Multimodal AI refers to artificial intelligence that can process different types of information, such as text, images, audio or video, and relate them to one another. A multimodal AI system can, for example, analyse a photograph, understand a question asked about it, and take both pieces of information into account together to provide an answer.
What is crucial here is not merely the ability to process different types of data. Multimodal systems can integrate information from multiple modalities within a single task. This makes them suitable for applications where relevant information is contained simultaneously in text, images, speech, videos or other data sources.
An AI is described as multimodal if it can process information from several different modalities. Typical modalities include written text, spoken language, images, video recordings and sensor data.
Multimodal AI is particularly relevant in situations where information is not available in isolation in a single data format. Technically, multimodality describes an AI system’s ability to process different forms of information within a single task and to link them together.
Put simply, a modality is a specific form of information. Text is one modality; an image is another. Speech, sounds, videos or sensor data can also represent distinct modalities, depending on the system.
Which modalities an AI supports depends on the specific model and its technical implementation. A system therefore does not need to be able to process text, images, speech and video simultaneously to be considered multimodal. Even the joint processing of, for example, text and images constitutes a form of multimodality.
For example, a person might take a photograph of a damaged component and ask:
“What could be faulty here?”
A purely text-based AI would not be able to incorporate the image directly into its analysis. A multimodal system, on the other hand, can take both the image information and the text-based question into account. The same principle can be applied to other tasks. For example, a system can:
Different types of data must first be processed into a format that the AI model can work with. Text, images and audio data each have different structures and therefore require different methods of preparation.
In the first step, the system processes the respective inputs. With text, for example, words, contextual meaning and sentence structures may be relevant. With images, the focus is on objects, shapes, spatial relationships or visible text, amongst other things. Audio data may contain speech, sounds or temporal patterns.
The inputs are converted into mathematical representations that can be further processed by the model.
The key step involves linking information from different modalities. If, for example, a system receives a product photograph along with the question ‘What colour is the left-hand button?’, it must recognise that the verbal formulation refers to a specific area of the image.
Depending on the technical implementation, such linkages are referred to, amongst other things, as multimodal fusion or cross-modal processing. The specific architecture varies between different models.
Based on the linked information, the system can then carry out a task. This includes, for example:
However, using multiple modalities does not automatically lead to more accurate results. A blurry image, an ambiguous question or contradictory information can compromise the quality of the output.
| Term | Key characteristic | Simple example |
|---|---|---|
Term | Key characteristic | Simple example |
Unimodal AI | primarily processes a single modality | Classifying text |
Multimodal AI | processes information from multiple modalities | Image + question → answer |
Generative AI | generates new content | Generating text, images or audio |
Large Language Model (LLM) | is designed primarily to process and generate language | Understanding and generating text |
Vision Language Model (VLM) | combines visual and language information | Analyzing an image and answering questions about it |
Multimodality describes the different forms of information that a system can process or combine. Generative AI, on the other hand, describes the ability to generate new content.
An AI system can therefore be both multimodal and generative at the same time. Furthermore, LLMs and multimodal systems are not fundamentally mutually exclusive. Language models can be enhanced with capabilities for processing additional modalities or developed with these capabilities from the outset.
Generative AI refers to systems that generate new content. Multimodality refers to the processing of different types of information. A system may possess both characteristics, but they are not synonymous.
An AI does not have to support every conceivable modality. Even a system that processes text and images together, for example, can be multimodal.
Additional sources of information can provide more context. However, misinterpretations, hallucinations or inadequate input data remain possible.
In a multi-model system, several AI models work together within a single application or process. Multimodality, on the other hand, describes the ability to process different types of information. Both concepts can occur in combination, but they are not identical.
As well as continuous text, business documents may contain, for example, tables, diagrams, forms, stamps or images. Multimodal systems can take various components of a document into account collectively and, for example, answer questions about the document or extract information from it.
An AI can recognise and describe visible content and combine it with linguistic information. In the case of videos, this is supplemented by temporal sequences and changes between individual frames.
Multimodal systems can link spoken content with additional information. For example, an assistance system can process a voice command whilst simultaneously taking visual information into account.
Image data from products or machines can be combined with technical data, production information or sensor measurements. This allows different sources of information to be taken into account together within a testing or analysis process.
In addition to a written question, users can, for example, provide photos, screenshots or documents. This gives an AI system access to information that would otherwise need to be described in detail in text form.
Using more modalities does not automatically increase the reliability of an AI system. Errors can arise either during the processing of individual inputs or only when these are combined. For example, a model may misidentify an object in an image and, based on this, generate a response that is linguistically plausible but factually incorrect.
Data quality therefore plays a key role. Blurry images, poor-quality audio recordings, a lack of context or contradictory information can complicate processing.
Other potential challenges include:
Where photos, audio recordings, videos or internal documents are processed, they may contain personal or confidential information. Companies must therefore, amongst other things, assess what data is being processed, for what purpose this is being done, and what legal and organisational requirements apply.
Multimodality expands a system’s available information base. However, the fundamental limitations of AI-based results remain.
Well-known multimodal model systems include, amongst others, Google Gemini and the latest models from OpenAI’s GPT family.
However, which input and output formats are actually available in practice does not depend solely on the underlying model. The specific product, the API, the pricing plan and the technical integration can also influence the scope of functionality. It is therefore important to distinguish between the fundamental capabilities of a model and the features of a specific AI service.
Business information is often available in various data formats. Invoices, for example, contain text and tables; technical documentation also includes drawings; production processes generate image and sensor data; and customers submit screenshots, documents or voice recordings. Multimodal systems can process such information within shared processes.
Possible areas of application include, amongst others:
Whether a multimodal solution is appropriate depends on the specific process. Particular factors to consider include the available data, the required level of accuracy, data protection requirements, the effort involved in integration and the costs.
Multimodal AI extends artificial intelligence to include the ability to interpret different types of data in conjunction with one another. Rather than considering text, images or audio in isolation, such systems can establish connections between multiple sources of information.
This gives rise to applications that are more closely aligned with real-world information scenarios – ranging from document analysis and visual assistance systems to industrial quality control. At the same time, data quality, data protection, computational complexity and potential misinterpretations remain key limitations. Multimodality is therefore neither synonymous with generative AI nor automatically a mark of quality. It primarily describes a technical capability: the ability to process multiple types of information within a shared context.
Depending on the model used and the available product features, ChatGPT can process various types of information. Current OpenAI models support, among other things, text and image inputs, as well as vision capabilities. The specific features available in ChatGPT may vary depending on the model, pricing plan, and product version.
Generative AI refers to systems that can generate new content such as text, images, or audio. Multimodal AI refers to systems that process and combine multiple types of information. An AI system can possess both of these characteristics at the same time.
Yes. A vision-language model combines at least visual and linguistic information, making it a form of multimodal AI. For example, it can process an image along with a text-based question.
Depending on the model, the system can process text, images, audio, video, code, structured data, sensor data, and more. The combinations supported vary from system to system.
No. Multimodality primarily refers to the processing of different types of information. For example, a system can analyze or classify multimodal data without generating new content itself.
Multimodal AI refers to the processing of different modalities. A multi-model system, on the other hand, refers to an architecture in which multiple models are combined. These models may be multimodal, but they do not have to be.
