Trinity Mini is a sparse Mixture-of-Experts model that concentrates 26B parameters into a highly efficient 3B active footprint per token, improving inference efficiency relative to dense models of similar total size. Its routing architecture is tuned for long-context workloads, enabling stable performance on multi-document analysis, retrieval-augmented generation, and complex tool orchestration. The model is particularly well-suited for function-heavy agents and workflow automation, where it can maintain coherent state across many turns without incurring prohibitive GPU cost. This makes Trinity Mini a strong fit for production systems that need a balance of reasoning depth, speed, and operational efficiency
