Large language models such as o1 are pre-trained on vast text datasets. While these high-capacity models have broad world knowledge, they can be expensive and slow for real-world applications. In contrast, o1‑mini is a smaller model optimized for STEM reasoning during pretraining. After training with the same high-compute reinforcement learning (RL) pipeline as o1, o1‑mini achieves comparable performance on many useful reasoning tasks, while being significantly more cost efficient.

When evaluated on benchmarks requiring intelligence and reasoning, o1‑mini performs well compared to o1‑preview and o1. However, o1‑mini performs worse on tasks requiring non-STEM factual knowledge (see Limitations⁠).

Mathematics: In the high school AIME math competition, o1‑mini (70.0%) is competitive with o1 (74.4%)–while being significantly cheaper–and outperforms o1‑preview (44.6%). o1‑mini’s score (about 11/15 questions) places it in approximately the top 500 US high-school students.

Coding: On the Codeforces competition website, o1‑mini achieves 1650 Elo, which is again competitive with o1 (1673) and higher than o1‑preview (1258). This Elo score puts the model at approximately the 86th percentile of programmers who compete on the Codeforces platform. o1‑mini also performs well on the HumanEval coding benchmark and high-school level cybersecurity capture the flag challenges (CTFs).