ernie-4.5-vl-424b-a47b

ERNIE-4.5-VL is a high-capacity multimodal Mixture-of-Experts (MoE) model designed for complex reasoning across text and visual domains. For developers, the standout feature is its architectural efficiency: while boasting 424B total parameters, it only activates 47B per token, optimizing inference latency without sacrificing the depth required for high-level cognitive tasks. Unlike standard vision-language models that often treat images as secondary tokens, this model is trained jointly on interleaved data, making it highly effective for document parsing, visual reasoning, and complex scene understanding. It supports a substantial 123,000 token context window, which is critical for analyzing long-form technical documentation or multi-image workflows. While it operates via API, its performance in structured data extraction and multimodal instruction following positions it as a competitive alternative to leading global frontier models, particularly for enterprise-grade applications requiring precise visual-textual alignment.