[!NOTE] We have released a paper for OpenThoughts! See our paper here.
OpenThinker3-1.5B
State-of-the-art SFT-only 1.5B reasoning model. 🚀
This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the OpenThoughts3-1.2M dataset.
See our paper and blog post for more details.
Evaluation Results
The numbers reported in the table below are evaluated with our open-source tool Evalchemy. In the table below, we bold values in each column that are within 2 standard errors of the best.
| Model | AIME24 | AIME25 | AMC23 | MATH500 | HMMT O2/25 | LCB 06/24-01/25 | CodeElo | CodeForces | GPQA-D | JEEBench |
|---|---|---|---|---|---|---|---|---|---|---|
| OpenThinker3-1.5B | 52.0 | 41.7 | 87.0 | 86.4 | 27.3 | 39.4 | 12.9 | 15.5 | 29.5 | 51.9 |
| DeepSeek-R1-Distill-Qwen-1.5B | 32.3 | 23.7 | 71.8 | 80.8 | 15.3 | 27.2 | 8.8 | 8.5 | 31.1 | 32.5 |
| Nemotron-Research-Reasoning-Qwen-1.5B | 47.7 | 32.0 | 87.5 | 86.0 | 21.7 | 31.4 | 54.7 | 40.3 | 41.8 | 52.6 |
| Qwen3-1.7B | 52.0 | 35.3 | 83.8 | 87.2 | 23.3 | 27.7 | 20.7 | 20.0 | 49.3 | 60.7 |
| Qwen2.5-1.5B-Instruct | 3.0 | 0.7 | 30.8 | 50.2 | 0.0 | 5.5 | 0.8 | 2.2 | 24.7 | 16.4 |
Data
This model was trained on the OpenThoughts3-1.2M dataset.
The key to the strong model performance is our comprehensive data pipeline and over 1,000+ ablation experiments. This led to the creation of OpenThoughts3-1.2M, which consists of 850,000 math questions, 250,000 code questions, and 100,000 science questions. Reasoning traces are generated with QwQ-32B.
See the OpenThoughts3-1.2M dataset page or our paper for additional information.
Intended uses & limitations
Apache 2.0 License
Training procedure
We used 64 A100 gpus to train the model for 7 days.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00016
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
More info can be found in our repository: https://github.com/open-thoughts/open-thoughts.
Links
- 📝 OpenThoughts Paper
- 📊 OpenThoughts3-1.2M and OpenThinker3-7B Blog Post
- 💻 Open Thoughts GitHub Repository
- 🧠 OpenThoughts3-1.2M dataset
- 🤖 OpenThinker3-7B model
- 🤖 OpenThinker3-1.5B model - this model.
Citation
@misc{guha2025openthoughtsdatarecipesreasoning,
title={OpenThoughts: Data Recipes for Reasoning Models},
author={Etash Guha and Ryan Marten and Sedrick Keh and Negin Raoof and Georgios Smyrnis and Hritik Bansal and Marianna Nezhurina and Jean Mercat and Trung Vu and Zayne Sprague and Ashima Suvarna and Benjamin Feuer and Liangyu Chen and Zaid Khan and Eric Frankel and Sachin Grover and Caroline Choi and Niklas Muennighoff and Shiye Su and Wanjia Zhao and John Yang and Shreyas Pimpalgaonkar and Kartik Sharma and Charlie Cheng-Jie Ji and Yichuan Deng and Sarah Pratt and Vivek Ramanujan and Jon Saad-Falcon and Jeffrey Li and Achal Dave and Alon Albalak and Kushal Arora and Blake Wulfe and Chinmay Hegde and Greg Durrett and Sewoong Oh and Mohit Bansal and Saadia Gabriel and Aditya Grover and Kai-Wei Chang and Vaishaal Shankar and Aaron Gokaslan and Mike A. Merrill and Tatsunori Hashimoto and Yejin Choi and Jenia Jitsev and Reinhard Heckel and Maheswaran Sathiamoorthy and Alexandros G. Dimakis and Ludwig Schmidt},
year={2025},
eprint={2506.04178},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2506.04178},
}