Documents and OCROllama2026
OpenBMB (ModelBest) · China
The first MiniCPM model built only for documents: it reads text, tables, formulas, charts and seals, and pulls the needed fields out of papers. Just 1.3B. By the authors' tests, 93.68 on Real5-OmniDocBench (document parsing in real conditions: scans, photos, skewed and recaptured pages) and 54.7 on OCRBench v2. Chinese and English are stated, Russian is not. An auxiliary DSpark model is published for it that speeds up generation: according to the authors processing is 55% faster, and the speed is about 1.5 times that of PaddleOCR-VL-1.6 and 3.6 times that of MinerU2.5 Pro. It runs in vLLM, SGLang, llama.cpp and Ollama, with compressed GGUF, AWQ and GPTQ versions.
- parsing scans and photos of documents into markup: text, tables, formulas
- extracting fields from invoices, contracts and forms in Chinese and English
- fast processing of a document flow on a small server
- Sizes
- 1.3B
- Hardware
- from: Laptop
Documents and OCR2026
Baidu · China
Baidu's OCR model building on DeepSeek-OCR ideas: processes multi-page documents and PDFs in a single pass and outputs structured text. Claimed to be multilingual, but the language list is not published. On October 27 Unlimited-OCR-2 (MIT, about 3.3B) came out: according to the authors it parses documents of unlimited length (the SR-SWA technique), and the MarathonDocBench set for very long documents was published with it.
- Converting multi-page PDFs and scans to text and Markdown
- Recognizing contracts, invoices and reports
- Preparing document archives for search and RAG
- Sizes
- 3.3B
- Hardware
- from: Laptop
TextRUGGUF2025–2026
MiniMax · China
Large MoE models with very long context (up to 1M tokens for Text-01 and M3). M3 is multimodal and understands images. In October the open weights of M3.1-Flash came out (about 428B, 23B active, up to 1M tokens of context, text, images and video): a model for programming, agents and computer use, sped up by sparse attention. MiniMax Community License: free for non-commercial use; commercial use needs the caption "Built with MiniMax M3.1-Flash" and a one-time notice to MiniMax, and a separate permission when the product earns $20 million a year or more; military use is prohibited. The benchmark figures come from MiniMax itself. Licenses differ greatly from version to version.
- Analysis of large document archives in a single request
- Agents with tools
- Help for developers
- Sizes
- 230B-A10B – 456B-A46B
- Hardware
- from: Cluster
Documents and OCRRU2026
Datalab · USA
A tiny OCR model that runs on an ordinary CPU with no graphics card and no PyTorch: it uses ONNX Runtime, takes about 230 ms per page on four cores and reads up to 49 pages per second. It turns PDFs and images into markdown, plain text or JSON with boxes for lines and blocks (headings, lists, tables, captions) and reading order. In digital PDFs it takes the existing text layer and recognises only what is missing. Russian is among the stated languages. For handwriting, formulas and complex tables the authors point to their larger Chandra. The speed and accuracy figures are the authors' own.
- fast bulk conversion of PDFs and scans to markdown on a server with no graphics card
- search over document archives: text with boxes and reading order
- pre-marking pages before a heavy model
- Sizes
- about 1M parameters (the models weigh about 18 MB)
- Hardware
- from: Laptop
TextOllama2023–2026
DeepSeek · China
DeepSeek's flagship line: from the first 7B/67B to V4-Pro with 1.6 trillion parameters. Closed-model quality under an open MIT license; V4-Flash-Vision-Exp and V4.1-Flash understand images, context up to 1M tokens. The fresh V4.1-Flash-1017 answers shorter and faster than the previous Flash: by the measurements of DeepSeek itself, 77.0 vs 74.2 on DeepSWE v1.1 and 42.3 vs 31.2 on Terminal-Bench 4.0.
- Employee assistant on your own server
- Analysis of long contracts and reports
- Agents that work with tools and APIs
- Sizes
- 7B – 1.6T-A49B
- Hardware
- from: Laptop
Visual document searchGGUF2026
Tencent · China
Finds the right pages in documents as pictures, without turning them into text: matches a query against how the page looks. Version 8B scores 66.75 on ViDoRe V3. The index can be compressed to 32 vectors per page.
