Your OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly.

🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results.

Two new models:
🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast.
🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench.

⚡ What’s new?
Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2.

A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8).

Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute.

Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS.

Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills.

🔗 GitHub: http://github.com/OpenBMB/MiniCPM-OCR
🤗 Hugging Face:
MiniCPM-OCR 1.0：http://huggingface.co/openbmb/MiniCPM-OCR-1.0
MiniCPM-OCR Jev：http://huggingface.co/openbmb/MiniCPM-OCR-Jev
👉 Try the Demo：http://platform.modelbest.ai/console/playground/document-parse
Use the API：http://platform.modelbest.ai/console/docs/models/document-parse