pierre

Liteparse now support markdown, beat markitdown in speed and accuracy, fully open source https://github.com/run-llama/liteparse

Yes, it evaluate using frontier model for parsing from all 3 major provider (google, anthropic and openai). It is also easy to extend to evaluaye new model (code/dataset is available)

Build a benchmark to evaluate how good document parser work on a dataset of 2000 PDFs manually annotated, trying to evaluate accross multiple dimensions: charts, tables, text styling, text correctness, and attribution.

The benchmark evaluate performance on full page (not selected part of the pages), and evaluaye different OSS / crobtier model / commercial approach.

For transparency it is available as a HF leaderbaord.

Paper: https://arxiv.org/abs/2604.08538

A fast and open source spatial text parser

A new document (including PDF) parser that outperform traditional tool such as PyPDF or MuTools.

Link to open source repo: https://github.com/run-llama/liteparse

Contributor here, happy to answer any questions!

Demo: https://olmocr.allenai.org/ Paper: https://arxiv.org/abs/2510.19817

Main issue is that token are not equivalent across provider / models. With huge disparity inside provider beyond the tokenizer model:

- An image will take 10x token on gpt-4o-mini vs gpt-4.

- On gemini 2.5 pro output token are token except if you are using structure output, then all character are count as a token each for billing.

- ...

Having the price per token is nice, but what is really needed is to know how much a given query / answer will cost you, as not all token are equals.

LlamaIndex | Senior/Staff Software Engineer (LlamaParse) | San-Francisco, CA | Remote | Full-time | $100K – $300K + Equity | https://www.llamaindex.ai/careers

LlamaIndex is building a platform for AI agents that can find information, synthesize insights, generate reports, and take actions over the most complex enterprise data.

We are seeking an exceptional engineer to join our growing LlamaParse team. Will work at the intersection of document processing, machine learning, and software engineering to push the boundaries of what's possible in document understanding. As a key member of a focused team, will have significant impact on our product's direction and technical architecture.

We are also hiring for a range of other roles, see our career page:

- Backend Software Engineer

- Forward Deploy Engineer

- Founding AI Engineer

- Open Source Engineer Python

- Founding Lead Product Manager

- Platform Engineer

- Senior Developer Relation Engineer

- Senior / Staff Backend Engineer

- Product Marketing Manager

If you want to try agentic parsing we added support for sonnet-3.7 agentic parse and gemini 2.0 in llamaParse. cloud.llamaindex.ai/parse (select advanced options / parse with agent then a model)

However this come at a high cost in token and latency, but result in way better parse quality. Hopefully with new model this can be improved.