LlamaIndex launches ParseBench for enterprise docs

Hot take: this is more useful as a regression and vendor-comparison harness than as a “best parser” leaderboard, because parser quality depends heavily on your document mix.

• –The strongest part is the multidimensional scoring model; it captures the failures that actually break downstream agent workflows, not just generic OCR quality.
• –Running it on your own documents is the right recommendation, since leaderboards can hide domain-specific weaknesses in tables, charts, or formatting fidelity.
• –The release is strategically useful for LlamaIndex because it turns document parsing into an evaluable product surface, which helps buyers compare tools more concretely.
• –The main caveat is that benchmark scores will still depend on how closely the test docs match your real corpus, so the numbers should be treated as directional, not absolute.