GPT-6 Sol vs Muse Ember
GPT-6 Sol and Muse Ember score 76.9% vs 76.6% averaged across five Vision Evals tasks (#14 and #16 of 67 models), so they are effectively tied.
- Muse Ember wins 3 of the 5 tasks, but the widest gap is object detection, where GPT-6 Sol leads 74.7% vs 63.8%.
- Muse Ember is about 1/8 of the cost: $0.0009 vs $0.0074 overall average cost per sample.
- Muse Ember is faster: 6.1s vs 10.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6 Sol vs Muse Ember 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 Ember on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6 Sol vs Muse Ember Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | GPT-6 Sol | Muse Ember |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Nov 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 | $0.100 |
| Output $/1M | $10.00 | $0.500 |
| 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% | 76.6% |
| Avg cost / sample | $0.0074 | $0.0009 |
| Avg speed / sample | 10.20s | 6.08s |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | 63.8% ±0.5, Mean of 3 runs, range 63.3 to 64.3 |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | 64.0% ±0.5, Mean of 3 runs, range 63.5 to 64.5 |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 79.7% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 90.6% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| OCR (low) | 66.7% | 69.5% |
| by category |
|
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| OCR (high) | 68.8% | 70.5% |
| by category |
|
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| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | 78.2% ±1.3, Mean of 3 runs, range 76.8 to 79.5 |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | 81.9% ±1.7, Mean of 3 runs, range 80.1 to 83.4 |
GPT-6 Sol vs Muse Ember: 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 Ember is a proprietary multimodal reasoning model from Meta, released on November 9, 2026. It accepts text, images, video, audio, and PDF documents and returns text. The Meta Model API serves it as muse-ember-1.0, with a 1,048,576-token context window and up to 524,288 output tokens. Reasoning effort is adjustable from minimal to max.
Meta positions Ember for repeated tasks within larger agent workflows: tool calls, routing, coding subtasks, and processing collections of images and documents. It can caption or sort photos and extract fields from records while a larger Muse model handles planning. Meta recommends Spark or Wonder for harder single-shot coding and knowledge-heavy problems. Ember is available as a hosted model; its weights are not publicly released.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Ember performed better. It scores higher on 3 of the five vision tasks and averages 76.6% (#16 of 67) against 76.9% (#14 of 67) for GPT-6 Sol. 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 63.8%. This is the widest gap between the two models across the benchmark's tasks.
Muse Ember is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0009 per sample against $0.0074. GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Muse Ember is priced at $0.10 per 1M input tokens and $0.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Muse Ember is faster. Across Roboflow's Vision Evals it averaged 6.1s per inference against 10.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.