Model comparison · YOLO12 vs YOLOv8
YOLO12 vs YOLOv8
A size-tier-by-size-tier comparison of YOLO12 and Ultralytics YOLOv8: 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
YOLO12
- Nano 40.6
- Small 48.0
- Medium 52.5
- Large 53.7
- Extra large 55.2
YOLOv8
- Nano 37.3
- Small 44.9
- Medium 50.2
- Large 52.9
- Extra large 53.9
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 | YOLO12 | YOLOv8 | Δ mAP | ||||
|---|---|---|---|---|---|---|---|
| Model | mAP | Params | Model | mAP | Params | ||
| Nano | YOLO12n | 40.6 | 2.6M | YOLOv8n | 37.3 | 3.2M | +3.3 |
| Small | YOLO12s | 48.0 | 9.3M | YOLOv8s | 44.9 | 11.2M | +3.1 |
| Medium | YOLO12m | 52.5 | 20.2M | YOLOv8m | 50.2 | 25.9M | +2.3 |
| Large | YOLO12l | 53.7 | 26.4M | YOLOv8l | 52.9 | 43.7M | +0.8 |
| Extra large | YOLO12x | 55.2 | 59.1M | YOLOv8x | 53.9 | 68.2M | +1.3 |
Δ mAP is YOLO12 minus YOLOv8: positive means YOLO12 is ahead at that tier. Sources: YOLO12 docs · YOLO12 paper · YOLOv8 docs
Additional information
YOLO12 posts the higher published COCO mAP at all 5 matched size tiers, averaging 2.16 mAP ahead of YOLOv8. The gap is widest at the nano tier, where YOLO12 leads by 3.3 mAP. At the top of each range, YOLO12 reaches 55.2 mAP against 53.9.
40% fewer parameters, same accuracy. YOLO12l reaches 53.7 mAP from 26.4M parameters, matching or beating YOLOv8l at 52.9 mAP from 43.7M.
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 →YOLOv8
The workhorse that made anchor-free multi-task YOLO mainstream.
- Released
- 2023 · Ultralytics
- License
- AGPL-3.0
- Framework
- Ultralytics (PyTorch)
- Tasks
- Detect, Segment, Classify, Pose, OBB
The anchor-free model that made multi-task YOLO mainstream: one architecture for detection, segmentation, pose, classification and OBB, with the largest ecosystem.
Download YOLOv8 weights →YOLO12 and YOLOv8 against the whole lineage
YOLO12 and YOLOv8 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 |
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.