Model Landscape

SAM 3 vs Alternatives

SAM 3 is a unified foundation model for promptable segmentation in images & videos that can exhaustively segment all instances of an open-vocabulary concept from a short text phrase. Evaluated on ~270K unique concepts with 75–80% of human performance.

See how it compares to other segmentation models — and find the right tool for your use-case.

Feature Comparison

“Open-vocab concept segmentation” means: type a noun phrase like “yellow school bus” and get masks for all matching instances.

Model	Best At	Output	License
SAM 3	Promptable + concept segmentation in images/videos	Masks + IDs for matching instances	SAM License
SAM 2	Promptable segmentation in images/videos; real-time video processing	Masks; tracking across frames	Meta license
Grounded-SAM	"Text → detect → segment" open-world pipelines	Boxes + masks	Mixed (pipeline)
Grounded-SAM 2	Open-world grounding + tracking in video (pipeline)	Tracks + masks	Mixed (pipeline)
SEEM	Universal "prompt-anything" interface across prompt types	Masks, interactive refinement	Varies
X-Decoder	Open-vocabulary + referring segmentation; multimodal decoding	Masks + language tokens	Repo available
Mask2Former	Panoptic / instance / semantic on standard benchmarks	Masks + classes (taxonomy)	MIT
SegFormer	Efficient semantic segmentation (taxonomy)	Per-pixel semantic labels	NVlabs
YOLO (seg)	Real-time instance segmentation when classes are known	Masks + class labels + confidences	Ultralytics
Mask R-CNN	Classic instance segmentation baseline	Masks + boxes + class labels	Detectron2

Full support Partial / pipeline-dependent Not supported

Decision Guide

When Should I Choose SAM 3?

Different models shine in different scenarios. Here’s a practical guide to help you decide.

SAM 3 vs SAM 2

Choose SAM 3 when

You need promptable concept segmentation — "segment all [noun phrase] instances" with open vocabulary and exhaustive instance coverage.

Choose SAM 2 when

Your focus is interactive / video promptable segmentation and you don't need concept-exhaustive behavior.

SAM 3 vs Grounded-SAM Pipelines

Choose SAM 3 when

You want a single-model API that directly returns masks/IDs for all instances matching a concept prompt — no detector thresholds or multi-model complexity.

Choose Grounded-SAM Pipelines when

You already rely on open-vocabulary detection workflows ("text → detect → segment") and are comfortable tuning detector thresholds.

SAM 3 vs SEEM / X-Decoder

Choose SAM 3 when

Your product is primarily high-quality concept masks + tracking (productionized PCS).

Choose SEEM / X-Decoder when

Your application is more "multimodal segmentation + language tasks" and you want a broader "universal interface" research direction.

SAM 3 vs YOLO-seg / Mask R-CNN / Mask2Former

Choose SAM 3 when

You need "segment any concept by phrase" without retraining — including uncommon categories like "wire", "logo", or "plant leaves".

Choose YOLO-seg / Mask R-CNN / Mask2Former when

You have a fixed label set and want predictable class outputs, strong speed, or classic deployment patterns.

SAM 3 Licensing Note

SAM 3 is distributed under the SAM License — a “non-exclusive, worldwide, royalty-free limited license” that includes restrictions (e.g., trade controls and prohibited uses). It is not Apache / MIT. When using the segmentationAPI, you are accessing SAM 3 through our hosted service; please review the license for details relevant to your use-case.

Ready to try SAM 3?

Jump into the playground, explore the docs, or start building with our API — no signup required to try.