InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency (2025)
Summary of the InternVL3.5 technical report: a next-generation vision–language model that integrates large-scale visual and textual understanding, achieving strong performance in multimodal reasoning, image captioning, and VQA.
Home › Research Papers › Vision–Language Models › InternVL3.5
Paper:
Overview
InternVL3.5 is an open-source vision–language model designed to push the boundaries of multimodal AI. It combines image and text understanding within a scalable architecture that delivers state-of-the-art results across reasoning, captioning, and visual question answering (VQA). By refining its model design and training strategy, InternVL3.5 achieves a strong balance between versatility, reasoning depth, and computational efficiency.
Key Contributions
Builds on prior InternVL models with improved cross-modal alignment and multimodal fusion.
Scales efficiently to large datasets while maintaining robust generalization across benchmarks.
Offers open-source accessibility, encouraging reproducibility and community-driven innovation.
Demonstrates versatile capabilities, including multimodal reasoning, captioning, retrieval, and visual QA.
Method (high-level)
InternVL3.5 employs a dual-encoder architecture with enhanced interaction mechanisms between image and text embeddings. The system is trained on large-scale multimodal datasets, using alignment objectives that encourage coherent cross-modal understanding. Improvements in data preprocessing, loss balancing, and optimization contribute to both stability and performance.
Results & Applications
InternVL3.5 achieves state-of-the-art performance on a wide range of multimodal benchmarks, including reasoning-heavy tasks such as VQA and multimodal inference. Its efficiency makes it suitable for large-scale deployment while remaining accessible to the open-source community. Potential applications include education (interactive AI tutors), accessibility tools (image-to-text systems for the visually impaired), and advanced research in multimodal understanding.