MinerU vs olmocr

Side-by-side comparison of two AI agent tools

Short answer

  • olmocr has had no commit in 6 months; MinerU is actively maintained (905 commits in the last 90 days).
  • MinerU is growing faster: +3,746 GitHub stars in the last 30 days vs +416 for olmocr.
  • Pick MinerU for: transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows. Pick olmocr for: toolkit for linearizing PDFs for LLM datasets/training.

From GitHub data refreshed daily.

MinerUfree

Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.

olmocropen-source

Toolkit for linearizing PDFs for LLM datasets/training

Metrics

MinerUolmocr
Stars81.0k19.7k
Star velocity /mo3.7k415.55555555555554
Commits (90d)9050
Releases (6m)100
Overall score0.89090267841715070.3573227826099884

Pros

  • +专门针对 LLM 优化的输出格式,确保转换后的 Markdown/JSON 能够被 AI 模型高质量理解和处理
  • +支持复杂 PDF 文档的结构化解析,保持表格、图像和文本布局的完整性
  • +提供 Python SDK 和 Web 应用双重接口,既适合程序化集成也支持交互式使用
  • +Excellent handling of complex document layouts including equations, tables, handwriting, and multi-column formats with natural reading order preservation
  • +Cost-effective processing at under $200 per million pages, making it economical for large-scale dataset creation
  • +Continuous model improvements with recent releases showing significant performance gains and reduced hallucinations on blank documents

Cons

  • -主要专注于 PDF 处理,对其他文档格式的支持可能有限
  • -复杂文档的处理质量可能依赖于原始文档的质量和结构清晰度
  • -大规模批量处理时可能需要考虑计算资源和处理时间的平衡
  • -Requires GPU resources due to 7B parameter model, making it computationally intensive and potentially expensive to run
  • -May require multiple retries for some documents to achieve optimal results
  • -Limited to image-based document formats (PDF, PNG, JPEG) and requires technical expertise for setup and optimization

Use Cases

  • •构建 RAG(检索增强生成)系统时,将企业内部 PDF 文档转换为向量数据库可索引的格式
  • •为 AI 代理开发智能文档分析功能,自动提取和结构化合同、报告中的关键信息
  • •建立知识管理系统,将历史文档资料转换为可搜索和可查询的结构化数据
  • •Converting academic papers and research documents with complex equations and figures for LLM training datasets
  • •Processing legacy document archives with multi-column layouts and mixed content types into searchable text format
  • •Creating high-quality training data from technical manuals, textbooks, and scientific publications for domain-specific language models

FAQ

Which is more popular, MinerU or olmocr?
MinerU has more GitHub stars (80,986 vs 19,687).
Which is more actively developed, MinerU or olmocr?
MinerU had more commits in the last 90 days (905 vs 0).
Should I use MinerU or olmocr?
Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.