The examples that I chose for my benchmark demonstrate that Lexiathan maintains accuracy and performance even on severely degraded input. On less corrupted input, Lexiathan runs significantly faster and is even more accurate.

Lexiathan also doesn't have any edit distance parameters that need to be configured, so there is no "tuning" required. In particular, it's worth mentioning that using a very large dictionary, e.g. 500,000 words, often degrades accuracy rather than improves it, and likely increases memory usage and latency as well.

Regarding Norvig's 98.9% figure--this seems to be from Norvig's own made-up data. In the real world, users often generate misspellings that exceed 2 edit distances in many use cases (OCR, non-native speakers, medical/technical terminology, etc), and published text (often already spell-checked) doesn't reflect the same level of errors. And in any case, Norvig's spell-checker apparently only achieves an accuracy of 67% on its own chosen benchmarks, so clearly the 98.9% figure is not a realistic reflection of actual spell-checker performance, even for an edit distance of 2. Lexiathan is extremely accurate and retains high performance even on heavily degraded input, and the benchmark data (and demo) that I presented reflect that.