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
35.541.347.052.858.5mAPNanoSmallMediumLargeExtra largeModel size tierYOLO11n: 39.5 mAP, 2.6M paramsYOLO11s: 47.0 mAP, 9.4M paramsYOLO11m: 51.5 mAP, 20.1M paramsYOLO11l: 53.4 mAP, 25.3M paramsYOLO11x: 54.7 mAP, 56.9M paramsYOLOv9t: 38.3 mAP, 2M paramsYOLOv9s: 46.8 mAP, 7.2M paramsYOLOv9m: 51.4 mAP, 20.1M paramsYOLOv9c: 53.0 mAP, 25.5M paramsYOLOv9e: 55.6 mAP, 58.1M params

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.
TierYOLO11YOLOv9Δ mAP
ModelmAPParamsModelmAPParams
NanoYOLO11n39.52.6MYOLOv9t38.32.0M+1.2
SmallYOLO11s47.09.4MYOLOv9s46.87.2M+0.2
MediumYOLO11m51.520.1MYOLOv9m51.420.1M+0.1
LargeYOLO11l53.425.3MYOLOv9c53.025.5M+0.4
Extra largeYOLO11x54.756.9MYOLOv9e55.658.1M-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.
ModelReleasedmAP rangeParams rangeLicence
YOLO26202640.9 to 57.52.4M to 55.7MAGPL-3.0
YOLO12202540.6 to 55.22.6M to 59.1MAGPL-3.0
YOLO11202439.5 to 54.72.6M to 56.9MAGPL-3.0
YOLOv10202438.5 to 54.42.3M to 29.5MAGPL-3.0
YOLOv9202438.3 to 55.62.0M to 58.1MGPL-3.0
YOLOv8202337.3 to 53.93.2M to 68.2MAGPL-3.0
YOLOv5 (v5u)202034.3 to 53.22.6M to 97.2MAGPL-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.

Every matchup

YOLO26 vs YOLO12YOLO26 vs YOLO11YOLO26 vs YOLOv10YOLO26 vs YOLOv9YOLO26 vs YOLOv8YOLO26 vs YOLOv5 (v5u)YOLO12 vs YOLO11YOLO12 vs YOLOv10YOLO12 vs YOLOv9YOLO12 vs YOLOv8YOLO12 vs YOLOv5 (v5u)YOLO11 vs YOLOv10YOLO11 vs YOLOv9YOLO11 vs YOLOv8YOLO11 vs YOLOv5 (v5u)YOLOv10 vs YOLOv9YOLOv10 vs YOLOv8YOLOv10 vs YOLOv5 (v5u)YOLOv9 vs YOLOv8YOLOv9 vs YOLOv5 (v5u)YOLOv8 vs YOLOv5 (v5u)

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.