The coefficient is the measure of how strongly the given factor predicts the dependent variable, or in other words how much of the variance in the dependent variable can be explained by that factor. So the coefficient itself, and not the t-statistic of the coefficient, is a measure of what you're calling how well the model (or, more correctly, the regressor) fits the data.
The t-statistic of the coefficient in a multiple regression is the statistical test of that coefficient, calculated as the coefficient divided by its standard error. So it would be most correct to say that this is a measure of how reliable the sample coefficient is, or how likely it is, given the coefficient we got, that the true coefficient is 0. But this is theoretically independent of the goodness of the fit, and you could have a very stable but small coefficient, which would get a high t-stat, or a large-on-average, but highly variable coefficient, which would receive a low t-stat. The results of the test will also, obviously, depend on your sample size and the degrees of freedom in your model.
Where the confusion might be is that the coefficient is more or less the same as a comparison to baseline, becuase the baseline is not (or shouldn't be) an explicit regressor in the model. A higher coefficient will mean, all else being equal, that the fit is better and/or that the effect is larger. But unless you're explicitly running a comparison of activation vs. baseline, this not what the coefficient actually means.
You can read more about this last issue here: