The YOLO family has evolved rapidly. In 2023 we had YOLOv8. By late 2024 YOLO11 arrived. And in September 2025, YOLO26 was released — purpose-built for edge deployment. If you’re building a computer vision system on NVIDIA Jetson in 2026, which model should you choose? This comparison breaks it all down.
A brief history of YOLO
YOLO (You Only Look Once) was first published in 2016 and changed computer vision forever by making real-time object detection practical. Since then, Ultralytics — the primary maintainer of modern YOLO releases — has pushed out version after version, each improving on accuracy, speed, and deployment flexibility.
- YOLOv8 (2023): Anchor-free detection, clean Python API, TensorRT/ONNX/CoreML export. Became the industry standard.
- YOLOv9 (2024): Introduced GELAN architecture and progressive distillation for better accuracy at same speed.
- YOLO10 (2024): NMS-free inference for reduced post-processing latency.
- YOLO11 (2024): Improved backbone, better small object detection, multi-task support.
- YOLO26 (Sep 2025): Edge-first design, removed Distribution Focal Loss, end-to-end NMS-free inference, new MuSGD optimiser.
Head-to-head: architecture differences
| Feature | YOLOv8 | YOLO11 | YOLO26 |
|---|---|---|---|
| Anchor-free | ✅ | ✅ | ✅ |
| NMS-free inference | ❌ | Partial | ✅ Full |
| Distribution Focal Loss | ✅ | ✅ | ❌ Removed |
| Small target detection | Good | Better | Best (STAL) |
| Multi-task support | ✅ | ✅ | ✅ |
| TensorRT export | ✅ | ✅ | ✅ |
| Edge-optimised design | Partial | Partial | ✅ Primary goal |
Speed benchmarks on NVIDIA Jetson Orin Nano (TensorRT FP16)
| Model (Nano variant) | FPS @ 640px | mAP50-95 (COCO) | Parameters |
|---|---|---|---|
| YOLOv8n | ~55 FPS | 37.3 | 3.2M |
| YOLO11n | ~58 FPS | 39.5 | 2.6M |
| YOLO26n | ~65 FPS | 40.1 | 2.4M |
YOLO26 is faster AND more accurate than YOLOv8 at the nano scale — a significant achievement. The removal of DFL and the NMS-free design reduces end-to-end latency substantially on edge devices.
Which model should you choose?
Choose YOLOv8 if…
- You already have a trained YOLOv8 model in production
- You need maximum community support and tutorials
- You are using third-party integrations that only support YOLOv8
- You are new to YOLO and want the most documented option
Choose YOLO11 if…
- You need better small object detection than YOLOv8
- You are training a new model from scratch today
- You need slightly fewer parameters for a tight memory budget
Choose YOLO26 if…
- You are deploying on NVIDIA Jetson or other edge hardware
- You need the lowest latency possible for real-time systems
- You want the best accuracy-speed trade-off in 2026
- You are building a new system and have no legacy constraints
Our recommendation for NVIDIA Jetson in 2026: Start with YOLO11n for new projects. If you need the absolute best edge performance and are comfortable with a newer model, use YOLO26n. Keep YOLOv8 for any project where you have existing trained weights or integrations.
How to switch from YOLOv8 to YOLO11
The good news: Ultralytics made the API identical. Switching is one line of code:
# YOLOv8
model = YOLO("yolov8n.pt")
# YOLO11 — identical API
model = YOLO("yolo11n.pt")
# YOLO26
model = YOLO("yolo26n.pt")Everything else — training, export, inference — stays exactly the same. Your existing pipelines work without modification.
The HemiHex Jetson Inspection Kit is compatible with all YOLO versions. It ships with JetPack 6.x pre-installed so you can run any model out of the box. Shop now →