InternVL - Open-source multimodal large language model for vision-language tasks

What is InternVL

If you've ever struggled with tasks that require understanding both images and text together, you're not alone. Developers and researchers face this challenge daily—whether it's explaining a screenshot of code, analyzing a chart in a research paper, or extracting data from a photo of a document. That's exactly why we built InternVL (Intern Vision-Language), an open-source multimodal large language model from Shanghai AI Lab.

InternVL represents our vision to make powerful multimodal AI accessible to everyone. By deeply integrating a vision encoder with a large language model, we've created a system that can see, understand, and reason about images just like humans do. The latest version, InternVL3.5-241B-A28B, boasts an impressive 241 billion parameters, making it one of the most powerful open-source multimodal models available today.

What sets InternVL apart is our community-driven approach. This isn't just our product—it's a collaborative effort by researchers and developers worldwide. Whether you're an AI researcher pushing the boundaries of computer vision, a developer building intelligent applications, or a student exploring the future of multimodal AI, InternVL gives you the tools to bring your ideas to life.

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Core Features of InternVL

InternVL comes packed with capabilities designed to handle real-world multimodal challenges. Let us walk you through what this model can do.

Image Understanding & Analysis forms the foundation. InternVL accurately comprehends what's in an image—objects, scenes, relationships, and context. This makes it invaluable for image captioning, content moderation, and visual search applications. Upload a photo of a busy street scene, and InternVL will describe it with remarkable detail.

Visual Question Answering (VQA) lets you ask questions about any image. "What's the error message in this screenshot?" or "What color is the car in this photo?" The model answers precisely, making it perfect for educational tools, accessibility features, and intelligent customer support.

Image Information Extraction handles structured data pull from images—text, tables, numbers, and documents. Use cases include invoice processing, contract analysis, and business card digitization. This transforms static images into actionable, searchable data.

Complex Reasoning goes beyond surface-level understanding. InternVL can analyze mathematical problems, draw logical conclusions from visual scenarios, and perform multi-step reasoning. Students and researchers find this particularly valuable for solving visual puzzles and understanding intricate diagrams.

Multi-Image Comparison analyzes multiple images simultaneously. Compare product variations, detect differences between before-and-after shots, or track changes over time. This is powerful for quality assurance, research, and competitive analysis.

Code Understanding & Generation bridges visual and technical domains. Upload a screenshot of code, and InternVL explains what it does, identifies bugs, or even generates similar code. Developers use this for code reviews, documentation, and rapid prototyping.

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Who Is Using InternVL

InternVL serves a diverse community across multiple domains. Here's how our users are putting this model to work.

Developer Assistance is one of our most popular use cases. When you're stuck on a complex architecture diagram, a confusing UI pattern, or a tricky piece of code in a screenshot, InternVL explains everything. @devcommunity member Chen shares: "I uploaded a screenshot of our microservices architecture and InternVL explained the entire flow in minutes. Saved me hours of digging through documentation."

Education & Learning transforms how students interact with visual materials. Upload a photo of a textbook diagram or a math problem, and get detailed explanations. Parents use it to help children with homework; students use it for self-study and exam preparation.

Content Creation gets a significant boost with InternVL. Generate compelling descriptions, catchy titles, or engaging social media captions from your images. Content creators report saving hours of brainstorming time while discovering new creative angles.

Business Automation handles high-volume image processing at scale. Companies use InternVL to automate extraction from invoices, contracts, and forms—reducing manual data entry by up to 80% and eliminating human error.

Accessibility Assistance makes images accessible to everyone. For visually impaired users, InternVL provides detailed image descriptions that screen readers can convert to speech, enabling independent navigation of visual information.

Research Analysis accelerates academic work. Researchers upload charts, graphs, and experiment images to extract key findings and patterns. What once took hours of manual analysis now happens in seconds.

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Getting Started with InternVL

Ready to try InternVL? We've made it easy to start in minutes, whether you prefer a quick online experience or a self-hosted deployment.

Online Experience (Recommended for Beginners)

The fastest way to explore InternVL is through our online demo at https://chat.intern-ai.org.cn. No setup required—just upload your image and start asking questions. This is perfect for testing capabilities, prototyping ideas, or simply satisfying your curiosity about multimodal AI.

GitHub Repository

For developers ready to dive deeper, visit our GitHub repository at https://github.com/InternLM/InternVL. You'll find complete model weights, training scripts, technical documentation, and examples. The repo includes inference code, fine-tuning guides, and contribution guidelines.

OpenXLAB Model Platform

Researchers can download models directly from OpenXLAB at https://openxlab.org.cn/models/detail/InternVL. This platform provides verified model weights and makes it simple to integrate InternVL into your research pipeline.

Self-Hosted Deployment

For production deployments or custom fine-tuning, you'll need GPU infrastructure. We recommend high-performance GPUs with sufficient VRAM for the model size you choose. Detailed hardware requirements and setup instructions are available in our GitHub documentation.

Basic Workflow

1. Prepare your image (supported formats include JPG, PNG, and more)
2. Input your question or prompt
3. Receive detailed, contextually accurate responses

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Ecosystem & Integration

InternVL doesn't exist in isolation—it's part of a thriving open-source ecosystem that we're building together with the community.

InternLM Family

As a member of the InternLM open-source family, InternVL works seamlessly with InternLM (our language model). Together, they provide complete multimodal AI capabilities—from understanding images to generating human-like text. Many applications benefit from combining both models for end-to-end workflows.

GitHub Community

Our GitHub repository at https://github.com/InternLM/InternVL is the heart of community collaboration. Developers contribute code, report issues, share examples, and help each other troubleshoot. The repository has attracted thousands of stars and active participation from researchers worldwide.

OpenXLAB Integration

InternVL is hosted on OpenXLAB, China's leading open machine learning platform. This provides reliable model hosting, easy downloads, and integration with other open-source models. Researchers can easily compare InternVL with other models on the platform.

Plugin Development

Community members can build custom plugins using our SDK to extend InternVL for specific domains—medical imaging, industrial inspection, satellite analysis, and more. The plugin system lets you tailor the model to specialized use cases without modifying the core model.

API & Enterprise Integration

For organizations, we provide standard API interfaces that integrate with existing systems. Whether you're building a customer service chatbot or automating document processing, InternVL fits into your tech stack with minimal friction.

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Frequently Asked Questions