Documents & OCR
OCR and document-understanding models, the apps that read and answer questions about your files, and parsing libraries and APIs that turn PDFs and scans into structured text, locally or in the cloud.
Models are ranked by OmniDocBench v1.6 (end-to-end, v1.6_full), ParseBench (models), MDPBench (multilingual document parsing), then OCR Arena for models without a result on the first. Tools with their own result on OmniDocBench v1.6 (end-to-end, v1.6_full): pipeline tools are ranked by it (each board ranks one kind of product); other tools inherit the tier of the best model they offer. How we rank.
- Tier: how close to the best result on the task's main public benchmark
- Outlined: a tool inherits the tier of the best model it offers
- No public result yet
- Cloud · Local: runs on your own hardware
Models
The engines themselves. Use them through an API or inside the apps on the right.
Apps
Ready-made apps with a UI: sign in or install, then start.
Parsers & APIs
Libraries and APIs that convert PDFs and scans into Markdown, tables or JSON for your own pipelines.
Quality vs. setup
The same tools as the map, placed by how good the results can be (up) and how much setup they take (right). Top-left is the easiest way to top quality; further right buys control, privacy or price at the cost of setup. Models are left out: you reach them through these tools.
- Cloud
- Local: runs on your hardware
- Hollow: tier inherited from the best model the tool offers
Not placed (no public result yet): MarkItDown, Tesseract OCR, Docling, Marker, OCRmyPDF, OpenDataLoader PDF, Unstructured, LiteParse, Chunkr, PyMuPDF4LLM, ABBYY FineReader PDF, Adobe Acrobat (AI Assistant), Amazon Textract, anyformat, Azure Document Intelligence, ChatGPT, ChatPDF, Claude, Document AI, Extend, Foxit PDF Editor, Gemini, Gemini Notebook (formerly NotebookLM), Humata, LandingAI Agentic Document Extraction, LlamaParse, Mathpix, Nutrient Document Web Services, PDFgear, Pulse, Reducto, Smallpdf, Upstage Document Parse
Everything, in order
| Tier | Name | Approach | Runs | Rated by | Price | Skill |
|---|---|---|---|---|---|---|
| S | XingChen-AGI | Models | Local | 97 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~5 GB VRAM | — |
| S | ATH-MaaS | Models | Local | 96 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~4 GB VRAM | — |
| S | Baidu (PaddlePaddle) | Models | Local | 96 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~4 GB VRAM | — |
| S | OpenDataLab (Shanghai AI Laboratory) | Models | Local | 96 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~4 GB VRAM | — |
| S | Z.ai | Models | Local | 95 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~5 GB VRAM | — |
| S | Baidu | Models | Local | 94 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~8 GB VRAM | — |
| S | Baidu | Models | Local | 94 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~11 GB VRAM | — |
| S | Tencent (Youtu) | Models | Local | 94 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~7 GB VRAM | — |
| S | FireRed Team | Models | Local | 93 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~6 GB VRAM | — |
| S | Baidu (PaddlePaddle) | Parsers & APIs | Local | via PaddleOCR-VL-1.6 | Free | advanced |
| S | MinerU OpenDataLab (Shanghai AI Laboratory) | Parsers & APIs | Local | via MinerU2.5-Pro-2605-1.2B Cloud models: tier S | Free | intermediate |
| A | DeepSeek | Models | Local | 90 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~9 GB VRAM | — |
| A | ByteDance | Models | Local | 90 on OmniDocBench v1.6 (end-to-end, v1.6_full) | ~9 GB VRAM | — |
| – | Microsoft | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | Tesseract OCR project | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | Docling Docling Project (LF AI & Data) | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | Datalab | Parsers & APIs | Local | No public result yet Cloud models: tier A | Free | advanced |
| – | OCRmyPDF project | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | OpenDataLoader project | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | Unstructured | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | LlamaIndex | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | Lumina AI | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | Artifex | Parsers & APIs | Local | No public result yet | Free | advanced |
| – | ABBYY | Apps | Local | No public result yet | — | beginner |
| – | Adobe | Apps | Cloud | No public result yet | $9.99/mo | beginner |
| – | Amazon Web Services | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | anyformat | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | Microsoft | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | Datalab | Models | Local | No public result yet | ~13 GB VRAM | — |
