The miniBLING model represents a further simplification of the BLING (Biogeochemistry with Light, Iron, Nutrients and Gas) model, specifically engineered to achieve even greater computational efficiency while still capturing essential biogeochemical dynamics. It serves as a highly streamlined tool for ocean modeling, particularly valuable for studies requiring extensive simulations or higher spatial resolutions where computational resources are a primary constraint. This reduced-tracer model is a testament to the ongoing effort to balance model complexity with practical applicability in Earth system science. Developed as a derivative of the original BLING model, miniBLING significantly reduces the number of prognostic tracers. In its most simplified configuration, it can operate with as few as one tracer (e.g., 'PO4'), although other implementations utilize three key prognostic variables: dissolved inorganic carbon (DIC), phosphate (PO4), and dissolved oxygen (O2). To achieve this reduction, miniBLING removes the dissolved organic pool and employs a fixed climatology for the dissolved iron field, meaning iron is not treated as a prognostically evolving tracer. Like BLING, miniBLING is written in FORTRAN (F90) and is designed for seamless integration with GFDL's Modular Ocean Model (MOM) framework, being publicly available as part of MOM releases on GitHub. miniBLING is frequently employed in eddy-resolving ocean models and other high-resolution simulations where the computational burden of more complex biogeochemical models would be prohibitive. Its applications include investigating the transport of heat, carbon, oxygen, and phosphate across oceanic fronts and exploring the role of mesoscale eddies in biogeochemical cycling. Despite its simplified structure, miniBLING has demonstrated performance comparable to more sophisticated biogeochemical models for many key processes, making it a reliable choice for a wide array of studies in oceanography and climate modeling. However, its design inherently limits its ability to simulate the full complexity of the nitrogen cycle or explicitly track biomass, which can affect the representation of plankton growth dynamics and certain biogeochemical feedbacks. Nonetheless, its minimal computational footprint makes it an indispensable tool for large-scale and long-term climate simulations.