Sudo su(@sudoingX):i can't stop looking at this chart MiniCPM-OCR 1.0 is a 1.3B model built only for reading documents, and it's the one dot in the top right, the highest average score across four document benchmarks and the most pages per second their numbers: > 93.68 on Real5-OmniDocBench > 92.39 on Wild-OmniDocBench > 54.7 on OCRBench v2 > 1.217 pages/s on one rtx 5090 > 1.49x PaddleOCR-VL-1.6 throughput > 3.62x MinerU2.5 Pro throughput the real-world scores are the ones i care about, real documents reach a model as a phone photo of a contract at an angle or a curved page out of a book, and that's where it leads, on the standard OmniDocBench v1.6 it sits 0.4 behind the top ocr is the input for everything after it, agents and rag pipelines are only as good as the text they pull out of a pdf, and one wrong digit on an invoice makes the whole chain confidently wrong that's why the companion model got my attention, MiniCPM-OCR Jev is a 2B checker that reads the page next to any ocr model's output and scores each region, so you know which parts a human still has to check > AUROC 0.936 on JevOCRBench > Qwen3.5-397B-A17B gets 0.938 the parser and the checker are small enough to run on your own gpu, so contracts and receipts never leave your machine, with llama.cpp, vllm, sglang and ollama supported on day one weights and code from @OpenBMB: Hugging Face: MiniCPM-OCR 1.0: https://t.co/p92f5nlDm7 MiniCPM-OCR Jev: https://t.co/2hOy5XCkCz GitHub:

2026.10.27 18:18

i can't stop looking at this chart MiniCPM-OCR 1.0 is a 1.3B model built only for reading documents, and it's the one dot in the top right, the highest average score across four document benchmarks and the most pages per second their numbers: > 93.68 on Real5-OmniDocBench > 92.39 on Wild-OmniDocBench > 54.7 on OCRBench v2 > 1.217 pages/s on one rtx 5090 > 1.49x PaddleOCR-VL-1.6 throughput > 3.62x MinerU2.5 Pro throughput the real-world scores are the ones i care about, real documents reach a model as a phone photo of a contract at an angle or a curved page out of a book, and that's where it leads, on the standard OmniDocBench v1.6 it sits 0.4 behind the top ocr is the input for everything after it, agents and rag pipelines are only as good as the text they pull out of a pdf, and one wrong digit on an invoice makes the whole chain confidently wrong that's why the companion model got my attention, MiniCPM-OCR Jev is a 2B checker that reads the page next to any ocr model's output and scores each region, so you know which parts a human still has to check > AUROC 0.936 on JevOCRBench > Qwen3.5-397B-A17B gets 0.938 the parser and the checker are small enough to run on your own gpu, so contracts and receipts never leave your machine, with llama.cpp, vllm, sglang and ollama supported on day one weights and code from @OpenBMB: Hugging Face: MiniCPM-OCR 1.0: MiniCPM-OCR Jev: GitHub:

OpenBMB@OpenBMB

2026.10.27 16:52

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: 🤗 Hugging Face: MiniCPM-OCR 1.0： MiniCPM-OCR Jev： 👉 Try the Demo： Use the API：