Paddleocr VL 0.9b
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Paddleocr VL 0.9b

paddleocr-vl-0.9bllms.txt
Baidu
PaddleOCR-VL is an advanced and efficient document parsing model specifically designed for element recognition within documents. Its core component, PaddleOCR-VL-0.9B, is a compact yet powerful vision-language model (VLM) composed of a NaViT-style dynamic resolution visual encoder and the ERNIE-4.5-0.3B language model, enabling precise element recognition. This model supports 109 languages and excels at recognizing complex elements such as text, tables, formulas, and charts while maintaining extremely low resource consumption. Through comprehensive evaluations on widely used public benchmarks and internal benchmarks, PaddleOCR-VL achieves state-of-the-art (SOTA) performance in both page-level document parsing and element-level recognition. It significantly outperforms existing pipeline-based solutions, multimodal document parsing approaches, and advanced general-purpose multimodal large models, while also offering faster inference speed.

Pricing

$0.025/page

Image Generation

Quality
standard
standard
$0.025

Input Modalities

    Output Modalities

    • Text

    Providers

    Baidu paddleocr-vl-0.9b
    Pricing

    $0.025/page

    Context0
    Max output0
    Latency-
    Throughput-
    Uptime
    0.00% uptime 2 days ago
    0.00% uptime yesterday
    0.00% uptime today

    Performance for paddleocr-vl-0.9b

    Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

    Uptime
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    Latency
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    Throughput
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    Frequently asked questions

    What is Paddleocr VL 0.9b?

    PaddleOCR-VL is an advanced and efficient document parsing model specifically designed for element recognition within documents. Its core component, PaddleOCR-VL-0.9B, is a compact yet powerful vision-language model (VLM) composed of a NaViT-style dynamic resolution visual encoder and the ERNIE-4.5-0.3B language model, enabling precise element recognition. This model supports 109 languages and excels at recognizing complex elements such as text, tables, formulas, and charts while maintaining extremely low resource consumption. Through comprehensive evaluations on widely used public benchmarks and internal benchmarks, PaddleOCR-VL achieves state-of-the-art (SOTA) performance in both page-level document parsing and element-level recognition. It significantly outperforms existing pipeline-based solutions, multimodal document parsing approaches, and advanced general-purpose multimodal large models, while also offering faster inference speed.