InternVL3 78B: Flagship Multimodal Language Model

InternVL3 78B represents the flagship model in OpenGVLab's InternVL3 series, combining a 6B vision transformer with Qwen2.5-72B as the language component. The model demonstrates superior overall performance through integrated multimodal perception and reasoning capabilities, representing a significant advancement in open-source multimodal AI through its native training approach that achieves strong vision-language performance without compromising text-only capabilities.

Architecture and Design

The model follows a proven ViT-MLP-LLM paradigm enhanced with several architectural innovations:

Vision Component:

• InternViT-6B-448px-V2_5 processes images through dynamic resolution tiling
• Pixel Unshuffle reduces visual tokens to one-quarter of original count for computational efficiency
• Variable Visual Position Encoding (V2PE) implements flexible positional increments for improved long-context understanding

Language Integration:

• Qwen2.5-72B serves as the language backbone
• Native integration enables simultaneous multimodal representation development
• Maintains strong text-only performance despite multimodal training

Multi-modal Support:

• Handles images with dynamic resolution processing
• Processes video sequences with temporal understanding
• Supports interleaved image-text sequences for complex conversations

Advanced Training Methodology

Native Multimodal Pre-Training: A distinguishing characteristic is the consolidation of language and vision learning into a single pre-training stage, rather than sequentially adapting language models to vision. This approach enables simultaneous development of multimodal representations, resulting in more cohesive understanding across modalities.

Mixed Preference Optimization (MPO): Addresses distribution shift between training (ground-truth tokens) and inference (model-predicted tokens) by incorporating preference signals during training. This methodology enhances reasoning capabilities and reduces exposure bias during generation.

Test-Time Scaling: Employs Best-of-N evaluation with VisualPRM-8B as a critic model for reasoning and mathematics tasks, enabling quality-optimized inference for applications requiring high accuracy.

Benchmark Performance

InternVL3 78B excels across diverse evaluation categories:

• Multimodal Reasoning: Superior performance on mathematical and visual reasoning benchmarks
• Document Understanding: Strong OCR, chart interpretation, and document analysis capabilities
• Video Comprehension: Effective temporal understanding of video sequences
• GUI and Spatial Reasoning: Advanced interface grounding and spatial analysis
• Language Performance: Outperforms base Qwen2.5 models on text-only tasks despite multimodal training focus

The model's ability to exceed text-only baseline performance while maintaining multimodal capabilities demonstrates the effectiveness of native multimodal training approaches.

Key Capabilities

The model demonstrates exceptional performance across multiple domains:

Image Analysis:

• Single and multi-image conversations with detailed descriptions
• Fine-grained visual understanding and attribute recognition
• Complex scene comprehension and relationship analysis

Document Processing:

• Optical character recognition across diverse formats
• Chart and diagram interpretation with data extraction
• Technical documentation understanding

Video Understanding:

• Frame-by-frame analysis with temporal coherence
• Event detection and narrative comprehension
• Long-form video summarization

Agent Applications:

• GUI navigation and interface interpretation
• Tool usage coordination for autonomous agents
• Spatial reasoning for robotic applications

Industrial Applications:

• 3D vision perception and depth understanding
• Specialized image analysis for domain-specific tasks

Use Cases

InternVL3 78B excels in applications requiring sophisticated multimodal understanding:

• Visual question answering across diverse domains
• Document analysis and information extraction
• Video content understanding and summarization
• GUI automation and interface interaction
• Scientific visualization interpretation
• Educational content analysis
• Medical image interpretation with contextual analysis
• Industrial quality inspection with visual reasoning
• Autonomous agent development requiring visual understanding
• Technical documentation processing

Deployment and Integration

The model supports flexible deployment through multiple frameworks:

• Transformers Library: Standard integration (requires version 4.37.2+)
• LMDeploy: Production-optimized deployment with RESTful API compatibility
• Quantization Support: BF16, FP16, and 8-bit quantized variants for efficiency
• Multi-GPU Support: Distributed inference for accelerated processing

Technical Considerations

The native multimodal pre-training approach distinguishes InternVL3 78B from models that adapt pre-trained language models to vision tasks. This methodology enables more cohesive cross-modal understanding, as evidenced by the model's ability to outperform text-only baselines while maintaining strong multimodal performance.

The V2PE and Pixel Unshuffle innovations reduce computational requirements for long visual sequences, making the model practical for applications requiring analysis of high-resolution images or extended video content. Test-time scaling with critic models provides an additional quality lever for accuracy-critical applications.