Muse Spark 1.1 vs SAM 3
Compare Muse Spark 1.1 and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Muse Spark 1.1 vs SAM 3 Comparison Table
Evals updated October 8, 2026Pricing updated October 11, 2026
| Property | Muse Spark 1.1 | SAM 3 |
|---|---|---|
| Organization | Meta | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Nov 2025 |
| Context Window | 1.0M | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | No published price |
| Output $/1M | $4.25 | No published price |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Demo | 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 | Demo | 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 | Demo | Not listed |
| Zero Shot Segmentation | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| Multimodal Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Not listed |
| Zero-shot Detection | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 74.4% | Not evaluated |
| Avg cost / sample | $0.0075 | – |
| Avg speed / sample | 7.15s | – |
| By task | ||
| Object Detection (low) | 60.6% ±1.9, Mean of 3 runs, range 58.4 to 62.1 | – |
| Object Detection (high) | 60.0% ±0.5, Mean of 3 runs, range 59.6 to 60.7 | – |
| Counting (low) | 76.6% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | – |
| Counting (high) | 76.1% ±0.7, Mean of 3 runs, range 75.7 to 77.0 | – |
| Identification (low) | 91.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| Identification (high) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| OCR (low) | 68.8% | – |
| by category |
| |
| OCR (high) | 67.5% | – |
| by category |
| |
| Reasoning (low) | 74.2% ±1.7, Mean of 3 runs, range 72.2 to 75.5 | – |
| Reasoning (high) | 76.6% ±1.3, Mean of 3 runs, range 75.5 to 78.2 | – |
Muse Spark 1.1 vs SAM 3: Overview
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.
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 1.1 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.
