Model Releases
Arcee AI Unveils Trinity Large: The 400B Open Source MoE Powerhouse
Arcee AI releases Trinity Large, a 400B parameter MoE model with Apache 2.0 licensing, challenging proprietary giants with sovereign infrastructure.
January 27, 2026
Model ReleaseTrinity Large
Introduction
In a bold move to challenge the proprietary dominance of major tech giants, Arcee AI officially released Trinity Large on January 27, 2026. This new foundation model represents a paradigm shift for enterprise developers seeking sovereign control over their AI infrastructure. Unlike the walled gardens of proprietary models, Trinity Large is built in the United States with open weights, ensuring that data and logic remain under developer jurisdiction.
The release comes as global labs pivot toward proprietary lock-in, making Arcee's position as a sovereign infrastructure layer increasingly valuable. For developers, this means the ability to download, customize, and adapt the model for long-horizon agentic workflows without fear of service termination or data leakage. Arcee, a tiny 26-person U.S. startup, has managed to build a high-performing, massive open-source LLM that is gaining significant traction among OpenClaw users and enterprise CTOs alike.
- Released: January 27, 2026
- Provider: Arcee AI (US-based)
- License: Apache 2.0
- Status: Open Weights
Key Features & Architecture
Trinity Large utilizes a sophisticated Mixture of Experts (MoE) architecture designed to balance computational efficiency with raw intelligence. The model boasts a total parameter count of 400 billion, yet it only activates 13 billion parameters per token during inference. This sparse MoE design allows for high-quality reasoning without the prohibitive costs associated with dense 400B models.
The architecture is optimized for context-heavy tasks, supporting a massive context window that enables deep analysis of lengthy documents and codebases. It features native multimodal capabilities, allowing it to process text, code, and visual data seamlessly. This flexibility makes it a versatile tool for modern software engineering pipelines where understanding both code and documentation is critical.
- Total Parameters: 400B (MoE)
- Active Parameters: 13B
- Context Window: 256K tokens
- Architecture: Sparse MoE
- Multimodal: Text, Code, Visual
Performance & Benchmarks
Trinity Large demonstrates state-of-the-art performance across critical developer benchmarks. In the MMLU evaluation, the model achieved a score of 88.5%, outperforming many proprietary closed-source alternatives. Its coding capabilities are particularly robust, securing a HumanEval score of 92.1%, which indicates superior ability to generate functional and bug-free code.