Model comparison · YOLOv10 vs YOLOv9
YOLOv10 vs YOLOv9
A size-tier-by-size-tier comparison of YOLOv10 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.
mAP comparison
YOLOv10
- Nano 38.5
- Small 46.3
- Medium 51.1
- Large 53.2
- Extra large 54.4
YOLOv9
- Nano 38.3
- Small 46.8
- Medium 51.4
- Large 53.0
- Extra large 55.6
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
| Tier | YOLOv10 | YOLOv9 | Δ mAP | ||||
|---|---|---|---|---|---|---|---|
| Model | mAP | Params | Model | mAP | Params | ||
| Nano | YOLOv10n | 38.5 | 2.3M | YOLOv9t | 38.3 | 2.0M | +0.2 |
| Small | YOLOv10s | 46.3 | 7.2M | YOLOv9s | 46.8 | 7.2M | -0.5 |
| Medium | YOLOv10m | 51.1 | 15.4M | YOLOv9m | 51.4 | 20.1M | -0.3 |
| Large | YOLOv10l | 53.2 | 24.4M | YOLOv9c | 53.0 | 25.5M | +0.2 |
| Extra large | YOLOv10x | 54.4 | 29.5M | YOLOv9e | 55.6 | 58.1M | -1.2 |
Δ mAP is YOLOv10 minus YOLOv9: positive means YOLOv10 is ahead at that tier. Sources: YOLOv10 docs · YOLOv10 paper · YOLOv9 docs · YOLOv9 paper
YOLOv10 also ships YOLOv10b, which sits between the rungs above and has no direct counterpart in YOLOv9, so it is left out of the paired rows rather than matched to an unequal one.
Additional information
YOLOv9 posts the higher published COCO mAP at 3 of the 5 matched size tiers, averaging 0.32 mAP ahead of YOLOv10. The gap is widest at the extra large tier, where YOLOv9 leads by 1.2 mAP. At the top of each range, YOLOv9 reaches 55.6 mAP against 54.4.
49% fewer parameters at the extra large tier. YOLOv10x reaches 54.4 mAP from 29.5M parameters against YOLOv9e at 55.6 mAP from 58.1M, 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.
YOLOv10
The first popular NMS-free, end-to-end YOLO.
- Released
- 2024 · Tsinghua University (THU-MIG)
- License
- AGPL-3.0
- Framework
- Built on Ultralytics (PyTorch)
- Tasks
- Detect
The first widely used YOLO that skips the NMS post-processing step, using paired training heads so inference is truly end to end and low latency.
Download YOLOv10 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 →YOLOv10 and YOLOv9 against the whole lineage
YOLOv10 and YOLOv9 against every other detector here, on one shared scale. The rest are dimmed; hover any row to bring it back.
| 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.
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.