How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements. (1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model - InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448x448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. (3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition (OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://github.com/OpenGVLab/InternVL.