Is YOLO11 better than YOLOv5 (v5u)?

On published COCO accuracy, YOLO11. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 2.9 mAP. The gap is widest at the nano tier, where YOLO11 is ahead by 5.2 mAP. YOLO11 tops out at 54.7 mAP and YOLOv5 (v5u) at 53.2. "Better" still depends on your constraint, though: accuracy is one axis and parameter count, task coverage and licence are the others, all of which are in the table above.

Which is smaller, YOLO11 or YOLOv5 (v5u)?

YOLO11 gets more out of each parameter. YOLO11l reaches 53.4 mAP on 25.3M parameters, matching or beating YOLOv5xu at 97.2M: about 74% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.

Is YOLO11 faster than YOLOv5 (v5u)?

Nothing on this page is a speed measurement, and it would be dishonest to imply one: every figure here is published COCO accuracy and parameter count. Parameters are a rough guide to inference cost within one family, but they do not transfer across architectures, and the post-processing step matters as much as the backbone. Export both to ONNX and measure on your own hardware and footage, which is the only comparison that predicts what you will actually get.

Are YOLO11 and YOLOv5 (v5u) free for commercial use?

Both ship under AGPL-3.0, so the licence is not a tiebreaker here: choose on accuracy and size. AGPL-3.0 is free for open-source, research and internal use, but a closed-source product or paid service needs an Ultralytics Enterprise licence. Read the licence text before you ship; this is a plain reading of it, not legal advice.

Model comparison · YOLO11 vs YOLOv5 (v5u)

YOLO11 vs YOLOv5 (v5u)

A size-tier-by-size-tier comparison of Ultralytics YOLO11 and Ultralytics YOLOv5 (v5u): published COCO accuracy, parameter counts, licensing and which one to reach for. Every figure is the model authors' own, linked to its source.

CompareYOLO11

AgainstYOLOv5 (v5u)

74%fewer params

mAP comparison

YOLO11YOLOv5 (v5u)

YOLO11

Nano 39.5
Small 47.0
Medium 51.5
Large 53.4
Extra large 54.7

YOLOv5 (v5u)

Nano 34.3
Small 43.0
Medium 49.0
Large 52.2
Extra large 53.2

COCO mAP at 640px, as published by each model’s authors. The y axis spans 31.0 to 58.0 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 YOLOv5 (v5u) compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
Tier	YOLO11			YOLOv5 (v5u)			Δ mAP
Tier	Model	mAP	Params	Model	mAP	Params	Δ mAP
Nano	YOLO11n	39.5	2.6M	YOLOv5nu	34.3	2.6M	+5.2
Small	YOLO11s	47.0	9.4M	YOLOv5su	43.0	9.1M	+4.0
Medium	YOLO11m	51.5	20.1M	YOLOv5mu	49.0	25.1M	+2.5
Large	YOLO11l	53.4	25.3M	YOLOv5lu	52.2	53.2M	+1.2
Extra large	YOLO11x	54.7	56.9M	YOLOv5xu	53.2	97.2M	+1.5

Δ mAP is YOLO11 minus YOLOv5 (v5u): positive means YOLO11 is ahead at that tier. Sources: YOLO11 docs · YOLOv5 (v5u) docs

Additional information

YOLO11 posts the higher published COCO mAP at all 5 matched size tiers, averaging 2.88 mAP ahead of YOLOv5 (v5u). The gap is widest at the nano tier, where YOLO11 leads by 5.2 mAP. At the top of each range, YOLO11 reaches 54.7 mAP against 53.2.
74% fewer parameters, same accuracy. YOLO11l reaches 53.4 mAP from 25.3M parameters, matching or beating YOLOv5xu at 53.2 mAP from 97.2M.
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 →

YOLOv5 (v5u)

The classic that made YOLO easy to ship, retrained anchor-free.

Released: 2020 · Ultralytics
License: AGPL-3.0
Framework: Ultralytics (PyTorch)
Tasks: Detect, Segment, Classify

The anchor-free v5u retrain of the classic PyTorch YOLO, keeping its easy training and export while adopting the newer YOLOv8 detection head for better accuracy.

Download YOLOv5 (v5u) weights →

YOLO11 and YOLOv5 (v5u) against the whole lineage

YOLO11 and YOLOv5 (v5u) 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

Frequently asked questions

Is YOLO11 better than YOLOv5 (v5u)?: On published COCO accuracy, YOLO11. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 2.9 mAP. The gap is widest at the nano tier, where YOLO11 is ahead by 5.2 mAP. YOLO11 tops out at 54.7 mAP and YOLOv5 (v5u) at 53.2. "Better" still depends on your constraint, though: accuracy is one axis and parameter count, task coverage and licence are the others, all of which are in the table above.
What is the difference between YOLO11 and YOLOv5 (v5u)?: YOLO11 (Ultralytics, 2024) and YOLOv5 (v5u) (Ultralytics, 2020) are 4 years apart. The best-supported all-round default across five tasks. The classic that made YOLO easy to ship, retrained anchor-free. Task coverage differs too: YOLO11 supports 5 (Detect, Segment, Classify, Pose, OBB) against YOLOv5 (v5u)'s 3 (Detect, Segment, Classify).
Which is smaller, YOLO11 or YOLOv5 (v5u)?: YOLO11 gets more out of each parameter. YOLO11l reaches 53.4 mAP on 25.3M parameters, matching or beating YOLOv5xu at 97.2M: about 74% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.
Is YOLO11 faster than YOLOv5 (v5u)?: Nothing on this page is a speed measurement, and it would be dishonest to imply one: every figure here is published COCO accuracy and parameter count. Parameters are a rough guide to inference cost within one family, but they do not transfer across architectures, and the post-processing step matters as much as the backbone. Export both to ONNX and measure on your own hardware and footage, which is the only comparison that predicts what you will actually get.
Are YOLO11 and YOLOv5 (v5u) free for commercial use?: Both ship under AGPL-3.0, so the licence is not a tiebreaker here: choose on accuracy and size. AGPL-3.0 is free for open-source, research and internal use, but a closed-source product or paid service needs an Ultralytics Enterprise licence. Read the licence text before you ship; this is a plain reading of it, not legal advice.

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.