ME News report, September 19 (UTC+8), Beating AI Bulletin: Jina AI has released the document parsing model jina-ocr-v1, which directly converts PDFs, scanned documents, tables, and charts into Markdown. Built on DeepSeek-OCR through fine-tuning, it retains the MoE architecture with approximately 3.4 billion total parameters and around 570 million activated parameters per token during decoding, while incorporating FastMTP speculative decoding. In Jina’s internal evaluations, jina-ocr-v1 scored 91.14 on OmniDocBench v1.6, 0.89 points higher than DeepSeek-OCR-2, and achieved 83.4 on olmOCR-Bench, 7.4 points higher than its underlying DeepSeek-OCR base model. Throughput is one of Jina’s key selling points: on a single A100 with 32 concurrent requests, it reaches 2.57 pages per second—the highest among the 14 systems tested by Jina, approximately 22% higher than DeepSeek-OCR’s 2.10 pages per second. The model weights are now available on Hugging Face under the CC BY-NC 4.0 license; commercial use requires contacting Jina. (Source: BlockBeats)
Jina AI launches jina-ocr-v1, achieving the highest page throughput in self-testing.
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Summary
Jina AI, a leading voice in AI and crypto news, has launched jina-ocr-v1, a document parsing model that converts PDFs, scanned pages, tables, and charts into Markdown. Built on DeepSeek-OCR and further trained, the model employs a MoE architecture with 3.4 billion total parameters and 5.7 billion active parameters per token. It achieves a score of 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench. On-chain benchmarks show it processes 2.57 pages per second on a single A100 GPU, outperforming DeepSeek-OCR by 22%. Model weights are available on Hugging Face under the CC BY-NC 4.0 license.
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