Muse Spark 1.3 vs SAM 3
Compare Muse Spark 1.3 and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Muse Spark 1.3 vs SAM 3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Muse Spark 1.3 | SAM 3 |
|---|---|---|
| Organization | Meta | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Nov 2025 |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | |
| Output $/1M | $4.25 | |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Zero-shot Detection | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 79.8% | Not evaluated |
| Avg cost / sample | $0.0075 | – |
| Avg speed / sample | 23.14s | – |
| By task | ||
| Object Detection (low) | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 | – |
| Object Detection (high) | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 | – |
| Counting (low) | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 | – |
| Counting (high) | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 | – |
| Identification (low) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | – |
| OCR (low) | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 | – |
| OCR (high) | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 | – |
| Data Extraction (low) | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 | – |
| Data Extraction (high) | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 | – |
| Reasoning (low) | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 | – |
| Reasoning (high) | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 | – |
Muse Spark 1.3 vs SAM 3: Overview
Muse Spark 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
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.
Frequently Asked Questions
SAM 3 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Muse Spark 1.3 is released under Proprietary, while SAM 3 uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.
