title: Expanding Performance Boundaries of Open-Source Multimodal Models with
    Model, Data, and Test-Time Scaling
summary: We introduce InternVL 2.5, an advanced multimodal large language model (MLLM)
  series that builds upon InternVL 2.0, maintaining its core model architecture
  while introducing significant enhancements in training and testing strategies
  as well as data quality. In this work, we delve into the relationship between
  model scaling and performance, systematically exploring the performance trends
  in vision encoders, language models, dataset sizes, and test-time
  configurations. Through extensive evaluations on a wide range of benchmarks,
  including multi-discipline reasoning, document understanding, multi-image /
  video understanding, real-world comprehension, multimodal hallucination
  detection, visual grounding, multilingual capabilities, and pure language
  processing, InternVL 2.5 exhibits competitive performance, rivaling leading
  commercial models such as GPT-4o and Claude-3.5-Sonnet. Notably, our model is
  the first open-source MLLMs to surpass 70% on the MMMU benchmark, achieving a
  3.7-point improvement through Chain-of-Thought (CoT) reasoning and showcasing
  strong potential for test-time scaling. We hope this model contributes to the
  open-source community by setting new standards for developing and applying
  multimodal AI systems. HuggingFace demo see
  https://huggingface.co/spaces/OpenGVLab/InternVL
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paper_title: Expanding Performance Boundaries of Open-Source Multimodal Models with
    Model, Data, and Test-Time Scaling
paper_summary: We introduce InternVL 2.5, an advanced multimodal large language model (MLLM)
  series that builds upon InternVL 2.0, maintaining its core model architecture
  while introducing significant enhancements in training and testing strategies
  as well as data quality. In this work, we delve into the relationship between
  model scaling and performance, systematically exploring the performance trends
  in vision encoders, language models, dataset sizes, and test-time
  configurations. Through extensive evaluations on a wide range of benchmarks,
  including multi-discipline reasoning, document understanding, multi-image /
  video understanding, real-world comprehension, multimodal hallucination
  detection, visual grounding, multilingual capabilities, and pure language
  processing, InternVL 2.5 exhibits competitive performance, rivaling leading
  commercial models such as GPT-4o and Claude-3.5-Sonnet. Notably, our model is
  the first open-source MLLMs to surpass 70% on the MMMU benchmark, achieving a
  3.7-point improvement through Chain-of-Thought (CoT) reasoning and showcasing
  strong potential for test-time scaling. We hope this model contributes to the
  open-source community by setting new standards for developing and applying
  multimodal AI systems. HuggingFace demo see
  https://huggingface.co/spaces/OpenGVLab/InternVL
paper_ai_summary: InternVL 2.5, an advanced multimodal large language model, showcases competitive performance across various benchmarks, including multimodal reasoning and understanding, and is the first open-source model to surpass 70% on the MMMU benchmark using Chain-of-Thought reasoning.
paper_ai_keywords: multimodal large language model vision encoders language models dataset sizes test-time configurations multi-discipline reasoning document understanding multi-image/video understanding real-world comprehension multimodal hallucination detection visual grounding multilingual capabilities pure language processing Chain-of-Thought reasoning test-time scaling MMMU benchmark GPT-4 Claude-3.5-Sonnet