Interactive benchmarks for LLMs usually report a single metric, even though succeeding in them requires models to show several different skills. While those benchmarks are useful for measuring the overall capability of models to perform the downstream task, it is harder to identify which skill was lacking and caused failure solely from the aggregate score. For example, in benchmarks where models are supposed to navigate a simulated world to perform a task, failures can come from perception, an ambiguous instruction, retrieval, missing commonsense about what actions do, unknown dynamics, or planning. \benchmark recasts the procedurally generated BabyAI gridworld as a fully observable, purely textual environment designed to evaluate planning separately from those factors. The nature of the environment and the structure of the prompt control for the other factors cited above. To evaluate the models, we define the PPD suite: \textbf{Predict} asks for the state that follows an action sequence, \textbf{Plan} asks for an action sequence that reaches a goal, and \textbf{Decompose} asks for a subgoal sequence that achieves a mission. Across seven frontier and open models, queried zero-shot, we find simulation far ahead of planning for every model, close to saturated for the strongest. The length of the required solution governs planning failure, and each of the four strongest models has a horizon beyond which single-attempt success collapses. The responses of claude-opus-4-6 and claude-sonnet-4-6, which show their working step by step, reveal that they plan by proposing L-shaped corridors, which is consistent with their successful plans staying near-optimal while success falls. At the subgoal level, decomposition precision likewise falls to zero on long missions, while comprehension of those missions persists.

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