Model comparison · YOLO26 vs YOLO11
YOLO26 vs YOLO11
A size-tier-by-size-tier comparison of Ultralytics YOLO26 and Ultralytics YOLO11: published COCO accuracy, parameter counts, licensing and which one to reach for. Every figure is the model authors' own, linked to its source.
mAP comparison
YOLO26
- Nano 40.9
- Small 48.6
- Medium 53.1
- Large 55.0
- Extra large 57.5
YOLO11
- Nano 39.5
- Small 47.0
- Medium 51.5
- Large 53.4
- Extra large 54.7
Accuracy and size, tier by tier
Each row pairs the equivalent rung of both size ladders, so the numbers sit side by side at comparable capacity. Every figure is the model authors’ own.
- COCO mAP50-95 @ 640px
- mAP: higher is better
- Params (M): lower is better
| Tier | YOLO26 | YOLO11 | Δ mAP | ||||
|---|---|---|---|---|---|---|---|
| Model | mAP | Params | Model | mAP | Params | ||
| Nano | YOLO26n | 40.9 | 2.4M | YOLO11n | 39.5 | 2.6M | +1.4 |
| Small | YOLO26s | 48.6 | 9.5M | YOLO11s | 47.0 | 9.4M | +1.6 |
| Medium | YOLO26m | 53.1 | 20.4M | YOLO11m | 51.5 | 20.1M | +1.6 |
| Large | YOLO26l | 55.0 | 24.8M | YOLO11l | 53.4 | 25.3M | +1.6 |
| Extra large | YOLO26x | 57.5 | 55.7M | YOLO11x | 54.7 | 56.9M | +2.8 |
Δ mAP is YOLO26 minus YOLO11: positive means YOLO26 is ahead at that tier. Sources: YOLO26 docs · YOLO11 docs
Additional information
YOLO26 posts the higher published COCO mAP at all 5 matched size tiers, averaging 1.80 mAP ahead of YOLO11. The gap is widest at the extra large tier, where YOLO26 leads by 2.8 mAP. At the top of each range, YOLO26 reaches 57.5 mAP against 54.7.
56% fewer parameters, same accuracy. YOLO26l reaches 55.0 mAP from 24.8M parameters, matching or beating YOLO11x at 54.7 mAP from 56.9M.
Parameter count is not latency. It is a good proxy for download size and memory, but throughput depends on your hardware, batch size and export format, so measure on your own target.
YOLO26
NMS-free, end-to-end, and tuned for the edge.
- Released
- 2026 · Ultralytics
- License
- AGPL-3.0
- Framework
- Ultralytics (PyTorch)
- Tasks
- Detect, Segment, Semantic segmentation, Depth, Classify, Pose, OBB, Open-vocabulary
A single-stage detector that outputs final boxes directly, with no separate NMS cleanup step, so it runs faster on CPUs and edge devices while keeping top accuracy.
Download YOLO26 weights →YOLO11
The best-supported all-round default across five tasks.
- Released
- 2024 · Ultralytics
- License
- AGPL-3.0
- Framework
- Ultralytics (PyTorch)
- Tasks
- Detect, Segment, Classify, Pose, OBB
An anchor-free, single-pass detector that predicts objects in one shot across five vision tasks, giving a strong balance of speed and accuracy for everyday production use.
Download YOLO11 weights →YOLO26 and YOLO11 against the whole lineage
YOLO26 and YOLO11 against every other detector here, on one shared scale. The rest are dimmed; hover any row to bring it back.
| Model | Released | mAP range | Params range | Licence |
|---|---|---|---|---|
| YOLO26 | 2026 | 40.9 to 57.5 | 2.4M to 55.7M | AGPL-3.0 |
| YOLO12 | 2025 | 40.6 to 55.2 | 2.6M to 59.1M | AGPL-3.0 |
| YOLO11 | 2024 | 39.5 to 54.7 | 2.6M to 56.9M | AGPL-3.0 |
| YOLOv10 | 2024 | 38.5 to 54.4 | 2.3M to 29.5M | AGPL-3.0 |
| YOLOv9 | 2024 | 38.3 to 55.6 | 2.0M to 58.1M | GPL-3.0 |
| YOLOv8 | 2023 | 37.3 to 53.9 | 3.2M to 68.2M | AGPL-3.0 |
| YOLOv5 (v5u) | 2020 | 34.3 to 53.2 | 2.6M to 97.2M | AGPL-3.0 |
Every matchup
Looking for the weights themselves? Download any of these checkpoints from the model-weights hub, or see how the trackers behind them compare in the YOLO tracker benchmarks.