Compare YOLO11 vs YOLO26 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Run a pretrained Instance Segmentation (COCO) model and compare detections on the same image.
Upload an image
Drag and drop an image here, or click to browse
YOLO11 vs YOLO26 on COCO
At similar sizes, YOLO11 Small (9.4M params) scores 45.5 and YOLO26 Small (9.5M) scores 47.2.
COCO val2017 mAP 50:95 against parameter count, one point per size · from leaderboard.roboflow.com
YOLO11 vs YOLO26 Comparison Table
| Property | YOLO11 | YOLO26 |
|---|---|---|
| Organization | Ultralytics | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2024 | Oct 2025 |
| Context Window | — | — |
| Parameters | 2.6M-56.9M | 2.4M-55.7M |
| License | AGPL 3.0 | AGPL 3.0 |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | commercial use needs a license (included on Roboflow cloud) |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | 53.6XL · 56.9M params | 56.3Extra Large · 55.7M params |
| Smallest size | 38.6Nano · 2.6M params | 39.9Nano · 2.4M params |
| mAP range | 38.6–53.6across 5 sizes | 39.9–56.3across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 38.6640×640 · 2.6M params | 39.9640×640 · 2.4M params |
| Small | 45.5640×640 · 9.4M params | 47.2640×640 · 9.5M params |
| Medium | 50.5640×640 · 20.1M params | 52.0640×640 · 20.4M params |
| Large | 52.2640×640 · 25.3M params | 53.6640×640 · 24.8M params |
| XL | 53.6640×640 · 56.9M params | 56.3640×640 · 55.7M params |
| COCO val2017 mAP 50:95 and parameter counts from leaderboard.roboflow.com. Size names don't line up across model families, so compare sizes with similar parameter counts. The live demo runs some of these sizes at other resolutions, which were not benchmarked. | ||
| Vision Tasks | ||
Both support 2 more tasks | ||
| Model Features | ||
| Real-Time Vision | Supported | Not listed |
YOLO11 vs YOLO26: 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.
YOLO26 is a real-time object detection model developed by Ultralytics, released in October 2025. It introduces a native end-to-end, NMS-free architecture that eliminates the Non-Maximum Suppression post-processing step, reducing CPU latency by up to 43% for the Nano variant compared to NMS-dependent versions. The model incorporates the MuSGD optimizer and ProgLoss with STAL for improved training stability and small-object detection, and removes Distribution Focal Loss to ensure maximum compatibility with ONNX and TensorRT export targets.
YOLO26 supports object detection, instance segmentation, pose estimation, and oriented bounding box detection within a unified framework, with model sizes available from Nano to Extra Large. Its NMS-free design makes it particularly well suited for deployment scenarios where post-processing overhead is a bottleneck, such as embedded systems and real-time edge inference pipelines.
