Initial release of Trinity Large Thinking as a free, open source reasoning model with 262K context window and focus on agentic workloads.
Initial release of Trinity-Large-Thinking, a 400B parameter open-weight reasoning model with 256 mixture-of-experts (13B active per token) optimized for agent tasks. Apache 2.0 licensed, trained on 17 trillion tokens over 33 days on 2,048 Nvidia B300 GPUs.
Arcee AI has released Trinity Large Thinking, an open source reasoning model with a 262,144-token context window. The model is available free via OpenRouter and claims strong performance in PinchBench, agentic workloads, and reasoning tasks.
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.
Arcee AI released Trinity-Large-Thinking, a 398B-parameter sparse Mixture-of-Experts model with approximately 13B active parameters per token, post-trained with extended chain-of-thought reasoning for agentic workflows. The model achieves 94.7% on τ²-Bench, 91.9% on PinchBench, and 98.2% on LiveCodeBench, generating explicit reasoning traces in <think>...</think> blocks before producing responses.