Language models can be used to solve long-horizon planning problems in two distinct modes. In afast‘System-’ mode, models directly generate plans without any explicit search or backtracking, and in aslow‘System-’ mode, they plan step-by-step by explicitly searching over possible actions. While System-planning is typically more effective, it is also more computationally expensive, often making it infeasible for long plans or large action spaces. Moreover, isolated System-or System-planning ignores the user’s end goals and constraints (e.g., token budget) , failing to provide ways for the user to control their behavior. To this end, we propose theSystem-Planner, a controllable planning framework with language models that is capable of generating hybrid plans and balancing between the two planning modes based on the difficulty of the problem at hand. System-consists of (i)  a controller, (ii)  a System-Planner, and (iii)  a System-Planner. Based on a user-specified hybridization factorgoverning the degree to which the system uses System-vs. System-, the controller decomposes a planning problem into sub-goals, and classifies them as easy or hard to be solved by either System-or System-, respectively. We fine-tune all three components on top of a single base LLM, requiring only search traces as supervision. Experiments with two diverse planning tasks – Maze Navigation and Blocksworld – show that our System-Planner outperforms a System-Planner, a System-Planner trained to approximate A∗search, and also a symbolic planner (A∗search) , given an exploration budget. We also demonstrate the following key properties of our planner: (1) controllability: by adjusting the hybridization factor(e.g., System-vs. System-)  we can perform more (or less)  search, improving performance, (2) flexibility: by building a neuro-symbolic variant composed of a neural System-planner and a symbolic System-planner, we can take advantage of existing symbolic methods, and (3) generalizability: by learning from different search algorithms (BFS, DFS, A∗) , we show that our method is robust to the choice of search algorithm used for training.111Code available at:https://github.com/swarnaHub/System-1.x