The Allen Institute for AI (Ai2) released a new generation of its flagship large language models, designed to compete more squarely with industry and academic heavyweights.
The Seattle-based nonprofit unveiled Olmo 3, a collection of open language models that it says outperforms fully open models such as Stanford’s Marin and commercial open-weight models like Meta’s Llama 3.1.
Earlier versions of Olmo were framed mainly as scientific tools for understanding how AI models are built. With Olmo 3, Ai2 is expanding its focus, positioning the models as powerful, efficient, and transparent systems suitable for real-world use, including commercial applications.
“Olmo 3 proves that openness and performance can advance together,” said Ali Farhadi, the Ai2 CEO, in a press release Thursday morning announcing the new models.
It’s part of a broader evolution in the AI world. Over the past year, increasingly powerful open models from companies and universities — including Meta, DeepSeek, Qwen, and Stanford — have started to rival the performance of proprietary systems from big tech companies.
Many of the latest open models are designed to show their reasoning step-by-step — commonly called “thinking” models — which has become a key benchmark in the field.
Ai2 is releasing Olmo 3 in multiple versions: Olmo 3 Base (the core foundation model); Olmo 3 Instruct (tuned to follow user directions); Olmo 3 Think (designed to show more explicit reasoning); and Olmo 3 RL Zero (an experimental model trained with reinforcement learning).
Open models have been gaining traction with startups and businesses that want more control over costs and data, along with clearer visibility into how the technology works.
Ai2 is going further by releasing the full “model flow” behind Olmo 3 — a set of snapshots showing how the model progressed through each stage of training. In addition, an updated OlmoTrace tool will let researchers link a model’s reasoning steps back to the specific data and training decisions that influenced them.
In terms of energy and cost efficiency, Ai2 says the new Olmo base model is 2.5 times more efficient to train than Meta’s Llama 3.1 (based on GPU-hours per token, comparing Olmo 3 Base to Meta’s 8B post-trained model). Much of this gain comes from training Olmo 3 on far fewer tokens than comparable systems, in some cases six times fewer than rival models.
Among other improvements, Ai2 says Olmo 3 can read or analyze much longer documents at once, with support for inputs up to 65,000 tokens, about the length of a short book chapter.
Founded in 2014 by the late Microsoft co-founder Paul Allen, Ai2 has long operated as a research-focused nonprofit, developing open-source tools and models while bigger commercial labs dominated the spotlight. The institute has made a series of moves this year to elevate its profile while preserving its mission of developing AI to solve the world’s biggest problems.
In August, Ai2 was selected by the National Science Foundation and Nvidia for a landmark $152 million initiative to build fully open multimodal AI models for scientific research, positioning the institute to serve as a key contributor to the nation’s AI backbone.
It also serves as the key technical partner for the Cancer AI Alliance, helping Fred Hutch and other top U.S. cancer centers train AI models on clinical data without exposing patient records.
Olmo 3 is available now on Hugging Face and Ai2’s model playground.
