AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
Authors: name: Yunjie Ji, name: Xiaoyu Tian, name: Sitong Zhao, name: Haotian Wang, name: Shuaiting Chen, name: Yiping Peng, name: Han Zhao, name: Xiangang Li

We present AM-Thinking-v1, a 32B dense language model that advances the
frontier of reasoning, embodying the collaborative spirit of open-source
innovation. Outperforming DeepSeek-R1 and rivaling leading Mixture-of-Experts
(MoE) models like Qwen3-235B-A22B and Seed1.5-Thinking, AM-Thinking-v1 achieves
impressive scores of 85.3 on AIME 2024, 74.4 on AIME 2025, and 70.3 on
LiveCodeBench, showcasing state-of-the-art mathematical and coding capabilities
among open-source models of similar scale.
  Built entirely from the open-source Qwen2.5-32B base model and publicly
available queries, AM-Thinking-v1 leverages a meticulously crafted
post-training pipeline - combining supervised fine-tuning and reinforcement
learning - to deliver exceptional reasoning capabilities. This work
demonstrates that the open-source community can achieve high performance at the
32B scale, a practical sweet spot for deployment and fine-tuning. By striking a
balance between top-tier performance and real-world usability, we hope
AM-Thinking-v1 inspires further collaborative efforts to harness mid-scale
models, pushing reasoning boundaries while keeping accessibility at the core of
innovation. We have open-sourced our model on
https://huggingface.co/a-m-team/AM-Thinking-v1{Hugging Face}.