- searching scans, slides and reports without text recognition
- RAG over documents with tables and charts
- searching archives of contracts and catalogs
- Sizes
- 4.5B и 8B
- Hardware
- from: Laptop
Documents and OCR2026
Tencent · China
Parses a whole document page: turns it into Markdown with LaTeX formulas and HTML tables. The 4B version scores 95.38 on OmniDocBench v1.6. Languages on the card: English and Chinese.
- turning scans and PDFs into Markdown for search and RAG
- recognizing tables and formulas
- parsing reports and scientific papers
- Sizes
- 2B и 4B
- Hardware
- from: Laptop
Speech to textRUGGUF2025–2026
Mistral AI · France
Mistral's speech models: they understand audio, transcribe and answer questions about a recording. The Realtime version recognizes speech live and supports Russian; speech synthesis and a separate version for Arabic dialects are also available.
- Transcribing and summarizing recordings
- Asking questions about audio
- Real-time recognition
- Sizes
- 3B – 24B
- Hardware
- from: Laptop
Text2026
Aleph Alpha · Germany
A European open reasoning model for German and English. A 78B mixture-of-experts with 3.5B active per token, a 262K-token context (tested up to a million) and tool calling.
- answers over long documents and RAG
- an agent that calls tools
- extracting structured data from documents
- Sizes
- 78B-A3.5B
- Hardware
- from: Cluster
Text analysis2026
Perplexity · USA
A decision model built on Qwen3.8-27B: it picks an option, answers yes or no or gives a rating, and returns calibrated probabilities. It also understands images.
- routing requests to teams
- checking AI answers against sources
- rating tone and urgency of emails
- Sizes
- 27B
- Hardware
- from: 1 GPU
Text analysis2026
FlowX.AI · USA and Romania
A family of small models that pull amounts, deal parties, jurisdictions, accounts and sanctions-screening mentions out of banking and insurance texts. A human makes the decision on a disputed operation; automatic blocking without review is not acceptable.
- Extracting amounts, parties and jurisdictions from operation descriptions
- Parsing customer due diligence material and sanctions lists
- Marking account numbers and payment details in documents
- Sizes
- about 150M (based on ModernBERT-base)
- Hardware
- from: Laptop
TextRU2024–2026
Sber · Russia
Sber open models with strong Russian language support and local context, from 10B-A1.8B to 702B, all MIT. GigaChat3.1-Audio handles recordings up to two hours; GFusion is a fast diffusion text version.
- Russian-language employee assistant on your own server
- Customer replies and request handling in Russian
- Working with contracts and internal policies
- Sizes
- 10B-A1.8B – 702B-A36B
- Hardware
- from: Laptop
Text analysis2024–2026
Urchade Zaratiana and Fastino AI · France / USA
Finds the entities you need in text without training: just list what to look for (name, amount, date). GLiNER2 also classifies text. Multilingual versions understand Russian.
- Extracting names, amounts and dates from emails and contracts
- Parsing requests into CRM fields
- Classifying requests by topic
- Sizes
- about 50M to 500M
- Hardware
- from: Laptop
Documents and OCR2026
Jina AI · Germany
Document parsing in a single model: a whole page becomes Markdown - text in correct reading order, tables and formulas in LaTeX. Built on DeepSeek-OCR, with only 0.6B of its 3.4B parameters active.
- Converting scans and PDFs to Markdown
- Recognizing tables and formulas
- Parsing invoices, acts and reports
- Sizes
- 3.4B-A0.6B
- Hardware
- from: 1 GPU
Image + text2026
Apple · USA
Reads long documents differently: it turns pages into downscaled images and expands the full text only for the pages needed to answer. A long document then costs far less context.
