Natively multimodal
Models designed from the start to understand and generate multiple types of data like text, images, and audio together.
Natively multimodal means a model was built from the beginning to handle different kinds of information at the same time. Think of it like a person who can read a book, look at the pictures, and listen to an audiobook all at once. This is different from older systems that might have separate tools for text and images, and had to switch between them.
For example, a natively multimodal AI could look at a photo of a meal and tell you what ingredients are in it, suggest a recipe, and even write a story about eating that meal. This integrated approach allows for a deeper understanding and more creative outputs.
Natively multimodal models are architected to process and generate information across diverse data modalities (text, image, audio, video, etc.) within a single, unified framework. This contrasts with earlier approaches that often relied on separate models for each modality, connected by fusion layers or late-stage integration.
The advantage lies in the potential for more seamless interaction and emergent capabilities, as the model learns joint representations of different data types. Tradeoffs can include increased complexity in training and architecture design, and potential challenges in balancing performance across all modalities.
Frontier AI labs are increasingly developing models that can process and generate multiple types of data, such as text, images, and audio, simultaneously. This shift towards natively multimodal capabilities allows for more integrated and sophisticated AI applications that can understand and interact with the world in a more human-like way. The recent focus on these models highlights a move beyond single-task AI to more versatile and powerful systems.
- spark Gemini 3.7 FlashGoogle · Gemini Models ·
- A natively multimodal model capable of precise, accurate, photorealistic outputsOpenAI · OpenAI Research ·
- Introducing our latest image generation model in the APIOpenAI · OpenAI News ·
- Google Gemini
- Google Gemini's latest multimodal reasoning models are built to handle various data types from the start.
- OpenAI ChatGPT
- OpenAI's models are being developed with integrated capabilities to process and generate different kinds of information, like text and images, at the same time.
- Multimodal AI
- This is a broader category for AI that handles multiple data type
- Late Fusion Models
- These models combine outputs from separate unimodal models,
The term 'natively multimodal' emerged within the AI research community to distinguish models designed from their core architecture to handle multiple data types simultaneously, rather than integrating separate unimodal systems.
Use an AI chatbot that supports image input. Upload a picture and ask it to describe the image, identify objects, or even write a story based on what it sees. This demonstrates the AI's ability to process visual information alongside your text prompts.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
- HeuristicDouble coding agent task completion with docs linksProviding coding agents with a documentation link significantly increases their task completion rate. · 2 min
Questions people ask
- What's the difference between multimodal and natively multimodal?
- Multimodal AI can process different types of data, but 'natively multimodal' means the AI was built from the ground up to handle these data types together seamlessly.
- Can natively multimodal models handle audio and video too?
- Yes, the concept applies to any combination of data types, including text, images, audio, and video, if the model is designed for them.
First seen on Subvolts Apr 23, 2025. Updated Sep 17, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.