Predict response vs manual for logistic regression probability - r

Question

I am trying to manually calculate the probability of a given x with logistic regression model.
my model looks like this fit2 <- glm(ability~stability, data = df2)
I created a function that gives me the response:
estimator <- function(x){
predict(fit2, type = "response", newdata = data.frame(stability=x))
}
this function gives me 0.5304603 for the value x=550
then i create the manual version. For this i use the function p = e^(B0+B1*x)/(1 + e^(B0+B1*x))
so our code will look like this
est <- function(par, x){
x = c(1,x)
exp(par%*%x)/(1+exp(par%*%x))
}
where par = fit2$coefficients, x = 550
but this code returns 0.6295905
Why?
edit:
summary(fit2):
Call:
glm(formula = ability ~ stability, data = df2)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.78165 -0.33738 0.09462 0.31582 0.72823
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.531574 0.677545 -2.26 0.03275 *
stability 0.003749 0.001229 3.05 0.00535 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.1965073)
Null deviance: 6.7407 on 26 degrees of freedom
Residual deviance: 4.9127 on 25 degrees of freedom
AIC: 36.614
Number of Fisher Scoring iterations: 2