# Release Notes
## SAM 3.1 — March 27, 2026
SAM 3.1 introduces **Object Multiplex**, a shared-memory approach for joint multi-object tracking that is significantly faster without sacrificing accuracy. This release also includes new model checkpoints and optimized inference.
### Object Multiplex
SAM 3's video pipeline processes each tracked object independently, which scales linearly with the number of objects. Object Multiplex groups objects into fixed-capacity buckets and processes them jointly, drastically reducing redundant computation. For technical details, see Appendix H (Object Multiplex) in the [SAM 3 paper](https://arxiv.org/abs/2511.16719).
#### Key Improvements
- **~7x speedup** at 128 objects on a single H100 GPU compared to the SAM 3 November 2025 release
- Inference optimizations that significantly improve multi-object tracking efficiency:
- Reduced CPU-GPU synchronization in detection-tracker association and other heuristics
- Enhanced `torch.compile` support with improved operation fusion
- Batched postprocessing and vision encoder to increase GPU utilization
- Mixed results on SA-Co/VEval video benchmarks, with notable improvement on YT-Temporal-1B (+2.1 cgF1)
- Improved VOS performance on 6 out of 7 benchmarks, including +2.0 on the challenging MOSEv2
#### Inference Efficiency
#### Video PCS with Text Prompt
| Model |
SA-Co/VEval benchmark test split |
Public benchmarks |
| SA-V |
YT-Temporal-1B |
SmartGlasses |
LVVIS |
BURST |
YTVIS21 |
OVIS |
| cgF1 |
pHOTA |
cgF1 |
pHOTA |
cgF1 |
pHOTA |
test mAP |
test HOTA |
val mAP |
val mAP |
| SAM 3 |
30.3 |
58.0 |
50.8 |
69.9 |
36.4 |
63.6 |
36.3 |
44.5 |
57.4 |
60.5 |
| SAM 3.1 |
30.5 |
58.7 |
52.9 |
70.7 |
36.3 |
64.4 |
34.3 |
43.3 |
56.6 |
61.5 |
#### Video Object Segmentation (VOS)
| Model |
J&F |
G |
J&Ḟ |
| MOSEv1 val |
DAVIS17 val |
LVOSv2 val |
SA-V val |
SA-V test |
YTVOS19 val |
MOSEv2 val |
| SAM 3 |
78.4 |
92.2 |
88.5 |
83.5 |
84.4 |
89.7 |
60.3 |
| SAM 3.1 |
79.6 |
92.7 |
89.2 |
83.8 |
85.1 |
89.3 |
62.3 |
### New Checkpoints
The SAM 3.1 checkpoints are available on the [Hugging Face repo](https://huggingface.co/facebook/sam3.1). See [Getting Started](README.md#getting-started) for download and authentication instructions.
### Notebooks
- [`sam3.1_video_predictor_example.ipynb`](examples/sam3.1_video_predictor_example.ipynb): Demonstrates how to use SAM 3.1 with Object Multiplex for video segmentation and dense tracking with text and point prompts.
### Contributors
[Arpit Kalla](https://github.com/arpitkalla), [Chaitanya Ryali](https://scholar.google.com/citations?user=4LWx24UAAAAJ&hl=en), [Christian Puhrsch](https://github.com/cpuhrsch), [Ho Kei Cheng](https://hkchengrex.com/), [Joseph Greer](https://scholar.google.com/citations?user=guL96CkAAAAJ&hl=en), [Meng Wang](https://github.com/mengwa41), [Miran Heo](https://sites.google.com/view/miranheo), [Pengchuan Zhang](https://pzzhang.github.io/pzzhang/), [Roman Rädle](https://scholar.google.com/citations?user=Tpt57v0AAAAJ&hl=en), [Yuan-Ting Hu](https://scholar.google.com/citations?user=E8DVVYQAAAAJ&hl=en)