Given that they seem to favor consistency in the CAP model, it would be closer to Hbase in this regard. Cassandra is, by design, eventually consistent and can not offer these same guarantees.
There is a general misconception that the an entire DBMS must pick tradeoffs based on the CAP theorem. (IE, you can use a database that offers availability, but can NEVER offer immediate consistency) Instead, a DBMS may offer different CAP guarantees per operation.
Another problem I can see is when you write to 3 nodes, have W=2 and a write succeeds only one one node. Then later on through read repair the value propagates to the other 2 nodes. So our write "failed" but really in the end succeeded. I'm not too much into the Cassandra details but those two things immediately came to my mind when reading some of the wiki pages.
It boils down to not using/offering algorithms like 2-Phase Commit or Paxos but more sloppy quorums.
So from what I can tell, Cassandra can not guarantee strong consistency. But then again I could be wrong and missed something.
"Note that if W + R > ReplicationFactor, where W is the number of nodes to block for on write, and R the number to block for on reads, you will have strongly consistent behavior; that is, readers will always see the most recent write."
Could you explain why this is incorrect? It seems the Cassandra devs use the definition of "consistency" from Brewer's paper, and Cassandra offers this level of consistency.
Your point about read-repair is correct when using QUORUM consistency level, but even then you can avoid that by using the ALL consistency level.