#DocJev

The non-obvious angle is that DocJev combines small-model prediction with pluggable OCR backends, letting teams trade latency for accuracy per document type rather than defaulting to a heavy VLM pipeline. Developers building RAG ingestion or document-processing workflows can wire it into existing pipelines via the GitHub repo, with the default path fully free. Compared with gpt-5.6-luna, it targets teams where throughput on large document packets matters more than marginal accuracy gains.

Jerry Liu@jerryjliu03 sourcesSource

Developers working with document processing can use DocJev as a faster alternative to Luna with similar accuracy, especially for digitalized documents.

Jerry Liu@jerryjliu03 sourcesSource

Free and open-source on GitHub, DocJev classifies documents 6x faster than gpt-5.6-luna at equivalent accuracy, giving developers a lightweight alternative to VLM-based pipelines like LlamaParse.

Jerry Liu@jerryjliu02 sourcesSource

For developers needing to classify or split documents, DocJev offers a fast, open-source alternative to GPT-5.6-luna, with the ability to choose between free and paid OCR backends.

shao__meng@shao__meng2 sourcesSource