Keras functional API slower than Sequential / Not improving - python-3.x

Question

SOLVED!(Had to set trainable=true in the sequential model)
I am currently changing my Keras model from Sequential to the functional API. While the Sequential model does improve to an accuracy of 1 after like 10 epochs, the functional API model does not even reach 0.7 and does not further improve. Apart from the Input layer, both nets should be the same.
Sequential:
model = Sequential()
model.add(Embedding(20000, 256,input_length = 30))
model.add(SpatialDropout1D(0.4))
model.add(LSTM(256, dropout=0.3, recurrent_dropout=0.3))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss = 'binary_crossentropy', optimizer=Adam(lr=0.0001),metrics = ['accuracy'])
print(model.summary())
Output is:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_6 (Embedding) (None, 30, 256) 5120000
_________________________________________________________________
spatial_dropout1d_5 (Spatial (None, 30, 256) 0
_________________________________________________________________
lstm_5 (LSTM) (None, 256) 525312
_________________________________________________________________
dense_6 (Dense) (None, 1) 257
=================================================================
Total params: 5,645,569
Trainable params: 5,645,569
Non-trainable params: 0
_________________________________________________________________
None
For the functional API:
inputs = Input(shape=(31,))
embed = Embedding(20000, 256, trainable=False)(inputs)
drop = (SpatialDropout1D(0.4))(embed)
lstm = LSTM(256, dropout=0.3, recurrent_dropout=0.3)(drop)
acti = Dense(1,activation='sigmoid')(lstm)
model = Model(inputs=inputs, outputs=acti)
model.compile(loss = 'binary_crossentropy', optimizer=Adam(lr=0.0001),metrics = ['accuracy'])
print(model.summary())
Result
Model: "model_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_8 (InputLayer) (None, 31) 0
_________________________________________________________________
embedding_7 (Embedding) (None, 31, 256) 5120000
_________________________________________________________________
spatial_dropout1d_6 (Spatial (None, 31, 256) 0
_________________________________________________________________
lstm_6 (LSTM) (None, 256) 525312
_________________________________________________________________
dense_7 (Dense) (None, 1) 257
=================================================================
Total params: 5,645,569
Trainable params: 525,569
Non-trainable params: 5,120,000
_________________________________________________________________
None
Have I overseen something or can someone explain my results?