Is YOLOv8 better than YOLOv5 (v5u)?

On published COCO accuracy, YOLOv8. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 1.5 mAP. The gap is widest at the nano tier, where YOLOv8 is ahead by 3.0 mAP. YOLOv8 tops out at 53.9 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, YOLOv8 or YOLOv5 (v5u)?

YOLOv8 gets more out of each parameter. YOLOv8x reaches 53.9 mAP on 68.2M parameters, matching or beating YOLOv5xu at 97.2M: about 30% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.

Is YOLOv8 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 YOLOv8 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 · YOLOv8 vs YOLOv5 (v5u)

YOLOv8 vs YOLOv5 (v5u)

A size-tier-by-size-tier comparison of Ultralytics YOLOv8 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.

CompareYOLOv8

AgainstYOLOv5 (v5u)

30%fewer params

mAP comparison

YOLOv8YOLOv5 (v5u)

YOLOv8

Nano 37.3
Small 44.9
Medium 50.2
Large 52.9
Extra large 53.9

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 57.0 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

YOLOv8 and YOLOv5 (v5u) compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
Tier	YOLOv8			YOLOv5 (v5u)			Δ mAP
Tier	Model	mAP	Params	Model	mAP	Params	Δ mAP
Nano	YOLOv8n	37.3	3.2M	YOLOv5nu	34.3	2.6M	+3.0
Small	YOLOv8s	44.9	11.2M	YOLOv5su	43.0	9.1M	+1.9
Medium	YOLOv8m	50.2	25.9M	YOLOv5mu	49.0	25.1M	+1.2
Large	YOLOv8l	52.9	43.7M	YOLOv5lu	52.2	53.2M	+0.7
Extra large	YOLOv8x	53.9	68.2M	YOLOv5xu	53.2	97.2M	+0.7

Δ mAP is YOLOv8 minus YOLOv5 (v5u): positive means YOLOv8 is ahead at that tier. Sources: YOLOv8 docs · YOLOv5 (v5u) docs

Additional information

YOLOv8 posts the higher published COCO mAP at all 5 matched size tiers, averaging 1.50 mAP ahead of YOLOv5 (v5u). The gap is widest at the nano tier, where YOLOv8 leads by 3.0 mAP. At the top of each range, YOLOv8 reaches 53.9 mAP against 53.2.
30% fewer parameters, same accuracy. YOLOv8x reaches 53.9 mAP from 68.2M 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.

YOLOv8

The workhorse that made anchor-free multi-task YOLO mainstream.

Released: 2023 · Ultralytics
License: AGPL-3.0
Framework: Ultralytics (PyTorch)
Tasks: Detect, Segment, Classify, Pose, OBB

The anchor-free model that made multi-task YOLO mainstream: one architecture for detection, segmentation, pose, classification and OBB, with the largest ecosystem.

Download YOLOv8 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 →

YOLOv8 and YOLOv5 (v5u) against the whole lineage

YOLOv8 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 YOLOv8 better than YOLOv5 (v5u)?: On published COCO accuracy, YOLOv8. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 1.5 mAP. The gap is widest at the nano tier, where YOLOv8 is ahead by 3.0 mAP. YOLOv8 tops out at 53.9 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 YOLOv8 and YOLOv5 (v5u)?: YOLOv8 (Ultralytics, 2023) and YOLOv5 (v5u) (Ultralytics, 2020) are 3 years apart. The workhorse that made anchor-free multi-task YOLO mainstream. The classic that made YOLO easy to ship, retrained anchor-free. Task coverage differs too: YOLOv8 supports 5 (Detect, Segment, Classify, Pose, OBB) against YOLOv5 (v5u)'s 3 (Detect, Segment, Classify).
Which is smaller, YOLOv8 or YOLOv5 (v5u)?: YOLOv8 gets more out of each parameter. YOLOv8x reaches 53.9 mAP on 68.2M parameters, matching or beating YOLOv5xu at 97.2M: about 30% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.
Is YOLOv8 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 YOLOv8 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.