YOLO26 vs YOLOv9
Compare YOLO26 and YOLOv9 side-by-side.
Compare YOLO26 vs YOLOv9 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
YOLO26 vs YOLOv9 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | YOLO26 | YOLOv9 |
|---|---|---|
| Organization | Ultralytics | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Oct 2025 | Feb 2024 |
| Context Window | — | — |
| Parameters | 2.4M-55.7M | 2.0M-57.3M |
| License | AGPL 3.0 | GPL v3 |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | commercial use means releasing your source code under GPL-3.0 |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | 56.3Extra Large · 55.7M params | Not benchmarked |
| Smallest size | 39.9Nano · 2.4M params | Not benchmarked |
| mAP range | 39.9–56.3across 5 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 39.9640×640 · 2.4M params | Not available |
| Small | 47.2640×640 · 9.5M params | Not available |
| Medium | 52.0640×640 · 20.4M params | Not available |
| Large | 53.6640×640 · 24.8M params | Not available |
| XL | 56.3640×640 · 55.7M params | Not available |
| 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. | ||
| Vision Tasks | ||
| Instance Segmentation | Demo (COCO) | Supported |
| Object Detection | Demo (COCO) | Supported |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
YOLO26 vs YOLOv9: 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.
YOLOv9 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in February 2024 under the GPL-3.0 license. It introduces Programmable Gradient Information (PGI), a mechanism that preserves complete input information through auxiliary reversible branches during training to address information loss in deep network layers. It also introduces the Generalized Efficient Layer Aggregation Network (GELAN), which achieves better parameter utilization compared to prior CSP-based designs.
YOLOv9-C achieves 53.0% AP on COCO with 42% fewer parameters and 21% less computation than YOLOv8-C at comparable accuracy. YOLOv9-E achieves 55.6% AP. The model is deployable through Roboflow Inference and supports fine-tuning via the standard training pipeline in the official repository.
