YOLO11 vs YOLOv12
Compare YOLO11 and YOLOv12 side-by-side.
Compare YOLO11 vs YOLOv12 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
YOLO11 vs YOLOv12 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | YOLO11 | YOLOv12 |
|---|---|---|
| Organization | Ultralytics | THU-MIG |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2024 | Feb 2025 |
| Context Window | — | — |
| Parameters | 2.6M-56.9M | 2.6M-59.1M |
| 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 | Not benchmarked |
| Smallest size | 38.6Nano · 2.6M params | Not benchmarked |
| mAP range | 38.6–53.6across 5 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 38.6640×640 · 2.6M params | Not available |
| Small | 45.5640×640 · 9.4M params | Not available |
| Medium | 50.5640×640 · 20.1M params | Not available |
| Large | 52.2640×640 · 25.3M params | Not available |
| XL | 53.6640×640 · 56.9M 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. The live demo runs some of these sizes at other resolutions, which were not benchmarked. | ||
| Vision Tasks | ||
| Instance Segmentation | Demo (COCO) | Supported |
| Object Detection | Demo (COCO) | Supported |
| Classification | Not listed | Supported |
| Pose Estimation | Not listed | Supported |
| Model Features | ||
| Real-Time Vision | Supported | Supported |
YOLO11 vs YOLOv12: 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.
YOLOv12 is an attention-centric real-time object detection model developed by researchers at Tsinghua University, with the arXiv paper published in February 2025 under the AGPL-3.0 license. It introduces an Area Attention module that partitions feature maps into regions and applies self-attention within each region, reducing the quadratic complexity of full self-attention while capturing long-range dependencies. It also incorporates R-ELAN for improved feature aggregation and scaled residual connections for training stability.
YOLOv12-L achieves 54.0% AP on COCO, while the YOLOv12-N variant achieves 40.5% mAP at 1.62ms latency on an NVIDIA T4 GPU. The model is built on the Ultralytics codebase, supporting detection, segmentation, and other standard YOLO tasks at competitive real-time speeds.
