In this paper we analyze GMM with strong and nearly-weak instruments. In the nearly weak-GMM, the correlation between the instruments and the first order conditions decline at a slower rate than root T. We find an important difference between the nearly-weak case and the weak case. Inference with point estimates is possible with Wald, Likelihood Ratio and Lagrange Multiplier tests in GMM with semi-weak instruments. The limit is the standard X squared limit. This is important from an applied perspective since tests on the weak case do depend on the true value and can only test simple null. Even though we may have all nearly-weak instruments in GMM it is still possible to test various hypothesis of interest. We also find a difference between nearly-weak and standard GMM cases. We derive higher order expansions for test statistics in the nearly-weak case, and we show that with declining quality of instruments finite sample behavior of these tests get worse, so standard GMM finite sample behavior is always better than nearly-weak GMM. Unlike the standard GMM, in the nearly-weak GMM we can not eliminate the second order terms from these test statistic's expansions. Wald test in Continuous Updating Estimator (CUE) have desirable properties in higher order expansions in the nearly-weak case. We also propose a pretest, bootstrap Kolmogorov-Smirnov test, to differentiate between weak and nearly-weak asymptotics. This is based on bootstrapping Wald CUE test. Since Wald CUE test has different limits under weak and nearly-weak cases and has good properties in the higher order expansions its empirical distribution can be used in a pretest. We also conduct some simulations and show that some of the asset pricing models conform to nearly-weak asymptotics. Clearly, this paper shows that when we move away from the standard GMM towards nearly-weak case, finite sample behavior suffers but large sample theory remains intact. But if we further move away from the nearly-weak case to weakly identified GMM the large sample theory changes too.