| – | OpenAI | Apps | Cloud | No public result yet | Free tier · $20/mo | beginner |
| – | ChatPDF | Apps | Cloud | No public result yet | Free tier | beginner |
| – | Anthropic | Apps | Cloud | No public result yet | Free tier · $20/mo | beginner |
| – | Anthropic | Models | Cloud | No public result yet | $20/M tokens | — |
| – | Anthropic | Models | Cloud | No public result yet | $0.2/M tokens | — |
| – | Anthropic | Models | Cloud | No public result yet | $8/M tokens | — |
| – | Anthropic | Models | Cloud | No public result yet | $4/M tokens | — |
| – | Google Cloud | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | rednote hi lab (dots studio) | Models | Local | No public result yet | ~8 GB VRAM | — |
| – | Extend | Parsers & APIs | Cloud | No public result yet | Free tier · $500/mo | intermediate |
| – | Technology Innovation Institute (TII) | Models | Local | No public result yet | ~2 GB VRAM | — |
| – | Foxit | Apps | Cloud | No public result yet | $11/mo | beginner |
| – | Google | Apps | Cloud | No public result yet | Free tier · $4.99/mo | beginner |
| – | Google | Models | Cloud | No public result yet | $4.5/M tokens | — |
| – | Google | Models | Cloud | No public result yet | $1.5/M tokens | — |
| – | Google | Apps | Cloud | No public result yet | Free tier | beginner |
| – | Google DeepMind | Models | Local | No public result yet | ~19 GB VRAM | — |
| – | Z.ai | Models | Local | No public result yet | ~185 GB VRAM | — |
| – | OpenAI | Models | Cloud | No public result yet | $20/M tokens | — |
| – | OpenAI | Models | Cloud | No public result yet | $0.2/M tokens | — |
| – | OpenAI | Models | Cloud | No public result yet | $4/M tokens | — |
| – | IBM | Models | Local | No public result yet | ~10 GB VRAM | — |
| – | IBM | Models | Local | No public result yet | ~3 GB VRAM | — |
| – | Baidu (PaddlePaddle) | Models | Local | No public result yet | ~4 GB VRAM | — |
| – | Humata | Apps | Cloud | No public result yet | Free tier · $9.99/mo | beginner |
| – | Tencent (Hunyuan) | Models | Local | No public result yet | ~4 GB VRAM | — |
| – | infly | Models | Local | No public result yet | ~6 GB VRAM | — |
| – | infly | Models | Local | No public result yet | ~72 GB VRAM | — |
| – | Jina AI | Models | Local | No public result yet | ~8 GB VRAM | — |
| – | Moonshot AI | Models | Local | No public result yet | ~1600 GB VRAM | — |
| – | LandingAI | Parsers & APIs | Cloud | No public result yet | Free tier · $250/mo | intermediate |
| – | LightOn | Models | Local | No public result yet | ~11 GB VRAM | — |
| – | LlamaIndex | Parsers & APIs | Cloud | No public result yet | Free tier · $50/mo | intermediate |
| – | Alibaba (Logics-MLLM) | Models | Local | No public result yet | ~3 GB VRAM | — |
| – | Mathpix | Apps + Parsers & APIs | Cloud | No public result yet | Free tier · $4.99/mo | beginner |
| – | Mistral AI | Models | Cloud | No public result yet | — | — |
| – | zenosai (MonkeyOCR team) | Models | Local | No public result yet | ~3 GB VRAM | — |
| – | Nanonets | Models | Local | No public result yet | ~9 GB VRAM | — |
| – | Nutrient | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | Allen Institute for AI (Ai2) | Models | Local | No public result yet | ~12 GB VRAM | — |
| – | PDFgear | Apps | Cloud | No public result yet | Free | beginner |
| – | Pulse AI | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | Alibaba (Qwen) | Models | Local | No public result yet | ~17 GB VRAM | — |
| – | Reducto | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | Smallpdf | Apps | Cloud | No public result yet | Free tier | beginner |
| – | Datalab | Models | Local | No public result yet | ~3 GB VRAM | — |
| – | Upstage | Parsers & APIs | Cloud | No public result yet | Free tier | intermediate |
| – | Tencent | Models | Local | No public result yet | ~10 GB VRAM | — |
Benchmarks used
- OmniDocBench v1.6 (end-to-end, v1.6_full) by OpenDataLab (Shanghai AI Laboratory) (automated tests, Overall page-parsing score: ((1 − text edit distance) × 100 + table TEDS + formula CDM) / 3) · snapshot 11 Sep 2026End-to-end table in the GitHub README (caption 'OmniDocBench (v1.6_full)'); last leaderboard update 2026-09-11 (TeleOCR, OvisOCR2, Unlimited-OCR added); the changelog also notes a v1.7 update on 2026-04-30 but the table is still labelled v1.6_full. 35 rows: 33 models (specialised OCR VLMs and general VLMs) plus 2 pipeline tools moved to omnidocbench-tools. Maintained and broad on open OCR models, but its API/LLM entries are older versions (Gemini 3 Pro/Flash, GPT-5.2, Kimi K2.5, Mistral OCR 2503) and are not tracked. Re-checked 2026-10-19: last repository commit 2026-09-11 (leaderboard update adding TeleOCR, OvisOCR2, Unlimited-OCR); still no current Claude/GPT/Gemini rows.
