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 $0.0074. GPT-6 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 10.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.