- Answering questions over multi-page documents
- Finding the right page in a large report or case file
- Working with scans and PDFs without a separate OCR step
- Sizes
- 9B
- Hardware
- from: 1 GPU
Deepfake detection2023–2026
Adobe Research and University of Surrey · USA
An image watermark for arbitrary resolutions built for the Content Authenticity Initiative: it can both apply a mark and remove one. The detector errs in both directions - a human reviews the output.
- Marking images on the way out of your own pipeline
- Checking the provenance of a submitted image
- Linking with content provenance metadata
- Sizes
- model types Q and P with different mark capacity
- Hardware
- from: Laptop
TextRUOllama2024–2026
Cohere · Canada
Business models: document search with source citations, tool calling, many languages. Command A+ (2026) was the first under Apache 2.0, followed by the North line: code, translation and compact vision.
- Knowledge-base answers with source citations
- Agents that work with internal systems
- Translation and correspondence in different languages
- Sizes
- 2.5B – 218B-A25B
- Hardware
- from: Laptop
Documents and OCRRU2024–2026
Datalab · USA
A compact OCR toolkit from the makers of Marker and Chandra: text recognition, page layout, reading order and tables. Surya OCR 2 (650M) also runs on a CPU, and Russian is among the supported languages.
- Recognizing scans and PDFs, including in Russian
- Page layout: headings, tables, images, reading order
- Recognizing tables by rows and columns
- Sizes
- up to 650M
- Hardware
- from: Laptop
TextRU2024–2026
T-Bank · Russia
T-Bank models fine-tuned from Qwen for Russian: they write and reason in Russian noticeably better than the original. T-Lite is 8B, T-Pro 32B on one GPU; T-Search is a multi-step search agent in Russian and English.
- Russian-language support chatbot
- Analysis of requests and documents in Russian
- Answers based on the company knowledge base
- Sizes
- 7B – 36B-A3B
- Hardware
- from: Laptop
Moderation and safety2024–2026
GLiNER community (Fastino, Knowledgator, NVIDIA and others) · USA
Small GLiNER-based models for finding personal data: passports, phone numbers, accounts, addresses. Data types are set in words. Russian is not officially supported.
- Masking personal data before cloud AI
- Finding passport data and bank details in documents
- Checking data exports for leaks
- Sizes
- about 200M to 500M
- Hardware
- from: Laptop
Documents and OCRGGUF2025–2026
Shanghai AI Laboratory (OpenDataLab) · China
A popular open tool for converting PDFs to Markdown with its own small model. MinerU2.5-Pro was improved through data alone, without growing in size. Languages on the card: Chinese and English.
- Converting PDF reports and contracts to Markdown
- Recognising tables and formulas
- Preparing documents for RAG and search
- Sizes
- 0.9B – 1.2B
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025–2026
Baidu (PaddlePaddle) · China
A compact document parsing model from the popular PaddleOCR toolkit. Per the model card it supports 109 languages, including Russian; version 1.6 leads the OmniDocBench benchmark.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising tables, formulas and stamps
- Converting scans to Markdown and JSON
- Sizes
- 0.9B
- Hardware
- from: Laptop
TextRU2024–2026
Ivan Bondarenko (bond005), Novosibirsk State University · Russia
Russian-language models for working with documents rather than chatting: knowledge-base answers, extraction of entities and facts from Russian text, long context.
- Answers to questions based on internal documents
- Extracting names, dates and amounts from contracts
- Short summaries of long Russian texts
- Sizes
- 1.5B – 7.6B
- Hardware
- from: Laptop
Text analysisOllama2024–2026
NuMind · France
Models for template-based data extraction: give it a document or scan and a JSON field template, get a filled-in JSON back. NuExtract3 (4B) also converts scans to Markdown.
- Extracting company details, amounts and dates from invoices and contracts into JSON
- Parsing receipts, waybills and forms against a set template
- Converting scans to Markdown for search
- Sizes
- 0.5B – 8B
- Hardware
- from: Laptop
Search and RAGGGUF2025–2026
Octen · USA / Singapore
Qwen3-Embedding models fine-tuned by the startup Octen for search in legal, financial and medical texts. As of January 2026 the 8B version topped the RTEB leaderboard.
