Roboflow

Grounding DINO vs YOLO World

Compare Grounding DINO and YOLO World side-by-side.

Compare Grounding DINO vs YOLO World live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

Tencent

Grounding DINO vs YOLO World Comparison Table

Evals updated July 10, 2026Pricing updated July 21, 2026

PropertyGrounding DINOYOLO World
OrganizationIDEA ResearchTencent AI Lab
Categoryopenopen
Modalityvisionmultimodal
Release DateMar 2023Feb 2024
Context Window13.0M
Parameters172M-341M
LicenseApache 2.0GPL v3
Vision Tasks
Object DetectionDemo
Open Vocabulary Object Detection
Phrase Grounding
Model Features
Zero-shot Detection
Foundation Vision
Multimodal Vision
Real-Time Vision

Grounding DINO vs YOLO World: Overview

Grounding DINO

Grounding DINO is an open-vocabulary object detection model developed by IDEA Research, released in March 2023 under the Apache 2.0 license. It extends the DINO transformer-based detector with grounded pre-training, enabling it to detect arbitrary objects described by free-form text queries rather than a fixed set of predefined categories. The model integrates a text encoder with a visual backbone through a feature fusion module that aligns language and visual representations at multiple scales.

Grounding DINO achieves strong zero-shot detection performance on COCO, LVIS, and ODinW benchmarks, and supports referring expression comprehension tasks. It is widely used as a foundation for open-vocabulary detection pipelines and as the detection backbone in systems such as Grounded-SAM. The model is particularly suited for applications requiring flexible, text-driven object localization across diverse domains.

YOLO World

YOLO-World v2 Small (YOLO-World-S-v2) is the smallest variant of Tencent AI Lab’s YOLO-World v2 family, released around February 2024 under GPL-v3. With ~13 million parameters, it adopts a prompt-then-detect paradigm using offline vocabularies and is pretrained on large-scale datasets such as Objects365 and GoldG. The model processes image inputs at 640×640 or 1280×1280 resolutions and supports zero-shot open-vocabulary object detection, enabling recognition of novel categories from text prompts without retraining.

Evaluations show competitive results across benchmarks like LVIS and COCO, while maintaining real-time efficiency. On an NVIDIA V100, the small variant reaches ~74 FPS at standard resolutions. Together with larger YOLO-World v2 models, it provides a scalable framework for efficient, open-vocabulary detection across diverse deployment settings.