Today we're releasing Trinity-Large-Thinking.
Available now on the Arcee API, with open weights on Hugging Face under Apache 2.0.
We built it for developers and enterprises that want models they can inspect, post-train, host, distill, and own.
Preview showed us where the demand was going.
People were already running Trinity-Large-Preview in real agent workflows, with long-horizon tool use and production constraints. So over the last two months, we pushed our SFT and RL stack to meet that moment.
Our focus was clear.
Build a model that stays coherent across turns, uses tools cleanly, follows instructions under constraint, and is efficient enough to serve at scale.
That is the bet behind Trinity-Large-Thinking.
tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
An Apache 2.0 reasoning modelA model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.Full definition → post-trained specifically for long-horizon tool useA model's ability to call external functions — run code, search the web, edit files — instead of only generating text.Full definition → and multi-turn coherence, so AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → workloads can self-host, distill, or further post-train rather than renting a closed model.
Read the full thread on X ↗