- Search across contracts and case law
- Search across financial reports
- Search across long documents up to 32K tokens
- Sizes
- 0.6B – 8B
- Hardware
- from: Laptop
Moderation and safety2026
OpenAI · USA
Finds and hides personal data: names, addresses, phone numbers, emails, account numbers, passwords. Runs even in the browser. Trained mostly on English.
- Removing personal data from text before sending it to cloud AI
- Finding passwords and keys in texts
- Anonymizing correspondence for analytics
- Sizes
- 1.5B (50M active)
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025–2026
Datalab · USA
A strong OCR model from the authors of Marker and Surya: handwriting, forms, tables. Per the model card it supports 90+ languages, with Russian among the examples.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising handwritten forms and questionnaires
- Recognising complex tables
- Sizes
- 5B – 9B
- Hardware
- from: Laptop
Search and RAGRU2026
Microsoft · USA
Microsoft's 2026 multilingual embeddings with context up to 32K tokens; Russian is on the language list. The 270M and 0.6B versions run on a regular server, 27B is the most accurate.
- Multilingual knowledge base search
- Picking passages for RAG
- Search across long documents
- Sizes
- 270M – 27B
- Hardware
- from: Laptop
TextGGUF2024–2026
AI21 Labs · Israel
A hybrid of Transformer and Mamba with a window of up to 256K tokens: handles long documents faster than conventional models. Jamba2 focuses on accurate, source-based answers.
- Answers based on long policies and contracts
- Knowledge-base search (RAG)
- Summaries of large documents
- Sizes
- 3B – 398B-A94B
- Hardware
- from: Laptop
Deepfake detection2024–2025
Meta · USA
A watermark for video and images that survives re-encoding and cropping. The detector errs in both directions: a missing mark does not prove a forgery, and finding one is a reason for a human to check.
- Marking video created or processed by AI
- Finding your own mark in re-uploaded clips
- Protecting ad materials from being reused as someone else's
- Sizes
- a mark of 96 to 1024 bits
- Hardware
- from: Laptop
Rerankers2025
ZeroEntropy · USA
Rerankers built on Qwen3. The model card lists the target domains — finance, law, code, medicine, science; the stated language is English.
- Refining results before an AI assistant answers
- Sorting search results across contracts and reports
- Search across technical and scientific documentation
- Sizes
- zerank-2 — 4B (based on Qwen3-4B), plus a smaller "small" version
- Hardware
- from: Laptop
Documents and OCRRUGGUF2025
Nanonets · USA / India
A model that converts documents to Markdown with tables, stamps, signatures, checkboxes and watermarks. The OCR2 model card lists Russian among its languages.
- Recognising invoices, contracts and delivery notes, including in Russian
- Recognising stamps, signatures and marks
- Handwriting recognition
- Sizes
- 1.5B – 3B
- Hardware
- from: Laptop
Voice: speakers and sound2022–2025
pyannoteAI (Hervé Bredin) · France
The most widely used open tool for splitting a recording by speaker: who spoke and when. Usually paired with speech recognition. Weights are issued after a short form on HF.
- Tagging calls: which part is the agent, which is the customer
- Meeting minutes with speaker labels
- Preparing recordings for transcription and analysis
- Sizes
- a few million parameters
- Hardware
- from: Laptop
Deepfake detection2023–2025
IBM Research and The Chinese University of Hong Kong · USA
An AI-text detector trained together with a paraphraser: it was deliberately taught not to give up when the text has been rewritten. It errs in both directions; a human reviews the output.
- Checking texts that may have been rewritten after generation
- First-pass filtering in a newsroom or admissions office
- Comparison against simpler detectors
- Sizes
- about 355M (RoBERTa-large)
- Hardware
- from: Laptop
Documents and OCR2025
IBM and Hugging Face · USA
Tiny models for the open Docling document converter: they turn a page into markup with tables, formulas and code. Run on an ordinary laptop.
