YOLO11 vs YOLOE
Compare YOLO11 and YOLOE side-by-side.
Compare YOLO11 vs YOLOE 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
YOLO11 vs YOLOE Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | YOLO11 | YOLOE |
|---|---|---|
| Organization | Ultralytics | THU-MIG |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2024 | Mar 2025 |
| Context Window | — | — |
| Parameters | 2.6M-56.9M | 10M-50M |
| License | AGPL 3.0 | AGPL 3.0 |
| Model Sizes input resolution per size variant | ||
| Nano | 640×640 | |
| Small | 1280×1280, 640×640 | |
| Medium | 1280×1280, 640×640 | |
| Large | 1280×1280, 640×640 | |
| XL | 1280×1280, 640×640 | |
| Vision Tasks | ||
| Instance Segmentation | Demo (COCO) | |
| Object Detection | Demo (COCO) | |
| Model Features | ||
| Real-Time Vision | ||
| Zero-shot Detection | ||
YOLO11 vs YOLOE: Overview
YOLO11 is an object detection and multi-task vision model developed by Ultralytics, released in September 2024 under the AGPL-3.0 license. It is the latest generation in the Ultralytics YOLO series and supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a single unified framework. YOLO11 introduces architectural refinements that improve accuracy while reducing parameter count compared to YOLOv8 at equivalent model sizes.
YOLO11 is available in five model sizes from Nano to Extra Large and is deployable through the Ultralytics Python package, Roboflow Inference, and export formats including ONNX, TensorRT, and CoreML. It supports fine-tuning on custom datasets through the standard Ultralytics training API.
YOLOE (YOLO with Everything) is an open-vocabulary object detection and segmentation model developed by THU-MIG at Tsinghua University, released in March 2025 under the AGPL-3.0 license. It extends the YOLO architecture to support open-vocabulary detection through text and visual prompts, enabling the model to detect arbitrary object categories beyond a fixed training set without retraining. The design integrates prompt encoding directly into the YOLO framework while preserving real-time inference speed.
YOLOE is evaluated on COCO and LVIS benchmarks and supports both closed-set and open-vocabulary detection modes. It is built on the Ultralytics codebase and maintains compatibility with standard YOLO training and export workflows. YOLOE is suited for applications requiring flexible, prompt-driven object detection where the target object vocabulary may change at inference time.
