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.
- 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
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
Visual document searchGGUF2026
Tencent · China
Tencent models based on Qwen3.5 for searching scans and PDFs as images. According to the model card, among the top of the ViDoRe leaderboard at release.
- Search across scans and PDFs without OCR
- RAG over reports with tables and charts
- Search across document archives
- Sizes
- 4.5B – 8B
- Hardware
- from: 1 GPU
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
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
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
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
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; Russian scored 88.8% in benchmarks.
- 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
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. 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
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 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.
- 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
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
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
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
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
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
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 is also available.
- Transcribing and summarizing recordings
- Asking questions about audio
- Real-time recognition
- Sizes
- 3B – 24B
- 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
