Ai2's Olmo 3 family challenges Qwen and Llama, adds open reasoning and data transparency

Models like Olmo 3, Smith said, also give enterprises more confidence in the technology. Since Olmo 3 provides the training data, Smith said enterprises can trust that the model did not ingest anything it shouldn't have. Ai2 has always claimed to be committed to greater transparency, even launching a tool called OlmoTrace in April that can track a model's output directly back to the original training data.

The company releases open-sourced models and posts its code to repositories like GitHub for anyone to use. Competitors like Google and OpenAI have faced criticism from developers over moves that hid raw reasoning tokens and chose to summarize reasoning, claiming that they now resort to "debugging blind" without transparency. Ai2 pretrained Olmo 3 on the six-trillion-token OpenAI dataset, Dolma 3.

The dataset encompasses web data, scientific literature and code. Smith said they optimized Olmo 3 for code, compared to the focus on math for Olmo 2. How it stacks up Ai2 claims that the Olmo 3 family of models represents a significant leap for truly open-source models, at least for open-source LLMs developed outside China.

The base Olmo 3 model trained "with roughly 2.5x greater compute efficiency as measured by GPU-hours per token," meaning it consumed less energy during pre-training and costs less. The company said the Olmo 3 models outperformed other open models, such as Marin from Stanford, LLM360's K2, and Apertus, though Ai2 did not provide figures for the benchmark testing. "Of note, Olmo 3-Think (32B) is the strongest fully open reasoning model, narrowing the gap to the best open-weight models of similar scale, such as the Qwen 3-32B-Thinking series of models across our suite of reasoning benchmarks, all while being trained on 6x fewer tokens," Ai2 said in a press release.