Model comparison · YOLO11 vs YOLOv10

YOLO11 vs YOLOv10

A size-tier-by-size-tier comparison of Ultralytics YOLO11 and YOLOv10: published COCO accuracy, parameter counts, licensing and which one to reach for. Every figure is the model authors' own, linked to its source.

CompareYOLO11

AgainstYOLOv10

48%fewer params

mAP comparison

YOLO11YOLOv10

YOLO11

Nano 39.5
Small 47.0
Medium 51.5
Large 53.4
Extra large 54.7

YOLOv10

Nano 38.5
Small 46.3
Medium 51.1
Large 53.2
Extra large 54.4

COCO mAP at 640px, as published by each model’s authors. The y axis spans 36.0 to 57.5 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

YOLO11 and YOLOv10 compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
Tier	YOLO11			YOLOv10			Δ mAP
Tier	Model	mAP	Params	Model	mAP	Params	Δ mAP
Nano	YOLO11n	39.5	2.6M	YOLOv10n	38.5	2.3M	+1.0
Small	YOLO11s	47.0	9.4M	YOLOv10s	46.3	7.2M	+0.7
Medium	YOLO11m	51.5	20.1M	YOLOv10m	51.1	15.4M	+0.4
Large	YOLO11l	53.4	25.3M	YOLOv10l	53.2	24.4M	+0.2
Extra large	YOLO11x	54.7	56.9M	YOLOv10x	54.4	29.5M	+0.3

Δ mAP is YOLO11 minus YOLOv10: positive means YOLO11 is ahead at that tier. Sources: YOLO11 docs · YOLOv10 docs · YOLOv10 paper

YOLOv10 also ships YOLOv10b, which sits between the rungs above and has no direct counterpart in YOLO11, so it is left out of the paired rows rather than matched to an unequal one.

Additional information

YOLO11 posts the higher published COCO mAP at all 5 matched size tiers, averaging 0.52 mAP ahead of YOLOv10. The gap is widest at the nano tier, where YOLO11 leads by 1.0 mAP. At the top of each range, YOLO11 reaches 54.7 mAP against 54.4.
48% fewer parameters at the extra large tier. YOLOv10x reaches 54.4 mAP from 29.5M parameters against YOLO11x at 54.7 mAP from 56.9M, so the saving costs 0.3 mAP. No checkpoint from either family matches a larger one from the other outright.
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.

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 →

YOLOv10

The first popular NMS-free, end-to-end YOLO.

Released: 2024 · Tsinghua University (THU-MIG)
License: AGPL-3.0
Framework: Built on Ultralytics (PyTorch)
Tasks: Detect

The first widely used YOLO that skips the NMS post-processing step, using paired training heads so inference is truly end to end and low latency.

Download YOLOv10 weights →

YOLO11 and YOLOv10 against the whole lineage

YOLO11 and YOLOv10 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.
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.