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Model Comparisons#

Choosing the right neural network architecture is the cornerstone of any successful computer vision project. The Ultralytics Model Comparison Hub collects detailed technical analyses and performance benchmarks covering the trade-offs between the latest Ultralytics YOLO26 and other leading architectures like YOLO11, YOLOv10, RT-DETR, and EfficientDet.

Whether your application demands the millisecond latency of edge AI or the high precision required for medical imaging, this guide provides the data you need to make an informed choice. Models are evaluated on mean Average Precision (mAP), inference speed, parameter efficiency, and ease of deployment.

Interactive Performance Benchmarks#

Visualizing the relationship between speed and accuracy is essential for identifying the "Pareto frontier" of object detection—models that offer the best accuracy for a given speed constraint. The chart below contrasts key metrics on standard datasets like COCO.

This chart visualizes key performance metrics so you can quickly assess the trade-offs between different models. Understanding these metrics is fundamental to selecting a model that fits your deployment constraints.

Quick Decision Guide#

Not sure where to start? Use this decision tree to narrow down the architecture that best fits your hardware and performance requirements.

The Current Landscape: YOLO26 and Beyond#

Object detection moves rapidly. While older models remain relevant for existing deployments, new architectures keep improving speed and accuracy.

Ultralytics YOLO26#

Released in January 2026, YOLO26 is the latest Ultralytics model and the recommended starting point for all new projects. It introduces architectural innovations including an optional End-to-End NMS-Free Design (nms=False) that eliminates the need for Non-Maximum Suppression (NMS) post-processing, resulting in faster and more predictable inference times. YOLO26n is up to 43% faster on CPUs than YOLO11n, making YOLO26 well suited to edge deployment.

Key innovations include:

  • NMS-Free End-to-End: Simplified deployment with no NMS post-processing required (nms=False)
  • DFL Removal: Streamlined exports to ONNX, TensorRT, and CoreML
  • MuSGD Optimizer: Hybrid SGD/Muon optimizer inspired by LLM training for stable convergence
  • ProgLoss + STAL: Enhanced small object detection performance
Why Choose YOLO26?

YOLO26 combines CNN efficiency with transformer-like end-to-end capabilities. It supports all Ultralytics tasks (detection, instance and semantic segmentation, depth estimation, classification, pose estimation, and OBB) while being smaller, faster, and easier to deploy than previous generations.

Ultralytics YOLO11#

YOLO11 remains a highly capable model: YOLO11m uses 22% fewer parameters than YOLOv8m while achieving higher detection accuracy. It is fully supported and recommended for users who need proven stability or have existing YOLO11 pipelines.

Community Models: A Note on YOLO12 and YOLO13#

You may encounter references to YOLO12 or YOLO13 in community discussions or repositories.

Production Caution

We currently do not recommend YOLO12 or YOLO13 for production use.

  • YOLO12: Utilizes attention layers that often cause training instability, excessive memory consumption, and significantly slower CPU inference speeds.
  • YOLO13: Benchmarks indicate only marginal accuracy gains over YOLO11 while being larger and slower. Reported results have shown issues with reproducibility.


Watch: YOLO Models Comparison: Ultralytics YOLO11 vs. YOLOv10 vs. YOLOv9 vs. Ultralytics YOLOv8

Detailed Model Comparisons#

Explore the technical comparisons below to understand specific architectural differences, such as backbone selection, head design, and loss functions. They are organized by model:

YOLO26 vs#

YOLO26 is the latest Ultralytics model, featuring NMS-free end-to-end detection, the MuSGD optimizer, and up to 43% faster CPU inference for YOLO26n versus YOLO11n. It is optimized for edge deployment while achieving high accuracy.

YOLO11 vs#

YOLO11 builds on the success of its predecessors. It features an improved backbone and neck architecture for better feature extraction and efficiency.

YOLOv10 vs#

Developed by Tsinghua University, YOLOv10 focuses on removing the NMS step to reduce latency variance, offering strong performance with reduced computational overhead.

YOLOv9 vs#

YOLOv9 introduces Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN) to address information loss in deep neural networks.

YOLOv8 vs#

Ultralytics YOLOv8 remains a highly popular choice. Its architecture combines an advanced backbone and neck with an anchor-free split head for strong accuracy-speed trade-offs.

YOLOv7 vs#

YOLOv7 introduced "trainable bag-of-freebies" and model re-parameterization, focusing on optimizing the training process without increasing inference costs.

YOLOv6 vs#

Meituan's YOLOv6 is designed for industrial applications, featuring Bi-directional Concatenation (BiC) modules and anchor-aided training strategies.

YOLOv5 vs#

Ultralytics YOLOv5 is known for its ease of use, stability, and speed. It remains a robust choice for projects requiring broad device compatibility.

RT-DETR vs#

RT-DETR (Real-Time Detection Transformer) combines a CNN backbone with a transformer encoder-decoder to achieve high accuracy with real-time performance, excelling in global context understanding.

PP-YOLOE+ vs#

PP-YOLOE+, developed by Baidu, uses Task Alignment Learning (TAL) and a decoupled head to balance efficiency and accuracy.

DAMO-YOLO vs#

From Alibaba Group, DAMO-YOLO employs Neural Architecture Search (NAS) and an efficient RepGFPN neck to balance latency and accuracy.

YOLOX vs#

YOLOX, developed by Megvii, is an anchor-free evolution known for its decoupled head and SimOTA label assignment strategy.

EfficientDet vs#

EfficientDet by Google Brain uses compound scaling and BiFPN to optimize parameter efficiency, offering a spectrum of models (D0-D7) for different constraints.

This index is updated as new models are released and benchmarks are refined. For enterprise deployments that require private licensing, see the Ultralytics Licensing page.

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GLglenn-jocher18PDpderrenger1RIRizwanMunawar1

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