Model reference · open weights
GLM-OCR is an open-weight language model from zai-org, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
GLM-OCR 👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/wechat.jpg" target="blank"WeChat</a and <a href="https://discord.gg/QR7SARHRxK" target="blank"Discord</a community 📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="blank"API</a 👉 <a href="https://github.com/zai-org/GLM-OCR" target="blank"GLM-OCR SDK</a Recommended 📖 <a href="https://arxiv.org/abs/2603.10910" target="blank" Technical Report</a Introduction GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts. Key Features - State-of-the-Art Performance: Achieves a score of 94.62 on OmniDocBench V1.5, ranking #1 overall, and delivers state-of-the-art results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction. - Optimized for Real-World Scenarios: Designed and optimized for practical business use cases, maintaining robust performance on complex tables, code-heavy documents, seals, and other challenging real-world layouts. - Efficient Inference: With only 0.9B parameters, GLM-OCR supports deployment via vLLM, SGLang, and Ollama, significantly reducing inference latency and compute cost, making it ideal for high-concurrency services and edge deployments. - Easy to Use: Fully open-sourced and equipped with a comprehensive SDK and inference toolchain, offering simple installation, one-line invocation, and smooth integration into existing production pipelines. Performance - Document Parsing & Information Extraction - Real-World Scenarios Performance - Speed Test Fo
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | zai-org |
|---|---|
| Type | Language models |
| Parameters (lead) | 1.3B |
| Variants | 1 |
| Runs with | transformers |
| Released | 2026-01-30 |
| Popularity | 2.2M downloads / month |
| Likes | 2,007 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| GLM-OCR | 1.3B | BF16 | ~3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys glm-ocr for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (glm-ocr below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"glm-ocr","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