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Arcee AI has released Trinity-Large-Thinking, a 400-billion-parameter open-weight reasoning model with a mixture-of-experts architecture that activates only 13 billion parameters per token. The model matches Claude Opus 4.6 on agent benchmarks like Tau2 and PinchBench but lags on general reasoning tasks. The company spent approximately $20 million—roughly half its total venture capital—to train the model on 2,048 Nvidia B300 GPUs over 33 days.

AI2 is developing physical AI systems trained primarily on virtual simulation data rather than expensive real-world demonstrations. The approach, demonstrated through projects like MolmoBot, addresses the historical bottleneck of manually collecting hardware training data.