Model comparison · YOLO26 vs YOLOv8
YOLO26 vs YOLOv8
Use YOLOv8 when you need to keep an existing YOLOv8 pipeline; consider YOLO26 for a new project that benefits from NMS-free exports and higher published COCO accuracy. Compare the size tiers below.
mAP comparison
YOLO26
- Nano 40.9
- Small 48.6
- Medium 53.1
- Large 55.0
- Extra large 57.5
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 | YOLO26 | YOLOv8 | Δ mAP | ||||
|---|---|---|---|---|---|---|---|
| Model | mAP | Params | Model | mAP | Params | ||
| Nano | YOLO26n | 40.9 | 2.4M | YOLOv8n | 37.3 | 3.2M | +3.6 |
| Small | YOLO26s | 48.6 | 9.5M | YOLOv8s | 44.9 | 11.2M | +3.7 |
| Medium | YOLO26m | 53.1 | 20.4M | YOLOv8m | 50.2 | 25.9M | +2.9 |
| Large | YOLO26l | 55.0 | 24.8M | YOLOv8l | 52.9 | 43.7M | +2.1 |
| Extra large | YOLO26x | 57.5 | 55.7M | YOLOv8x | 53.9 | 68.2M | +3.6 |
Δ mAP is YOLO26 minus YOLOv8: positive means YOLO26 is ahead at that tier. Sources: YOLO26 docs · YOLOv8 docs
Additional information
YOLO26 posts the higher published COCO mAP at all 5 matched size tiers, averaging 3.18 mAP ahead of YOLOv8. The gap is widest at the small tier, where YOLO26 leads by 3.7 mAP. At the top of each range, YOLO26 reaches 57.5 mAP against 53.9.
64% fewer parameters, same accuracy. YOLO26l reaches 55.0 mAP from 24.8M parameters, matching or beating YOLOv8x at 53.9 mAP from 68.2M.
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 →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 →YOLO26 and YOLOv8 against the whole lineage
YOLO26 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 |
Frequently asked questions
- Is Ultralytics YOLO26 better than Ultralytics YOLOv8?
- On published COCO accuracy, YOLO26. It leads 5 of the 5 size tiers where both families publish a figure, by an average of 3.2 mAP. The gap is widest at the small tier, where YOLO26 is ahead by 3.7 mAP. Ultralytics YOLO26 tops out at 57.5 mAP and Ultralytics YOLOv8 at 53.9. "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 Ultralytics YOLO26 and Ultralytics YOLOv8?
- Ultralytics YOLO26 (Ultralytics, 2026) and Ultralytics YOLOv8 (Ultralytics, 2023) are 3 years apart. NMS-free, end-to-end, and tuned for the edge. The workhorse that made anchor-free multi-task YOLO mainstream. Task coverage differs too: Ultralytics YOLO26 supports 8 (Detect, Segment, Semantic segmentation, Depth, Classify, Pose, OBB, Open-vocabulary) against Ultralytics YOLOv8's 5 (Detect, Segment, Classify, Pose, OBB).
- Which is smaller, Ultralytics YOLO26 or Ultralytics YOLOv8?
- YOLO26 gets more out of each parameter. YOLO26l reaches 55.0 mAP on 24.8M parameters, matching or beating YOLOv8x at 68.2M: about 64% fewer parameters for the same accuracy. That is the comparison worth making, since it is the same result from a smaller checkpoint.
- Is Ultralytics YOLO26 faster than Ultralytics YOLOv8?
- 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 Ultralytics YOLO26 and Ultralytics YOLOv8 free for commercial use?
- Both ship under AGPL-3.0, so the licence is not a tiebreaker here: choose on accuracy and size. AGPL-3.0 is free for open-source, research and internal use, but a closed-source product or paid service needs an Ultralytics Enterprise licence. 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.