The basicsAI glossary
Multimodal
A model that understands and/or generates multiple types of content: text, images, audio, video. Recent models are almost all multimodal.
In other words
Someone who reads, watches, listens AND draws, not just a reader.
Alyriaz articles that mention it
- Why benchmarks age so quickly
Benchmarks lose their discriminating power within a few months. We explain the three mechanisms that wear them down, and how to read a score accordingly.
- Ox Alpha revealed: it was GLM-5.3-Flash, and it runs entirely on Chinese chips
Z.ai confirmed on 26 August that the anonymous model Ox Alpha was GLM-5.3-Flash, released under the MIT licence with a one-million-token context. We analyse the reveal, the specs, and the point that goes far beyond the technical.
- AI and disability: the field where it changes the most lives, and the one we talk about least.
For people with disabilities, AI is not a matter of comfort but often a matter of access. We take a look at the uses that genuinely make a difference, and the limitations that need to be honestly acknowledged.
- FLUX 3 vs Seedance 2.5, Gemini Omni and Kling 3: where does the newcomer really stand?
On 23 July 2026, Black Forest Labs launched FLUX 3, capable of generating twenty seconds of video with synchronised audio from a single prompt. It is being compared to Seedance 2.5, Gemini Omni Flash, Kling 3.0, Veo 3.1 and Runway Gen-4.5, and we explain why no model wins across the board.
- Google releases three Gemini models at once, cheaper and faster, but still not the one we're waiting for.
On 21 July 2026, Google launched three new Gemini models in the Flash range: 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber. All focus on efficiency and lower costs for AI agents, while the flagship Gemini 3.5 Pro model remains stalled. We break down this trio, its real numbers, and the quiet announcement of Gemini 4 in the works.
- What is a "multimodal" AI? When an intelligence learns to see, read, and hear at the same time
Multimodal: this term refers to AIs capable of processing several types of content (text, image, sound, video) within a single model. We explain simply what this means, how it's possible, and why this capability marks an important step towards AIs closer to our own way of perceiving the world.