Jerry Liu

Today I’m excited to introduce OpenDocRouter - a unified API for document parsing with the latest frontier and open-weight models. There are a lot of VLMs and OCR models that can be used for document parsing: we have over 130+ models on ParseBench, and a HuggingFace search for “ocr” turns up thousands of results. It’s extremely time consuming to choose between OCR vendors. You need to figure out the right prompts, handle rate limits, manage deployments, integrations with all models you’re using, and benchmark new models as they come out. OpenDocRouter provides a comprehensive, transparent set of models along the price-performance frontier. It manages a unified API to transcribe documents to markdown. It serves all frontier and open-weight models “at-cost”, with a small transaction cut. It handles rate limits with all models to ensure you can put massive volume through. It even offers bounding boxes and layout as a service, so that you can add grounding to any model that you’re using. When new OCR candidate models ship, we will benchmark them on ParseBench and immediately add them to OpenDocRouter. We’re adding a lot more models very quickly, and also adding some extremely exciting feature improvements (e.g. automated routing) as we speak. We welcome your feedback! Check it out: https://www.opendocrouter.ai/ Blog: https://www.llamaindex.ai/blog/introducing-opendocrouter

You can literally do anything these days if you just ask for it

Unlimited OCR is a good example of a low-parameter OCR model that can process any document extremely quickly. It's good at reading order and layout, and decent over tables. It is slightly better than PaddleOCR on tables and slightly worse on layout. There are tradeoffs though - it ignores visual elements, formatting, and the most complex tables. It scores ~46.2% and ranks #115 on ParseBench. There's plenty of opportunities to improve this frontier, but Unlimited OCR is still a good milestone! Check out ParseBench: https://www.parsebench.ai/

OCR is the perfect example of a use case that has been dominated by legacy/brittle systems, and can be solved both accurately and cheaply by applying the right amount of agentic intelligence. A properly tuned agentic OCR needs to dynamically apply extra compute to complex elements, review and correct failures, and create the right semantic meaning throughout the page. It's a task that frontier models are both overengineered for in terms of cost and latency, and struggle at over a long tail of complex edge cases. Check out this blog post by @LoganMarkewich below: https://www.llamaindex.ai/blog/ocr-is-dead-long-live-agentic-ocr

We got extremely good at extracting a scanned from layer by layer 📋 - from printed fields, overlaid text, handwriting, and reviewer annotations. For instance, on an annotated permit, our Extract agents can extract out both the original permit number but also any drawn/handwritten annotations on top. they're also grounded exactly in the source text. our agents reduce error rates by 2x+, at a small fraction of the price compared to using a frontier model. Our Extract v2.5 agents are SOTA in price/performance, and scanned forms are just one example. Come check it out! Extract v2.5: https://www.llamaindex.ai/blog/introducing-extract-v2-5 Available in LlamaParse: https://cloud.llamaindex.ai/

I agree that chatgpt/codex has the current best agent interface for deep work. - It unifies all my work (coding/knowledge work) in one interface. Toggling between chatgpt/codex doesn't have explicitly separate flows - It has forking (why doesn't the claude app have forking?) Claude Code CLI is probably the best that a CLI app can get. I still love Opus 5.5, especially for product demos, but mostly use it through the CLI. But sometimes having a GUI is nicer.

Claude Haiku 5.5 is now available on OpenDocRouter as one of the cheapest models for document parsing. It is roughly $1.2 / 1k pages and does quite well on tables and text reading order compared to other models at this price point. It struggles a bit on charts, semantic formatting, and bounding boxes. Check out OpenDocRouter, which we just launched today! https://www.opendocrouter.ai/models

this was a really good business decision and ensures i'll continue using @bot

having high agency is choosing to do a hot ones challenge with no spice tolerance and powering through 9 out of 10 wings s/o matisse

Congrats to @nvidia for creating the best open-weight model on document parsing so far. Nemotron 3.5 Parse takes the #1 spot on ParseBench, our comprehensive real-world benchmark for document understanding. The full list consists of 150+ frontier models, open-weight models, and specialized solutions. It does this while remaining sub-1B parameters so it's also blazing fast ⚡️ Big thanks to @JensenHuang for the shoutout at GTC Berlin! Nemotron release: https://huggingface.co/blog/nvidia/nemotron-retriever-world-class-agentic-retrieval ParseBench: https://www.parsebench.ai/

Introducing the Document Parsing Protocol (DPP) There are hundreds of different OCR models and tools, but they might all parse a document in different ways. Some errors are very clear-cut (e.g. hallucinating text, or completely misaligning a table), but many errors are much more ambiguous (e.g. how to represent split tables sharing a header). It's an issue that plagues most parse benchmarks too, because they inherently reward a certain style even if other styles might be semantically correct. We introduce a protocol that proposes a standard way of parsing these documents into a correct format. We've curated a list of 140 rules that cover literally every aspect of doc parsing, for instance: - tables: borderless whitespace tables, header-only tables, horizontal slicing, side-by-side mini tables - forms: key-value regions, checkboxes, checklists, ids and labels, fields outside the form, - charts: standardized chart output, multiple charts, gantt charts, line charts You can use DPP as an evaluation rubric for LLMs when determining the quality of parsing a document. It's not a full evaluation benchmark (yet), but an initial evaluation guide, and meant to encourage a discussion on what are the "best" ways to parse a document. Check it out: https://www.parseprotocol.com/guide/forms-forms-and-key-value-regions Blog: https://www.llamaindex.ai/blog/introducing-the-document-parsing-protocol

Anthropic filed their 318-page S-1 yesterday for the biggest IPO in history. We parsed and synthesized every page in the S-1 and built this reader so that you can get the highlights: https://www.documentstories.com/anthropic-s1 Built with LlamaParse and @vercel