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.

CompareYOLO26
AgainstYOLO11
56%fewer params

mAP comparison

YOLO26YOLO11
36.542.548.554.560.5mAPNanoSmallMediumLargeExtra largeModel size tierYOLO26n: 40.9 mAP, 2.4M paramsYOLO26s: 48.6 mAP, 9.5M paramsYOLO26m: 53.1 mAP, 20.4M paramsYOLO26l: 55.0 mAP, 24.8M paramsYOLO26x: 57.5 mAP, 55.7M paramsYOLO11n: 39.5 mAP, 2.6M paramsYOLO11s: 47.0 mAP, 9.4M paramsYOLO11m: 51.5 mAP, 20.1M paramsYOLO11l: 53.4 mAP, 25.3M paramsYOLO11x: 54.7 mAP, 56.9M params

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
COCO mAP at 640px, as published by each model’s authors. The y axis spans 36.5 to 60.5 rather than starting at zero, so small differences stay visible. Points are the published checkpoints; the joining lines are for reading order only.

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
YOLO26 and YOLO11 compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
TierYOLO26YOLO11Δ mAP
ModelmAPParamsModelmAPParams
NanoYOLO26n40.92.4MYOLO11n39.52.6M+1.4
SmallYOLO26s48.69.5MYOLO11s47.09.4M+1.6
MediumYOLO26m53.120.4MYOLO11m51.520.1M+1.6
LargeYOLO26l55.024.8MYOLO11l53.425.3M+1.6
Extra largeYOLO26x57.555.7MYOLO11x54.756.9M+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.

Every YOLO detector in this section, with release year, publisher, published COCO mAP50-95 range at 640px, parameter-count range and licence.
ModelReleasedmAP rangeParams rangeLicence
YOLO26202640.9 to 57.52.4M to 55.7MAGPL-3.0
YOLO12202540.6 to 55.22.6M to 59.1MAGPL-3.0
YOLO11202439.5 to 54.72.6M to 56.9MAGPL-3.0
YOLOv10202438.5 to 54.42.3M to 29.5MAGPL-3.0
YOLOv9202438.3 to 55.62.0M to 58.1MGPL-3.0
YOLOv8202337.3 to 53.93.2M to 68.2MAGPL-3.0
YOLOv5 (v5u)202034.3 to 53.22.6M to 97.2MAGPL-3.0