We introduce a new model of computation: the online LOCAL model (OLOCAL). In this model, the adversary reveals the nodes of the input graph one by one, in the same way as in classical online algorithms, but for each new node the algorithm can also inspect its radius-T neighborhood before choosing the output; instead of looking ahead in time, we have the power of looking around in space. It is natural to compare OLOCAL with the LOCAL model of distributed computing, in which all nodes make decisions simultaneously in parallel based on their radius-T neighborhoods.It is not hard to see that the OLOCAL model is strictly stronger than LOCAL, and this already holds for cycles, but simple separations make use of problems that are defined in terms of global constraints, or rely on a promise about the input. The main goal of this work is to study the relation of OLOCAL and LOCAL for promise-free problems defined in terms of local constraints.We focus on the broadly studied family of locally checkable problems (LCLs). We show that on paths and cycles, the OLOCAL and LOCAL models are surprisingly close to each other: there are only two broad classes of LCL problems, one in which we need T = Θ(n) in both models, and one in which T = O(log * n) suffices in both models. These results showcase how ideas from distributed computing can be also used to classify the hardness of online graph problems.We then prove an exponential separation in 2-dimensional grids: we show that the 3-coloring problem in grids can be solved with radius T = O(log n) in the OLOCAL model, while it requires T = Ω( √ n) in the LOCAL model. This can be contrasted with the problem of 4-coloring grids, which is easy in both of the models, and with the problem of 2-coloring grids, which is hard in both of the models. Hence the OLOCAL model provides a new, more fine-grained perspective for studying the locality of graph problems, enabling us to e.g. separate the problems of 2-coloring and 3-coloring in grids in terms of their localities.