Compare YOLO26 vs YOLOv8 live
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YOLO26 vs YOLOv8 on COCO
At similar sizes, YOLO26 Large (24.8M params) scores 53.6 and YOLOv8 Medium (25.9M) scores 49.2.
COCO val2017 mAP 50:95 against parameter count, one point per size · from leaderboard.roboflow.com
YOLO26 vs YOLOv8 Comparison Table
| Property | YOLO26 | YOLOv8 |
|---|---|---|
| Organization | Ultralytics | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Oct 2025 | Jan 2023 |
| Context Window | — | — |
| Parameters | 2.4M-55.7M | 3.2M-68.2M |
| 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 | 56.3Extra Large · 55.7M params | 52.9XL · 68.2M params |
| Smallest size | 39.9Nano · 2.4M params | 36.5Nano · 3.2M params |
| mAP range | 39.9–56.3across 5 sizes | 36.5–52.9across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 39.9640×640 · 2.4M params | 36.5640×640 · 3.2M params |
| Small | 47.2640×640 · 9.5M params | 44.1640×640 · 11.2M params |
| Medium | 52.0640×640 · 20.4M params | 49.2640×640 · 25.9M params |
| Large | 53.6640×640 · 24.8M params | 51.8640×640 · 43.7M params |
| XL | 56.3640×640 · 55.7M params | 52.9640×640 · 68.2M 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 | ||
| Instance Segmentation | Demo (COCO) | Not listed |
Both support 1 more task | ||
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
YOLO26 vs YOLOv8: Overview
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.
YOLOv8 is an object detection and multi-task vision model developed by Ultralytics, released in January 2023 under the AGPL-3.0 license. It succeeds YOLOv5 and introduces an anchor-free detection head, a new C2f module for improved gradient flow, and a decoupled head that separates classification and regression tasks. These changes improve both accuracy and training efficiency compared to earlier Ultralytics models.
YOLOv8 supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a unified codebase. It is available in five sizes from Nano to Extra Large and exports to ONNX, TensorRT, CoreML, and other formats. YOLOv8 is one of the most widely adopted detection models in production and is directly supported by Roboflow Inference for custom model training and deployment.
