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ParseBench 评测 16 个前沿 VLM 的文档解析能力
Jerry Liu 称对 16 个近期前沿 VLM（包括 Opus 5.5、GPT-6 Sol/Luna）做了文档解析评测，考察更高推理投入是否带来更好的 PDF 读取表现。结论：Opus 5.5 相对价格性能最好，尤其擅长表格解析；Astra 表现也不错但起价更高；GPT-6 Luna 在低价端更有吸引力。作者认为大规模文档解析仍应使用 LlamaParse 等专用 OCR 方案，但在 Codex/Claude Code 等 agent 循环内解析文档时 Opus 5.5 目前领先。完整结果发布在 ParseBench。
AI推荐理由 LlamaIndex 作者发布的 ParseBench 评测，覆盖 16 个前沿 VLM（含 Opus 5.5、GPT-6 Sol/Luna）在文档解析上的表现，给出按价格性价比的结论（Opus 5.5 表格解析最佳、GPT-6 Luna 在低价端更有吸引力），并指出规模化场景仍建议专用 OCR。属于有具体结论和公开评测页的开发者一手信息，但为作者自述、无独立复核，且带 LlamaParse 推广成分。
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正文
We comprehensively evaluated 16 recent frontier VLMs - including Opus 5.5 and GPT-6 Sol/Luna - on whether higher effort led to better document parsing performance.
Higher effort typically leads to improvements on other benchmarks (coding, knowledge work), but up until recently it wasn't obvious that this natively improved capabilities for reading PDFs.
Results:
✅ Out of the frontier models, Opus 5.5 has the best performance relative to its price. It's especially good at parsing tables.
✅ Astra is also quite good, but starts at a more expensive price than Opus.
✅ GPT-6 Luna is more compelling at the cheaper end of doc parsing
If you're parsing documents at scale, you'll still want a dedicated OCR solution like LlamaParse ([cloud.llamaindex.ai](https://cloud.llamaindex.ai/)) that has better performance at a cheaper price.
But if you're parsing docs "in the agent loop" within an app like Codex/Claude code, and you're too lazy to integrate a dedicated solution, then Opus 5.5 is currently the leader.
Full results on ParseBench: [parsebench.ai](https://www.parsebench.ai/)
