Is YOLO11 better than YOLOv9?

On published COCO accuracy, YOLO11. It leads 4 of the 5 size tiers where both families publish a figure, by an average of 0.2 mAP. The gap is widest at the nano tier, where YOLO11 is ahead by 1.2 mAP. YOLO11 tops out at 54.7 mAP and YOLOv9 at 55.6. "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 YOLOv9?

YOLO11 (Ultralytics, 2024) and YOLOv9 (Academia Sinica (Wang et al.), 2024) shipped the same year. The best-supported all-round default across five tasks. Fixes deep-network information loss with PGI and GELAN. Task coverage differs too: YOLO11 supports 5 (Detect, Segment, Classify, Pose, OBB) against YOLOv9's 2 (Detect, Segment).

Which is smaller, YOLO11 or YOLOv9?

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 YOLO11 faster than YOLOv9?

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 YOLOv9 free for commercial use?

They differ: YOLO11 is AGPL-3.0 and YOLOv9 is GPL-3.0. That is a real decision, not a footnote, because the obligations are not the same and one of them may rule a model out of a closed-source product entirely. Read the licence text before you ship; this is a plain reading of it, not legal advice.

Model comparison · YOLO11 vs YOLOv9

YOLO11 vs YOLOv9

A size-tier-by-size-tier comparison of Ultralytics YOLO11 and YOLOv9: published COCO accuracy, parameter counts, licensing and which one to reach for. Every figure is the model authors' own, linked to its source.

CompareYOLO11

AgainstYOLOv9

23%fewer params

mAP comparison

YOLO11YOLOv9

YOLO11

Nano 39.5
Small 47.0
Medium 51.5
Large 53.4
Extra large 54.7

YOLOv9

Nano 38.3
Small 46.8
Medium 51.4
Large 53.0
Extra large 55.6

COCO mAP at 640px, as published by each model’s authors. The y axis spans 35.5 to 58.5 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 YOLOv9 compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
Tier	YOLO11			YOLOv9			Δ mAP
Tier	Model	mAP	Params	Model	mAP	Params	Δ mAP
Nano	YOLO11n	39.5	2.6M	YOLOv9t	38.3	2.0M	+1.2
Small	YOLO11s	47.0	9.4M	YOLOv9s	46.8	7.2M	+0.2
Medium	YOLO11m	51.5	20.1M	YOLOv9m	51.4	20.1M	+0.1
Large	YOLO11l	53.4	25.3M	YOLOv9c	53.0	25.5M	+0.4
Extra large	YOLO11x	54.7	56.9M	YOLOv9e	55.6	58.1M	-0.9

Δ mAP is YOLO11 minus YOLOv9: positive means YOLO11 is ahead at that tier. Sources: YOLO11 docs · YOLOv9 docs · YOLOv9 paper

Additional information

YOLO11 posts the higher published COCO mAP at 4 of the 5 matched size tiers, averaging 0.20 mAP ahead of YOLOv9. The gap is widest at the nano tier, where YOLO11 leads by 1.2 mAP. At the top of each range, YOLOv9 reaches 55.6 mAP against 54.7.
23% fewer parameters at the nano tier. YOLOv9t reaches 38.3 mAP from 2.0M parameters against YOLO11n at 39.5 mAP from 2.6M, so the saving costs 1.2 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 →

YOLOv9

Fixes deep-network information loss with PGI and GELAN.

Released: 2024 · Academia Sinica (Wang et al.)
License: GPL-3.0
Framework: Original repo + Ultralytics (PyTorch)
Tasks: Detect, Segment

Adds Programmable Gradient Information and the GELAN backbone to preserve detail through deep layers, reaching high accuracy with very few parameters.

Download YOLOv9 weights →

YOLO11 and YOLOv9 against the whole lineage

YOLO11 and YOLOv9 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

Licenses here cover the weights. Running either model through the Ultralytics package brings AGPL-3.0 with it, and that can reach your own application regardless of how the checkpoint itself is licensed. Check the terms against your distribution plan before you ship.

Frequently asked questions

Is YOLO11 better than YOLOv9?: On published COCO accuracy, YOLO11. It leads 4 of the 5 size tiers where both families publish a figure, by an average of 0.2 mAP. The gap is widest at the nano tier, where YOLO11 is ahead by 1.2 mAP. YOLO11 tops out at 54.7 mAP and YOLOv9 at 55.6. "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 YOLOv9?: YOLO11 (Ultralytics, 2024) and YOLOv9 (Academia Sinica (Wang et al.), 2024) shipped the same year. The best-supported all-round default across five tasks. Fixes deep-network information loss with PGI and GELAN. Task coverage differs too: YOLO11 supports 5 (Detect, Segment, Classify, Pose, OBB) against YOLOv9's 2 (Detect, Segment).
Which is smaller, YOLO11 or YOLOv9?: 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 YOLO11 faster than YOLOv9?: 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 YOLOv9 free for commercial use?: They differ: YOLO11 is AGPL-3.0 and YOLOv9 is GPL-3.0. That is a real decision, not a footnote, because the obligations are not the same and one of them may rule a model out of a closed-source product entirely. 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.