Why Multimodal Models Are the Next Step

Published on: July 24, 2026 Author: Ivo Reading time: ~2 min

The evolution of artificial intelligence is moving rapidly away from purely text-based interfaces. While traditional Large Language Models (LLMs) excel at generating and analyzing text, the rise of multimodal models represents the true paradigm shift. These systems understand and process text, images, video, and audio simultaneously, mimicking human perception much more closely.

From Coupled to 'Native' Multimodal

Earlier iterations of AI often combined separate models via APIs. A speech-to-text model provided input to a text model, after which a separate image generator visualized the result. This approach leads to a loss of nuance: intonation in speech or subtle context in an image is lost in translation to text.

The current generation of foundation models [to be verified: specific reference to the current state-of-the-art benchmarks of models such as Gemini 1.5 Pro or similar in July 2026] is "natively multimodal". They are trained from the ground up on various simultaneous data streams, resulting in a direct and seamless understanding of multimedia context without intermediate steps.

Tip: Would you like to better understand the underlying transformer architecture before diving into multimodal concepts? Then visit our learning module at leren.llmnet.nl.

Practical Applications

The ability to 'look' and 'listen' at the same time opens doors to completely new applications:

Healthcare: Systems can analyze medical scans in the direct context of spoken doctor's notes and written patient records [to be verified: current clinical approvals or studies regarding multimodal AI in diagnostics].
Software Development: Generate working frontend code directly from a rough sketch on a whiteboard or a screenshot, including the corresponding accessibility descriptions.
Customer Service: Real-time visual troubleshooting where an AI looks along via the user's camera and gives live spoken instructions on repairing equipment.

Implications and Challenges

Despite the enormous potential, multimodal models bring significant challenges. The required computing power (compute) for training and inferring video and audio is exponentially larger than for pure text. In addition, concerns around safety are increasing. The ability to generate flawless, context-aware deepfake audio or video in real-time requires robust safety mechanisms and detection software [to be verified: current European AI Act legislative status or technical mitigation strategies as of summer 2026].

Nevertheless, the transition is irreversible. AI that can perceive the world the way we do marks the beginning of much more intuitive and context-driven technology.