- Converting PDFs and scans to Markdown for search and RAG
- Recognising tables in reports
- Processing invoices and contracts on an ordinary PC
- Sizes
- 256M – 258M
- Hardware
- from: Laptop
TextGGUF2025
ByteDance · China
An open ByteDance 36B model with up to 512K tokens of context and an adjustable thinking budget. Fits on a single powerful GPU.
- Analysis of long documents
- Agents with tools
- Corporate assistant
- Sizes
- 36B
- Hardware
- from: 1 GPU
Visual document search2024–2025
Illuin Technology (ViDoRe team) · France
Searches PDFs and scans as images: pages do not need to be OCR'd first, the model finds the right one for a question directly, including tables and charts. Trained on English.
- Search across scans, presentations and PDFs
- RAG over documents with tables and charts
- Search across technical documentation
- Sizes
- 256M – 3B
- Hardware
- from: Laptop
Deepfake detection2024–2025
Meta · USA
An image watermark that can be applied to individual regions: the model shows which part of the image is marked. It errs in both directions - a human reviews the result.
- Marking generated and edited images
- Finding a marked fragment inside a collage
- Tracking which parts of a picture were made by AI
- Sizes
- a mark encoder and decoder for images
- Hardware
- from: Laptop
Rerankers2023–2025
Stanford NLP, later Answer.AI and LightOn · USA and France
A different search principle: every word of the question is compared with every word of the document, not the two texts as a whole. The index is heavier than with ordinary embeddings. The model cards list English.
- Search across a knowledge base of long documents
- Reordering retrieved passages
- Search across policies and technical documentation
- Sizes
- about 33M – 150M
- Hardware
- from: Laptop
TextOllama2025
DeepSeek · China
A reasoning model that thinks step by step before answering. Strong at calculations, logic and code; compact distilled versions are available.
- Complex calculations and logic checks
- Analysis of contracts and internal policies
- Help for developers
- Sizes
- 1,5B – 671B
- Hardware
- from: Laptop
Deepfake detection2022–2025
GRIP, University Federico II of Naples · Italy
Finds traces of editing and shows on a map which regions of an image look altered: suitable for scans of contracts, certificates and photos of documents. It errs in both directions - a person decides.
- Checking scans of certificates and contracts for edits
- Highlighting altered photo regions for an expert
- Filtering out obviously redrawn documents before manual review
- Sizes
- a transformer model producing a map of suspicious regions
- Hardware
- from: 1 GPU
Visual document search2025
Nomic AI · USA
Search across PDF pages and scans as images. The cards list English, Italian, French, German and Spanish — Russian is not among them.
- Search across an archive of scans and PDFs
- Search across tables and diagrams inside documents
- Picking pages for an AI assistant answer
- Sizes
- 3B and 7B
- Hardware
- from: 1 GPU
Math and reasoningOllama2024–2025
Qwen (Alibaba) · China
Qwen's first open reasoning model: it thinks step by step before answering and comes close to DeepSeek-R1 on maths tasks with only 32B parameters.
- Calculations and formula checks
- Complex analytics with step-by-step breakdowns
- Checking the logic of contracts and internal policies
- Sizes
- 32B
- Hardware
- from: 1 GPU
Speech to textRUGGUF2022–2025
OpenAI · USA
Speech recognition in 99 languages, including Russian. The de facto standard for transcribing calls and meetings. Hugging Face's faster Distil-Whisper is English only.
- Transcription of calls and video meetings
- Video subtitles
- Voice messages to text
- Sizes
- 39M – 1,5B
- Hardware
- from: Laptop
Visual document search2025
LlamaIndex · USA
A small model for searching document pages as images, from the team behind a popular RAG framework. The card lists English, Italian, French, German and Spanish.
- Search across scans and PDFs without OCR
- Search across invoices, acts and contracts
- Picking pages for an AI assistant answer
- Sizes
- 2B (based on Qwen2-VL)
- Hardware
- from: 1 GPU
Visual document search2024
LightOn · France
A reranker for document pages as images: after a visual search it reorders the found pages by how well they answer the question. The card does not state the languages.
