InternVL 2.5

InternVL 2.5 is a family of advanced multimodal large language models based on InternVL 2.0, which introduces significant enhancements in training and testing strategies and data quality while maintaining the core model architecture. This model provides an in-depth look at the relationship between model scaling and performance, systematically exploring performance trends for visual encoders, language models, dataset sizes, and test-time configurations. Through extensive evaluation on a wide range of benchmarks including multi-disciplinary reasoning, document understanding, multi-image/video understanding, real-world understanding, multi-modal hallucination detection, visual localization, multi-language capabilities and pure language processing, InternVL 2.5 has demonstrated competitiveness on par with leading commercial models such as GPT-4o and Claude-3.5-Sonnet. In particular, the model is the first open source MLLM to exceed 70% on the MMMU benchmark, achieve a 3.7 percentage point improvement via chain-of-thinking (CoT) inference, and demonstrate strong potential for test-time scaling.

The target audience is researchers, developers and enterprises, who need a powerful multi-modal AI system to process and understand large amounts of visual and language data. InternVL 2.5 helps them improve the efficiency and accuracy of data processing by providing advanced model architecture and optimized training strategies, thus promoting the development and application of artificial intelligence technology.