Muse Spark Open vs SAM 3
Muse Spark Open: 69.4% average across five Vision Evals tasks (#25 of 65 models). We have not run SAM 3 through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Muse Spark Open vs SAM 3 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.
Muse Spark Open vs SAM 3 Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | Muse Spark Open | SAM 3 |
|---|---|---|
| Organization | Meta | Meta |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Nov 2025 |
| Context Window | 1.0M | — |
| Parameters | 816B total / 42B active | Unknown |
| License | Custom | Custom |
| Commercial use | See license terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | No published price |
| Output $/1M | $4.25 | No published price |
| Vision Tasks | ||
| Object Detection | Supported | Demo |
| Captioning | Supported | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Instance Segmentation | Not listed | Supported |
| Multi-Label Classification | Supported | Not listed |
| OCR | Supported | Not listed |
| Open Vocabulary Object Detection | Not listed | Supported |
| Promptable Concept Segmentation | Not listed | Demo |
| Video Object Tracking | Not listed | Supported |
| Vision Language | Supported | Not listed |
| Visual Question Answering | Supported | Not listed |
| Zero Shot Segmentation | Not listed | Supported |
| Model Features | ||
| LLMs with Vision Capabilities | Supported | Not listed |
| Zero-shot Detection | Not listed | Supported |
Both support 2 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 69.4% | Not evaluated |
| Avg cost / sample | $0.0059 | – |
| Avg speed / sample | 7.37s | – |
| By task | ||
| Object Detection (low) | 57.8% ±0.6, Mean of 3 runs, range 57.0 to 58.2 | – |
| Object Detection (high) | 60.9% ±0.8, Mean of 3 runs, range 60.3 to 61.9 | – |
| Counting (low) | 75.7% ±2.7, Mean of 3 runs, range 73.0 to 78.4 | – |
| Counting (high) | 75.2% ±4.1, Mean of 3 runs, range 71.6 to 79.7 | – |
| Identification (low) | 82.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | – |
| Identification (high) | 80.2% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | – |
| OCR (low) | 67.6% | – |
| by category |
| |
| OCR (high) | 68.6% | – |
| by category |
| |
| Reasoning (low) | 63.8% ±1.0, Mean of 3 runs, range 62.9 to 64.9 | – |
| Reasoning (high) | 65.1% ±3.0, Mean of 3 runs, range 62.3 to 68.2 | – |
Muse Spark Open vs SAM 3: Overview
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.
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
