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
| MinerU | olmocr | |
|---|---|---|
| Stars | 81.0k | 19.7k |
| Star velocity /mo | 3.7k | 415.55555555555554 |
| Commits (90d) | 905 | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8909026784171507 | 0.3573227826099884 |
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.