OLMo 3 7B Think is a reasoning-focused large language model developed by the Allen Institute for AI (AI2) and released in November 2025. As part of the OLMo 3 family, it distinguishes itself by providing a fully open development pipeline, including the pre-training data, training code, intermediate checkpoints, and post-training recipes. The model is designed to prioritize transparency and reproducibility in AI research while delivering performance competitive with leading open-weight models of similar scale.
The "Think" variant is specialized for complex, multi-step problem solving through the use of long chain-of-thought (CoT) reasoning. It generates explicit intermediate reasoning traces, allowing users to inspect the logic the model follows before arriving at a final answer. In comparative evaluations, the 7B Think model has shown particular strength in mathematics and programming, matching or exceeding the performance of contemporary models like Qwen 3 8B on benchmarks such as MATH and HumanEvalPlus.
Built on a dense decoder-only transformer architecture, the 7B version utilizes Multi-Head Attention (MHA) and supports an extended context window of 65,000 tokens. The model was pre-trained on the Dolma 3 dataset, consisting of approximately 6 trillion tokens, and post-trained using the Dolci suite, which employs Reinforcement Learning from Verifiable Rewards (RLVR) to elicit high-quality reasoning behaviors. The entire OLMo 3 ecosystem is released under the Apache 2.0 license.