- Refining search results over scans and PDFs
- Selecting pages before an AI assistant answers
- Sorting retrieved slides and reports
- Sizes
- 2B (based on Qwen2-VL)
- Hardware
- from: 1 GPU
Visual document search2024
University of Waterloo, Tevatron project · Canada
Searches page screenshots: the page is not OCRed but turned into a single vector, so the index is more compact than with late-interaction models. The card lists English and French.
- Search across scans and PDFs without OCR
- Search across presentations and reports with complex layouts
- Picking pages for an AI assistant answer
- Sizes
- 2B (based on Qwen2-VL)
- Hardware
- from: 1 GPU
Moderation and safety2024
iiiorg · not disclosed
A popular detector of 17 types of personal data in six European languages. No Russian and a non-commercial license: suitable for trials and research.
- Finding personal data in texts
- Comparing the quality of PII detectors
- Sizes
- 278M
- Hardware
- from: Laptop
Fact-checking and judgesOllamaNot maintained2024
UT Austin and Bespoke Labs · USA
Checks whether each claim in an AI answer is supported by the source documents. The small versions are free; the larger 7B is in Ollama but non-commercial.
- Checking RAG bot answers against documents
- Finding unsupported claims in reports and summaries
- Automated quality control of AI answers
- Sizes
- 0.4B – 7B
- Hardware
- from: Laptop
RerankersGGUFNot maintained2023–2024
BAAI (Beijing Academy of Artificial Intelligence) · China
Rerankers: they take passages found by search and reorder them by how well they actually match the question. v2-m3 is multilingual and lightweight, often paired with bge-m3.
- Refining search results before a chatbot answers
- Sorting knowledge base search results
- Selecting the most relevant clauses of contracts and policies
- Sizes
- 278M – 9B
- Hardware
- from: Laptop
Fact-checking and judgesNot maintained2023–2024
Vectara · USA
A small model that checks whether an AI answer is grounded in the source text or made up. Runs on a CPU and works well as a filter in RAG systems.
- Checking knowledge base chatbot answers for fabrications
- Quality control of document summaries
- Comparing language models by their tendency to make errors
- Sizes
- 110M
- Hardware
- from: Laptop
FinanceNot maintained2024
Writer · USA
A large model for financial documents with a context window of about 131k tokens: it holds long reports whole. Not investment advice: decisions are made by a specialist.
- Working with long annual reports and prospectuses
- Summaries and digests of financial documents
- Finding answers inside a large document pack
- Sizes
- 70B (the model card states 72 billion parameters)
- Hardware
- from: Cluster
TextNot maintained2024
Equall · France
Language models for legal texts, fine-tuned on US and European legal corpora (based on Mistral and Mixtral). English only.
- Reviewing English-language contracts
- Spotting risks and non-standard terms
- Drafting legal memos
- Sizes
- 7B – 141B
- Hardware
- from: Laptop
Deepfake detectionNot maintained2023–2024
Sichuan University and co-authors · China
An open model for finding forgeries in images: it outputs a pixel-level mask of altered regions. It errs in both directions; its map is a hint for an expert, not proof of a forgery.
- Finding pasted and erased fragments in photos
- Checking document scans for edits
- A baseline when comparing manipulation-localization models
- Sizes
- a Vision Transformer based model
- Hardware
- from: 1 GPU
Search and RAGRUOllamaNot maintained2024
BAAI · China
A model for meaning-based search in about a hundred languages. The core of RAG: the bot finds the right part of a document before answering.
- Search across a document base
- RAG for a chatbot
- Finding similar requests and duplicates
- Sizes
- 568M
- Hardware
- from: Laptop
Text analysisNot maintained2020–2022
Microsoft · USA
Classic document understanding models: they take into account the text, its position on the page and the image. They are fine-tuned to extract fields from forms and receipts. Only the first version is free for commercial use.
- Extracting fields from questionnaires, forms and receipts after fine-tuning
- Classifying document types
- Answering questions about a scanned page
- Sizes
- about 110M – 370M
- Hardware
- from: Laptop
