Layer 4 takes less time to compute than layer 1? Anyone got an explaination?

Hello,

I am new to machine learning and was running some diagnostics.

When I do a model.predict:

myModel = Model(inputs = self.model_ori.input, outputs= self.model_ori.layers[1].output)
starttime = time.time()
myModelResult = myModel.predict(img)
endtime = time.time()

OR

myModel = Model(inputs = self.model_ori.input, outputs= self.model_ori.layers[4].output)
starttime = time.time()
myModelResult = myModel.predict(img)
endtime = time.time()

How is it that the 4th layer takes less time to predict than the first layer?

Layer Number Layer Name Size (Kilobytes) Time to Process (Seconds)
1 conv2d (Conv2D) 977 0.0484
2 batch_normalization (BatchNormalization) 1230 0.0665
3 leaky_re_lu (LeakyReLU) 1020 0.0547
4 max_pooling2d (MaxPooling2D) 300 0.0353

Can someone explain this behaviour to me? I want to learn more about how these things work.