Gemini 3 Pro vs SAM 3
Compare Gemini 3 Pro and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Gemini 3 Pro vs SAM 3: Overview
Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.
The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.
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.
Gemini 3 Pro vs SAM 3 Comparison Table
| Property | Gemini 3 Pro | SAM 3 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2025 | Nov 2025 |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | Proprietary |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | ||
| Classification | ||
| Instance Segmentation | ||
| OCR | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | ||
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Zero-shot Detection | ||