Since aic is not defined for quasimodels, and anyhow your two models in this case would not be nested, aic should not be used for comparison. I would go for model visualization, see for instance Dealing with Overdispersed Negative Binomial using glmmTMB for examples of use of simulated residuals for model checking (well, simalated residuals cannot be defined for quasi-models, so for that use maybe pearson residuals.)

See also Fitting a binomial GLMM (glmer) to a response variable that is a proportion or fraction

Since AIC is not defined for quasi-models, and anyhow your two models in this case would not be nested, AIC should not be used for comparison. I would go for model visualization, see for instance Dealing with Overdispersed Negative Binomial using glmmTMB for examples of use of simulated residuals for model checking (well, simulated residuals cannot be defined for quasi-models, so for that use maybe Pearson residuals.)

See also Fitting a binomial GLMM (glmer) to a response variable that is a proportion or fraction