- ParseBench (models) by LlamaIndex (automated tests, Overall: mean of five rule-based dimension scores (tables, charts, content faithfulness, semantic formatting, visual grounding) on ~2,000 human-verified enterprise pages) · snapshot 9 Oct 2026Model rows of leaderboard.csv in github.com/run-llama/ParseBench (full sortable board at parsebench.ai, also on Kaggle): 66 proprietary VLM rows, 47 open-weight VLM rows and 2 Mistral OCR 4 rows; several rows are effort/thinking settings of one model. Maintained and current: last leaderboard commit 2026-10-09 added LightOnOCR-3; frontier versions (Claude Opus 5.5, Fable 5.1, GPT-6.1 Sol, Gemini 3.8 Flash) are listed. Published by LlamaIndex, whose LlamaParse leads the tools view. Untracked older versions (GPT-5.x, Gemini 3.5-3.7 Flash, DeepSeek V4.1 Flash pre-1017, Mistral OCR 4) are not recorded.
- MDPBench (multilingual document parsing) by MDPBench authors (Yuliang-Liu/MultimodalOCR) (automated tests, Overall: page-level mean of text, formula and table parsing scores on the public split (3,400 images, 17 languages, digital and photographed pages)) · snapshot 4 Oct 202634 model rows (9 general VLMs, 25 specialised OCR VLMs) of leaderboard.json in the Hugging Face space (updated 2026-10-04; additions in Aug-Oct 2026 incl. Kimi K3, U2-OCR, PepperOCR-VL). A held-out private split is also reported per row; the public overall is recorded. Maintained by the group behind MonkeyOCR and OCRBench, so MonkeyOCRv2 rows come from the publisher. Current versions tracked: Kimi K3, MonkeyOCRv2, dots.mocr, Chandra OCR 2, PaddleOCR-VL-1.6, HunyuanOCR-1.5, olmOCR 2, GLM-OCR, Nanonets-OCR2-3B, Falcon-OCR, Unlimited-OCR. Older versions (Gemini 3 Pro preview, Claude Sonnet 4.6, GPT-5.2, Mistral OCR 4.0) and the ambiguous 'MinerU-2.5-Pro-1.2B' row are not recorded.
- OCR Arena by Extend (extend.ai) (blind human votes, Elo rating from blind pairwise human votes on OCR outputs of user-uploaded documents) · snapshot 19 Oct 2026Rows read from the site's JSON endpoint (https://www.ocrarena.ai/api/leaderboard; its 'lastUpdated' equals the fetch time). The model set is frozen: Wayback snapshots of the endpoint from 2026-06-10 and 2026-07-21 list the same 24 entries (newest models: Gemini 3 Flash, Claude Opus 4.6, GPT-5.2, GLM-OCR), while votes continue slowly (per-entry battle counts sum to 33,073 vs 31,795 on 2026-06-10). Ratings are online Elo (K=20) and can move tens of points on a few dozen votes. No current frontier version is listed, so only OCR-specialist entries whose version is current are tracked (GLM-OCR, olmOCR 2, Nanonets-OCR2-3B). Battles per entry: 344 to 2,333.
- OmniDocBench v1.6 (end-to-end, v1.6_full): pipeline tools by OpenDataLab (Shanghai AI Laboratory) (automated tests, Overall page-parsing score: ((1 − text edit distance) × 100 + table TEDS + formula CDM) / 3, ranks tools) · snapshot 11 Sep 2026Rows typed 'Pipeline Tools' in the same table: MinerU-Pipeline (MinerU 3.4.0) and Marker (1.8.2). Other pipelines in the README's model-information table (Docling, PP-StructureV3, Unstructured, Mathpix) have no row on the v1.6 leaderboard. Re-checked 2026-10-19: unchanged.