Roboflow

Promptable Concept Segmentation Models

The one promptable concept segmentation model in our catalog. Try it on your own images, free in the Roboflow Playground.

1 model · 1 open-weight · 1 free to try · prices synced Sep 11, 2026

Open-Source Promptable Concept Segmentation Models

1 model with downloadable weights you can self-host under their licenses (Custom). All run live in the Playground through hosted APIs, so self-hosting is optional.

Meta
SAM 3
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.

How to Choose a Promptable Concept Segmentation Model

Concept segmentation extends promptable segmentation from "whatever is at this click" to "every instance of this concept": one prompt, instance masks for all matches. It is the newest family on this site, popularized by SAM 3.

Classic SAM segments one thing per prompt, which is ideal for interactive editing. Concept-promptable models take a phrase or an example and return masks for every match in the image, which is what labeling, counting, and scene analytics actually need. If your workflows currently chain a detector into SAM to get that behavior, a concept-promptable model folds it into one step.

The sweet spot is open-world and one-off jobs: labeling a new dataset, ad hoc analysis, editing pipelines. For a fixed, high-volume segmentation task, a compact model trained on masks the promptable model helped label still wins on cost and latency, the same trade as everywhere else on this site.

The bottom line: Reach for concept segmentation when you need every instance of something you can describe, and graduate fixed high-volume tasks to a trained model once the concept stops changing.

Frequently Asked Questions About Promptable Concept Segmentation Models

Promptable concept segmentation is the task of segmenting every instance of a concept you specify with a prompt, such as a text phrase, an example box, or a point, and returning instance-level masks for all matches. Where classic SAM segments "whatever is at this click", concept segmentation as in SAM 3 finds and masks every occurrence, merging open-vocabulary detection with segmentation in one pass: "every yellow cone" yields a mask per cone. This removes the need to train a custom segmentation model for many one-off jobs. It is used for interactive dataset labeling, targeted image editing, and open-world scene analytics. This page covers the one promptable concept segmentation model we track, SAM 3, open-weight and free to self-host; it runs live in the Playground so you can test it on your own images.

SAM 3 is the only promptable concept segmentation model in our catalog, so there is nothing here to rank it against. For fixed categories in production, a model fine-tuned on your own data typically beats any general-purpose model. Try it on your own images to see whether it fits.

Yes. The one promptable concept segmentation model we track, SAM 3, is open-weight, free to self-host under its license (Custom).

Yes. You can run SAM 3 in the Roboflow Playground for free. Upload an image and see its output, no setup required.

This page lists the one promptable concept segmentation models in the Roboflow Playground catalog, all of them open-weight and free to self-host; all of them run live in the Roboflow Playground on your own images. Compare licenses, parameters, and release dates side by side, or open any model page for full details.