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A couple of points:
When you use the identity link function both the GEE and a (linear) mixed model give you coefficients that have a population/marginal interpretation.
In all other cases where you use a nonlinear link function, such as the logit or the log, there is a difference in the interpretation of the coefficients. Namely, GEEs provide coefficients ...
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When you have a random intercept, it's like have an exchangeable correlation structure, and when you have random intercepts and slopes, it's like having an AR-1 correlation structure... assuming the random effects are simple rather than cross-nested. These covariance matrices actually cover a few cases that random effects do not. Syntactically they're often ...
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In GLMMs you do not have an analogue of multivariate error terms for which you can define such a correlation structure.
A potential way to achieve something like this in GLMMs would be to use observation-level random effects, and define such a correlation structure for their variance-covariance matrix. I think this should be provided by the glmmTMB package.
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