Benchmarking Cassandra against other systems can lead to valuable insights, but many results are less useful due to preventable errors. Some common mistakes include using VMs with noisy neighbors, shared storage that becomes a bottleneck, and inadequate low-level operations like random reads. Additionally, benchmarking with small datasets or failing to reset the cluster between runs can lead to misleading results. To ensure accurate benchmarks, it's crucial to use established load generators, configure disks properly, allow JVM warmup, and follow best practices for performance tuning.