---
license: mit
datasets:
- OpenResearcher/OpenResearcher-Dataset
base_model:
- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16
---
Adopted by NVIDIA's Nemotron family of models!
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## Overview
**OpenResearcher** is a fully open agentic large language model (30B-A3B) designed for **long-horizon deep research** scenarios. It achieves an impressive **54.8%** accuracy on [BrowseComp-Plus](https://huggingface.co/spaces/Tevatron/BrowseComp-Plus), surpassing performance of `GPT-4.1`, `Claude-Opus-4`, `Gemini-2.5-Pro`, `DeepSeek-R1` and `Tongyi-DeepResearch`. It also demonstrates **leading performance** across a range of deep research benchmarks, including BrowseComp, GAIA, WebWalkerQA, and xbench-DeepSearch. We **fully open-source** the training and evaluation recipe—including data, model, training methodology, and evaluation framework for everyone to progress deep research.
## OpenResearcher-30B-A3B-GGUF
**Note: For the best performance, we recommend using [OpenResearcher-30B-A3B](https://huggingface.co/OpenResearcher/OpenResearcher-30B-A3B).**
To support efficient deployment, we release several quantized versions of [OpenResearcher-30B-A3B](https://huggingface.co/OpenResearcher/OpenResearcher-30B-A3B), including `Q4_K_M`, `Q5_0`, `Q5_K_M`, `Q6_K`, and `Q8_0`.
| Quantization | File Size | BPW | PPL | +/- | Tokens/sec |
|---|---|---|---|---|---|
| BF16 | 58.84 GiB | 16.00 | 8.4522 | 0.06489 | 4,117.90 |
| Q8_0 | 31.27 GiB | 8.51 | 8.4654 | 0.06499 | 7,490.81 |
| Q6_K | 31.20 GiB | 8.49 | 8.4784 | 0.06510 | 7,389.76 |
| Q5_0 | 20.37 GiB | 5.54 | 8.5462 | 0.06558 | 7,534.66 |
| Q4_K_M | 22.82 GiB | 6.21 | 8.5970 | 0.06610 | 7,046.96 |
| Q5_K_M | 24.24 GiB | 6.60 | 8.6074 | 0.06625 | 6,661.48 |
## Core Contributors
## Advisors
## Acknowledgements
## Citation
```bibtex
@article{li2026openresearcher,
title={{OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis}},
author={Li, Zhuofeng and Jiang, Dongfu and Ma, Xueguang and Zhang, Haoxiang and Nie, Ping and Zhang, Yuyu and Zou, Kai and Xie, Jianwen and Zhang, Yu and Chen, Wenhu},
journal={arXiv preprint arXiv:2603.20278},
year={2026}
}
```
