LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity LandingAI released Agentic Document Extraction (ADE) Gen2, a rebuild of its document intelligence stack around the new DPT-3 model family, now generally available with free access in the ADE playground. Gen2 splits parsing into DPT-3 Verity, which LandingAI says charges roughly 40% of the credits DPT-3 Pro charges, and DPT-3 Pro, and replaces DPT-2's flat 3 credits per page with pricing of 1 credit per page plus 0.5 credits per 1,000 output characters for Pro on the priority tier and 0.3 credits per page plus 0.2 credits per 1,000 output characters for Verity, with the standard tier halving both rates. LandingAI projects 25% to 80% cost reductions on mixed workloads and claims parsing under one cent per page with Verity on standard, and plans automated routing between the two models for fall 2026. LandingAI has shipped Agentic Document Extraction ADE Gen2 https://landing.ai/blog/introducing-agentic-document-extraction-gen2 , a rebuild of its document intelligence stack around a new model family called DPT-3. Gen1 treated a document as a flat list of chunks. Gen2 treats it as a tree, prices it by the characters it returns rather than by the page, and grounds every answer back to a specific line or word on the page. LandingAI team frames the release around three themes: affordability, agent ready outputs, and atomic grounding. Is it deployable? Yes. ADE Gen2 is generally available now. Developers start free in the ADE playground https://ade.landing.ai/ . Enterprises can run it in US or EU cloud, in their own VPC on AWS, Azure or Google Cloud, inside Snowflake, or on premises including air gapped environments. Two parsing models instead of one Gen2 splits parsing into two models so the workload picks the price. DPT-3 Verity https://docs.landing.ai/dpt3/parse-response transcribes digitally created documents deterministically and returns a bounding box and a confidence score for every word. It targets high volume text, tables and simple form fields. DPT-3 Pro reads page layout before words, detects block types from tables and figures through marginalia and signatures, returns them in reading order, and handles scanned pages, handwriting, non Latin scripts and LaTeX math. LandingAI team states DPT-3 Verity charges roughly 40% of the credits DPT-3 Pro charges, and also state automated routing between the two is planned for fall 2026. The pricing change is the real story Under DPT-2, every page cost a flat 3 credits. Under DPT-3, credit consumption https://docs.landing.ai/dpt3/credit-consumption is the sum of a page component and an output character component. On the priority tier, DPT-3 Pro bills 1 credit per page plus 0.5 credits per 1,000 output characters. DPT-3 Verity bills 0.3 credits per page plus 0.2 credits per 1,000 output characters. The standard tier halves both rates, so a 12 page Pro parse returning 48,120 characters comes to 36.1 credits on priority and roughly half that on standard. Totals round up to the nearest 0.1 credit, and the response metadata reports every input to the calculation. Service tiers are the second lever. Priority is for when a person or an agent is waiting. Standard runs asynchronously at 0.5x price and suits pipelines that tolerate minutes to hours. Note that synchronous calls always bill at priority, and the playground itself runs on priority. LandingAI projects 25% to 80% cost reductions on mixed workloads and claims parsing under one cent per page with Verity on standard. Treat those as vendor figures until you benchmark your own document mix, since the character component means a dense page can cost more than it did before. Blocks, not chunks The Parse v2 response https://docs.landing.ai/dpt3/parse-response has three top level fields: markdown in reading order, metadata , and structure . The structure is a document node whose children are pages, whose children are blocks. Block types include text, table, table cell, figure, marginalia, attestation, logo, card and scan code. Every block carries a semantic ID in the form type-index , stable within a response but not across re parses, plus a grounding object with the page number, a range into the markdown string, and a normalized bounding box. Markdown output is standardized too. Figures use