Is YOLO12 better than YOLO11?

On published COCO accuracy, YOLO12. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 0.8 mAP. The gap is widest at the nano tier, where YOLO12 is ahead by 1.1 mAP. YOLO12 tops out at 55.2 mAP and YOLO11 at 54.7. "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, YOLO12 or YOLO11?

Compare them tier by tier rather than family to family, since both ship a full ladder from nano to extra large and the size you pick matters far more than the generation. The parameter columns in the table above give the figure at every tier.

Is YOLO12 faster than YOLO11?

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 YOLO12 and YOLO11 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 · YOLO12 vs YOLO11

YOLO12 vs YOLO11

A size-tier-by-size-tier comparison of YOLO12 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.

CompareYOLO12

AgainstYOLO11

mAP comparison

YOLO12YOLO11

YOLO12

Nano 40.6
Small 48.0
Medium 52.5
Large 53.7
Extra large 55.2

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 37.0 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

YOLO12 and YOLO11 compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
Tier	YOLO12			YOLO11			Δ mAP
Tier	Model	mAP	Params	Model	mAP	Params	Δ mAP
Nano	YOLO12n	40.6	2.6M	YOLO11n	39.5	2.6M	+1.1
Small	YOLO12s	48.0	9.3M	YOLO11s	47.0	9.4M	+1.0
Medium	YOLO12m	52.5	20.2M	YOLO11m	51.5	20.1M	+1.0
Large	YOLO12l	53.7	26.4M	YOLO11l	53.4	25.3M	+0.3
Extra large	YOLO12x	55.2	59.1M	YOLO11x	54.7	56.9M	+0.5

Δ mAP is YOLO12 minus YOLO11: positive means YOLO12 is ahead at that tier. Sources: YOLO12 docs · YOLO12 paper · YOLO11 docs

Additional information

YOLO12 posts the higher published COCO mAP at all 5 matched size tiers, averaging 0.78 mAP ahead of YOLO11. The gap is widest at the nano tier, where YOLO12 leads by 1.1 mAP. At the top of each range, YOLO12 reaches 55.2 mAP against 54.7.
Same size at every tier. YOLO12 and YOLO11 ship near-identical parameter counts rung for rung, never more than 4% apart, so neither buys accuracy with weights the other does not spend. The choice here is ecosystem and licence, not size.
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.

YOLO12

The attention-centric generation, between YOLO11 and YOLO26.

Released: 2025 · Tian, Ye & Doermann (UB / UCAS)
License: AGPL-3.0
Framework: Ultralytics (PyTorch)
Tasks: Detect, Segment, Classify, Pose, OBB

An attention-centric YOLO: area attention and residual ELAN blocks replace some of the pure-convolution stack, buying about a point of mAP over YOLO11 at a similar size.

Download YOLO12 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 →

YOLO12 and YOLO11 against the whole lineage

YOLO12 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.
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 YOLO12 better than YOLO11?: On published COCO accuracy, YOLO12. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 0.8 mAP. The gap is widest at the nano tier, where YOLO12 is ahead by 1.1 mAP. YOLO12 tops out at 55.2 mAP and YOLO11 at 54.7. "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 YOLO12 and YOLO11?: YOLO12 (Tian, Ye & Doermann (UB / UCAS), 2025) and YOLO11 (Ultralytics, 2024) are 1 year apart. The attention-centric generation, between YOLO11 and YOLO26. The best-supported all-round default across five tasks. Both cover 5 tasks.
Which is smaller, YOLO12 or YOLO11?: Compare them tier by tier rather than family to family, since both ship a full ladder from nano to extra large and the size you pick matters far more than the generation. The parameter columns in the table above give the figure at every tier.
Is YOLO12 faster than YOLO11?: 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 YOLO12 and YOLO11 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.