--- title: "GLM-OCR" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c0b20be3-5218-48b0-8e02-a88fff37c976" updated: "2026-11-12" --- # GLM-OCR > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads text, formulas and tables from document images and pulls structured information out of them, in eight languages. A small OCR model from zai-org for developers and document pipelines. GLM-OCR is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by zai-org ## About ### What it is GLM-OCR is an OCR model from zai-org that reads complex documents. It recognizes text, formulas and tables, and it can extract structured information such as ID details when you give it a JSON-shaped prompt. The card describes a two-stage design, with layout analysis first and parallel recognition after it. ### Who it suits Developers who build document-reading services and need a small, open OCR model that handles tables and formulas. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Business operations | Back-office teams | Read forms, tables and seals | A scanned invoice read into text | | Software development | Pipeline developers | Add OCR to a document service | A service that turns page images into Markdown | | Research and education | Students and researchers | Read formulas and tables from papers | A formula from a scanned page | | Public services | Records teams | Pull details from ID documents | Extracting fields from an ID scan | If you only read documents that are already text, you can skip it. ### Where to use it - Python with the transformers library on Windows, macOS or Linux - Local servers run with vLLM, SGLang or Ollama, which the card says it supports - The maker's official SDK for document parsing, and its hosted API | Use case | Who | What they get back | | --- | --- | --- | | Document parsing | Developers | Raw text, formulas and tables from a page | | Information extraction | Business teams | Structured fields in a JSON shape you define | | High-volume services | Platform teams | Fast page-by-page reading on small hardware | ### How to set it up - Python and pip - About 2.7 GB of disk space in BF16, or about 663 MB at 4-bit (an estimate) - Optional: the official SDK, which the card recommends for document parsing 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Run the model with transformers, vLLM or SGLang, following the model card. ```sh pip install -U huggingface_hub hf download zai-org/GLM-OCR ``` ### How to use it For document parsing, the card recommends the official SDK, which adds layout analysis and structured output. For information extraction, run the model directly with a prompt that follows a strict JSON schema. For parsing, the card lists three prompt types. ```python { "text": "Text Recognition:", "formula": "Formula Recognition:", "table": "Table Recognition:" } ``` | Tool | What it does | | --- | --- | | Text Recognition prompt | Pulls raw text from a document page | | Formula Recognition prompt | Pulls formulas from a document page | | Table Recognition prompt | Pulls tables from a document page | | Official SDK | Parses documents with layout analysis and structured output | ### Example You give the model a page image with the table prompt. It returns the page's table content as text. For an ID document, you send a JSON-shaped prompt and get the requested personal details back as structured data. ### For newcomers and experts If you are new to OCR, try the official SDK on one scanned page before you serve the model yourself. Check the output against the page by eye. The model pairs a CogViT visual encoder with a small cross-modal connector and a GLM-0.5B language decoder. The card cites 0.9B parameters, while Hugging Face's record lists 1.3B. Training used multi-token prediction loss and reinforcement learning. The card reports 94.62 on OmniDocBench V1.5. A page image goes in → Layout analysis finds the regions → The model reads each region in parallel → Text, formulas or tables come out ### Good to know - Prompts: only two prompt scenarios are supported, parsing and information extraction - SDK: it is built for document parsing only, so extraction runs on the model directly - Benchmarks: the figures are the maker's own Reading speed in the maker's own speed test | Label | Value | | --- | --- | | PDF input | 1.86 pages/second | | Image input | 0.67 images/second | - License: mit - Cost to run: the local model needs no key, and the hosted API is named without prices - Data it reaches: the local model reads the files you give it, while the hosted API receives your documents - Not for: free-form chat, since only the two prompt scenarios are supported ## README ````markdown # GLM-OCR Reads text, formulas and tables from document images and pulls structured information out of them, in eight languages. A small OCR model from zai-org for developers and document pipelines. Made by zai-org on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: GLM-OCR is an OCR model from zai-org that reads complex documents. It recognizes text, formulas and tables, and it can extract structured information such as ID details when you give it a JSON-shaped prompt. The card describes a two-stage design, with layout analysis first and parallel recognition after it. Who it suits: Developers who build document-reading services and need a small, open OCR model that handles tables and formulas. ```table Industry | Who uses it | How they use it | Example Business operations | Back-office teams | Read forms, tables and seals | A scanned invoice read into text Software development | Pipeline developers | Add OCR to a document service | A service that turns page images into Markdown Research and education | Students and researchers | Read formulas and tables from papers | A formula from a scanned page Public services | Records teams | Pull details from ID documents | Extracting fields from an ID scan ``` If you only read documents that are already text, you can skip it. Where to use it: - Python with the transformers library on Windows, macOS or Linux - Local servers run with vLLM, SGLang or Ollama, which the card says it supports - The maker's official SDK for document parsing, and its hosted API ```table Use case | Who | What they get back Document parsing | Developers | Raw text, formulas and tables from a page Information extraction | Business teams | Structured fields in a JSON shape you define High-volume services | Platform teams | Fast page-by-page reading on small hardware ``` How to set it up: - Python and pip - About 2.7 GB of disk space in BF16, or about 663 MB at 4-bit (an estimate) - Optional: the official SDK, which the card recommends for document parsing 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Run the model with transformers, vLLM or SGLang, following the model card. ```sh pip install -U huggingface_hub hf download zai-org/GLM-OCR ``` How to use it: For document parsing, the card recommends the official SDK, which adds layout analysis and structured output. For information extraction, run the model directly with a prompt that follows a strict JSON schema. For parsing, the card lists three prompt types. ```python { "text": "Text Recognition:", "formula": "Formula Recognition:", "table": "Table Recognition:" } ``` ```table Tool | What it does Text Recognition prompt | Pulls raw text from a document page Formula Recognition prompt | Pulls formulas from a document page Table Recognition prompt | Pulls tables from a document page Official SDK | Parses documents with layout analysis and structured output ``` Example: You give the model a page image with the table prompt. It returns the page's table content as text. For an ID document, you send a JSON-shaped prompt and get the requested personal details back as structured data. For newcomers and experts: If you are new to OCR, try the official SDK on one scanned page before you serve the model yourself. Check the output against the page by eye. The model pairs a CogViT visual encoder with a small cross-modal connector and a GLM-0.5B language decoder. The card cites 0.9B parameters, while Hugging Face's record lists 1.3B. Training used multi-token prediction loss and reinforcement learning. The card reports 94.62 on OmniDocBench V1.5. ```flow A page image goes in Layout analysis finds the regions The model reads each region in parallel Text, formulas or tables come out ``` Good to know: - Prompts: only two prompt scenarios are supported, parsing and information extraction - SDK: it is built for document parsing only, so extraction runs on the model directly - Benchmarks: the figures are the maker's own ```chart Title: Reading speed in the maker's own speed test PDF input: 1.86 pages/second Image input: 0.67 images/second ``` - License: mit - Cost to run: the local model needs no key, and the hosted API is named without prices - Data it reaches: the local model reads the files you give it, while the hosted API receives your documents - Not for: free-form chat, since only the two prompt scenarios are supported ## On Hugging Face, 13 November 2026 - Task: image to text - Library: transformers - Parameters: 1.3B - Weights: about 2.7 GB in BF16; about 663 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,793,299 - Likes: 2,215 - License: mit - Languages: zh, en, fr, es, ru, de, ja, ko - Quantized versions: 41 - Fine-tunes: 38 - Tags: transformers, safetensors, glm_ocr, image-text-to-text, conversational, image-to-text ## Links - Model: https://huggingface.co/zai-org/GLM-OCR - Files: https://huggingface.co/zai-org/GLM-OCR/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download zai-org/GLM-OCR ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [zai-org/GLM-OCR on Hugging Face](https://huggingface.co/zai-org/GLM-OCR) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | GLM-OCR (this page) | 1,793,299 | 2,215 | 1.3B | mit | 2026-09-11 | | [Falcon OCR](https://mindket.com/p/6c3d18f2-91e5-4831-b338-824d98dfa214) | 3,667 | 178 | 270M | apache-2.0 | 2026-10-26 | | [indra-ocr](https://mindket.com/p/ffb3991c-77ab-4a5e-b2ca-042edbc0b406) | 8,624 | 529 | 1.3M | openrail | 2026-10-19 | | [anaksor-ocr](https://mindket.com/p/8371c1ec-4e07-4825-a6e0-cda18f0917d4) | 61 | 4 | — | apache-2.0 | 2026-11-07 | | [PP-OCRv6\_small\_rec](https://mindket.com/p/d7e2f0fd-1700-44b5-8b6f-5ef327223422) | 10,322 | 17 | — | apache-2.0 | 2026-06-12 | | [PP-OCRv6\_manga](https://mindket.com/p/1ffe1df9-703d-4ee9-9cd8-e9bf6b756171) | 437 | 14 | — | apache-2.0 | 2026-11-06 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · phenomen · 2026-08-24](https://news.ycombinator.com/item?id=49421088): OCR It – pull text out of un-copyable documents for your LLM: I tested many open-source and hosted OCR models and Datalab Chandra was the most accurate. It can parse complex layouts, tables, handwriting, and formulas at a fraction of the cost of Claude/Gemini. Local: https://github.com/datalab-to/chandra Hosted: https://www.datalab.to Another decent option… - [Bluesky · Adina Yakup · 2026-02-03](https://bsky.app/profile/adinayakup.bsky.social/post/3mdwvwbusa22y): GLM just entered the OCR field🔥 huggingface.co/zai-org/GLM-... ✨ 0.9B ✨ MIT licensed ✨ Multimodal GLM-V architecture ✨ \#1 on OmniDocBench v1.5 (94.62) - [Hacker News · cpnwaugha · 2026-08-20](https://news.ycombinator.com/item?id=49376554): Roboflow Playground: Try and Compare 30 Computer Vision Models: Formatting the command block for easy copy-paste. Via the CLI: pip install vlmrun vlmrun gw models vlmrun config set --api-key 'vlmrun' \# anon-user, rate-limited vlmrun gw chat \.pdf -m zai-org/glm-ocr vlmrun gw chat \.pdf -m zai-org/glm-ocr --json-mode vlmrun gw chat \.pdf -m… - [Bluesky · Tom Dörr · 2026-04-06](https://bsky.app/profile/tom-doerr.bsky.social/post/3mis6bt5e5x2l): https://github.com/zai-org/GLM-OCR - [Hacker News · cpnwaugha · 2026-08-20](https://news.ycombinator.com/item?id=49374471): GPT 5.6 Sol is the best "vision" model OpenAI ever released: Yeah, GPT 5.6 Sol is very good. Generally, Gemini models remain SOTA for VLM tasks with 3.7-flash at the top. --- That said, considering variables like cost (say, over 100k PDF pages) and accuracy requirements (e.g., construction documents with dense images \& tables), Gemini and other SOTA VLMs… - [Bluesky · Hacker News 250 Points · 2026-02-12](https://bsky.app/profile/newsyc250.bsky.social/post/3menchljmpu2j): GLM-OCR – A multimodal OCR model for complex document understanding https://github.com/zai-org/GLM-OCR (http://news.ycombinator.com/item?id=46924075) - [Hacker News · cpnwaugha · 2026-08-20](https://news.ycombinator.com/item?id=49373639): Roboflow Playground: Try and Compare 30 Computer Vision Models: With a one-line change, you can switch between open-weight OCR VLMs (DeepSeek OCR 2, GLM-OCR, dots.mocr, Paddle OCR VL, PP-OCRv6, etc.) and process 100K+ pages for under $60 on VLM Run Gateway \> one OpenAI-compatible endpoint for open-weight OCR and VLM models Try it out quickly via OpenAI SDK:… - [Bluesky · Hacker News 200 Points · 2026-02-11](https://bsky.app/profile/newsyc200.bsky.social/post/3memjv64bxf2c): GLM-OCR: Accurate × Fast × Comprehensive https://github.com/zai-org/GLM-OCR (http://news.ycombinator.com/item?id=46924075) - [Hacker News · philipkglass · 2026-04-06](https://news.ycombinator.com/item?id=47663485): Show HN: I Built Paul Graham's Intellectual Captcha Idea: At least one of the test questions was just a screen shot from a tweet. It was difficult to read. I'd suggest extracting text from screen shots with OCR. Apple has built-in functionality for this on their operating systems with Live Text. There are strong open source systems based on small vision… - [Bluesky · Hacker News Top Stories · 2026-02-11](https://bsky.app/profile/hackernewsbot.bsky.social/post/3melsrta45k27): GLM-OCR: Accurate × Fast × Comprehensive \| Discussion - [Hacker News · philipkglass · 2026-04-02](https://news.ycombinator.com/item?id=47619948): Google releases Gemma 4 open models: If you have a basic ARM MacBook, GLM-OCR is the best single model I have found for OCR with good table extraction/formatting. It's a compact 0.9b parameter model, so it'll run on systems with only 8 GB of RAM. https://github.com/zai-org/GLM-OCR Use mlx-vlm for inference:… - [Bluesky · Cartero · 2026-02-11](https://bsky.app/profile/news.karthihegde.dev/post/3melrestsso2l): GLM-OCR: Accurate × Fast × Comprehensive Discussion \| hackernews \| Author: ms7892 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-09-11 - First published: 2026-01-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open zai-org/GLM-OCR on Hugging Face](https://huggingface.co/zai-org/GLM-OCR) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c0b20be3-5218-48b0-8e02-a88fff37c976) - [Its page on Mindket](https://mindket.com/p/c0b20be3-5218-48b0-8e02-a88fff37c976)