Is YOLO26 better than YOLOv9?

On published COCO accuracy, YOLO26. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 2.0 mAP. The gap is widest at the nano tier, where YOLO26 is ahead by 2.6 mAP. YOLO26 tops out at 57.5 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 YOLO26 and YOLOv9?

YOLO26 (Ultralytics, 2026) and YOLOv9 (Academia Sinica (Wang et al.), 2024) are 2 years apart. NMS-free, end-to-end, and tuned for the edge. Fixes deep-network information loss with PGI and GELAN. Task coverage differs too: YOLO26 supports 8 (Detect, Segment, Semantic segmentation, Depth, Classify, Pose, OBB, Open-vocabulary) against YOLOv9's 2 (Detect, Segment).

Which is smaller, YOLO26 or YOLOv9?

YOLO26 gets more out of each parameter. YOLO26m reaches 53.1 mAP on 20.4M parameters, matching or beating YOLOv9c at 25.5M: about 20% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.

Is YOLO26 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 YOLO26 and YOLOv9 free for commercial use?

They differ: YOLO26 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 · YOLO26 vs YOLOv9

YOLO26 vs YOLOv9

A size-tier-by-size-tier comparison of Ultralytics YOLO26 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.

CompareYOLO26

AgainstYOLOv9

20%fewer params

mAP comparison

YOLO26YOLOv9

YOLO26

Nano 40.9
Small 48.6
Medium 53.1
Large 55.0
Extra large 57.5

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 35.0 to 60.5 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

YOLO26 and YOLOv9 compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
Tier	YOLO26			YOLOv9			Δ mAP
Tier	Model	mAP	Params	Model	mAP	Params	Δ mAP
Nano	YOLO26n	40.9	2.4M	YOLOv9t	38.3	2.0M	+2.6
Small	YOLO26s	48.6	9.5M	YOLOv9s	46.8	7.2M	+1.8
Medium	YOLO26m	53.1	20.4M	YOLOv9m	51.4	20.1M	+1.7
Large	YOLO26l	55.0	24.8M	YOLOv9c	53.0	25.5M	+2.0
Extra large	YOLO26x	57.5	55.7M	YOLOv9e	55.6	58.1M	+1.9

Δ mAP is YOLO26 minus YOLOv9: positive means YOLO26 is ahead at that tier. Sources: YOLO26 docs · YOLOv9 docs · YOLOv9 paper

Additional information

YOLO26 posts the higher published COCO mAP at all 5 matched size tiers, averaging 2.00 mAP ahead of YOLOv9. The gap is widest at the nano tier, where YOLO26 leads by 2.6 mAP. At the top of each range, YOLO26 reaches 57.5 mAP against 55.6.
20% fewer parameters, same accuracy. YOLO26m reaches 53.1 mAP from 20.4M parameters, matching or beating YOLOv9c at 53.0 mAP from 25.5M.
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.

YOLO26

NMS-free, end-to-end, and tuned for the edge.

Released: 2026 · Ultralytics
License: AGPL-3.0
Framework: Ultralytics (PyTorch)
Tasks: Detect, Segment, Semantic segmentation, Depth, Classify, Pose, OBB, Open-vocabulary

A single-stage detector that outputs final boxes directly, with no separate NMS cleanup step, so it runs faster on CPUs and edge devices while keeping top accuracy.

Download YOLO26 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 →

YOLO26 and YOLOv9 against the whole lineage

YOLO26 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 YOLO26 better than YOLOv9?: On published COCO accuracy, YOLO26. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 2.0 mAP. The gap is widest at the nano tier, where YOLO26 is ahead by 2.6 mAP. YOLO26 tops out at 57.5 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 YOLO26 and YOLOv9?: YOLO26 (Ultralytics, 2026) and YOLOv9 (Academia Sinica (Wang et al.), 2024) are 2 years apart. NMS-free, end-to-end, and tuned for the edge. Fixes deep-network information loss with PGI and GELAN. Task coverage differs too: YOLO26 supports 8 (Detect, Segment, Semantic segmentation, Depth, Classify, Pose, OBB, Open-vocabulary) against YOLOv9's 2 (Detect, Segment).
Which is smaller, YOLO26 or YOLOv9?: YOLO26 gets more out of each parameter. YOLO26m reaches 53.1 mAP on 20.4M parameters, matching or beating YOLOv9c at 25.5M: about 20% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.
Is YOLO26 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 YOLO26 and YOLOv9 free for commercial use?: They differ: YOLO26 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.