Taking the analogy and calculations directly, you are assuming that the fundamental computing unit of the brain is the neuron; we do not know if this is true. It could be a cortical column, a group of several neurons, the neuron, a dendritic branch (a fascinating review paper!), a synapse, receptors or neurotransmitter vesicles (how about glial cells?). This paper describes the somewhat accidental fascination with the historical view of a neuron being 'the unit'.
So, to the numbers. If one treats a singular neuron's action potential as a 1/0 like a transistor, sure you're numbers kind of make sense. (Not sure where you got the 1% figure from?) However this is making the assumption I eluded to above. If you were to run the numbers with synapses, the brain might win.
You are further assuming that a 1/0, spike or no spike, representation of a neuron is all that the brain does. We do not know how the brain encodes information -- as others have said, it might not work in the same serial 1/0 fashion that we have engineered computers to operate. Information/processing in the brain could be via individual neurons firing rates, population firing rates, spike timing, etc. (sections 1.5, 1.6 and 1.7 review some). Imagine that the timing of when a transistor flipped compared to another was important! Moreover, this only considers action potentials (or EPSPs if one redoes the numbers with synapses). Neurons (and synapses) are extremely non-linear and a lot of information/processing might be undertaken before or after an action potential (EPSP). This is the age old question of analogue (brains) vs. digital (modern computers).
Serial vs. parallel
I think an important point missing from previous answers is the fact that the brain is massively parallel. That is, there is not one processor serially undertaking instructions.
A typical CPU (core to be specific) in a computer may be very fast in terms of calculations per second -- arguably a great deal faster than a single neuron -- however, there are very few of these individual units when compared to the brain. This is why supercomputers take advantage of hundreds of CPUs (and cores). However, the numbers of these cores is extremely low when compared to the brain (if we are assuming that a neuron is the fundamental unit, see above), where an analogy might be that each core/neuron is a lot slower, there are however millions (trillions if 'the unit' is a synapse) of cores.
Yes, you have considered that a CPU has many many transitors on it, however the CPU itself it still only processing information in a serial fashion. It does this operation, then this operation, then this etc. As I said above, this is why supercomputers use many CPUs as this allows them to do a somewhat parallel process. However this is more complicated as in reality, a computer's parallel programme is typically just a serial one split a few times. For example, you want to calculate what A+b is where A is a huge matrix and b is a constant. One can do this in serial by iterating through all the elements in A and adding b; or, one can 'parallelise' this by using 10 CPUs and splitting the matrix in to 10 subsets and having each CPU do a serial operation on their own subset and finally concatenate the resulting subsets back in to A again. This isn't really parallel computing but rather smaller serial operations (parallel programming is hard). The brain is truly parallel. (The quote in the answer to this previous question has a great analogy in it.)
To compare the two is fundamentally flawed as the way we quantify the performance of a CPU does not apply to the brain. It might be possible to compare them if a common metric could be devised, but this would mean we would need to understand how the brain processes information in the first place, which is a long long way off.
As a final note, have a look at this article.
(Edited to include more detail as there was interest/comments.)