Pick the model type before the prompt: image models make stills, video models make short clips, audio models make music or voice, and LLMs handle text.
- Image models cannot move. They output a single frame, with no motion, time or sequence.
- Video models struggle with identity. Clip lengths vary by model, and reference images help hold a face steady.
- Audio splits in two. Music models like Suno v6 make songs; voice models like ElevenLabs AI Studio make speech.
- LLMs mainly handle text. Most do not make images, video or audio themselves, and none can guarantee factual accuracy.
- Prompts differ by type. Images need visual detail, video needs motion, LLMs need role, context and format.
The categories are still the right way to think about it. Specific model names have changed — the models page is dated and current.
The most common question from creators new to generative AI is not "how do I write a better prompt" — it's "which tool do I use for this?" The answer starts with understanding what each type of AI model actually does, because using the wrong type of model for a task isn't a prompting problem. No amount of prompt refinement will make a text-to-image model generate a video.
This article explains each model type in plain language — what it does, what it can't do, and where it fits in a production workflow.
Text-to-Image Models
Text-to-image models generate a static image from a text description. You write a prompt, the model generates pixels. The output is a single frame — no motion, no time, no sequence. Everything the model produces exists within one image.
WHAT THEY DO WELL
Portraits, product photography, editorial imagery, concept art, fashion content, brand visuals, any scenario where you need a high-quality static image. With img2img and inpainting, they also edit and refine existing images.
WHAT THEY CANNOT DO
Generate motion, time, or sequence. They cannot produce video, animation, or audio. Any "video" effect from a static image model is either a separate tool or a cheap zoom effect — not true video generation.
Text-to-Video Models
Text-to-video models generate a short video clip from a text description. They understand motion, time, physics, and camera movement — not just what something looks like, but how it moves. The output is a video file, and clip length depends on the model: Runway says “Gen-4 creates videos in 5 or 10 second durations based on an input image and text prompt you provide.”, and lists its newer Gen-4.5 at “Supported durations 2 - 10 seconds”, while Dreamina says of Seedance 2.5, “You can create cinematic videos up to 30 seconds in standard mode or extend them to 180 seconds with the beta long-video mode.”Runway, Gen-4 Video Prompting Guide, read in a browser 17 Sep 2026, and Creating with Gen-4.5, read at source 22 Sep 2026 · Dreamina (ByteDance), Seedance 2.5, read at source 17 Sep 2026. An earlier version of this page said clips are typically 5–15 seconds, and listed HappyHorse among image models; Artificial Analysis lists it among video models.
The fundamental challenge of text-to-video is temporal consistency — keeping the subject looking the same across every frame. Reference image input, where a tool offers it, is the usual way to hold a face steady across a clip. An earlier version of this paragraph named the models with the most consistent motion and the strongest identity lock; no source ranked them, and the claim was removed.
WHAT THEY DO WELL
Human motion, physics-based action, environmental video (weather, water, fire), cinematic camera movements, lifestyle b-roll, and short narrative sequences with a single subject in a stable environment.
WHAT THEY CANNOT DO
Maintain perfect identity consistency without reference image input. Generate reliable dialogue or lip sync. Handle complex multi-subject scenes reliably.
Audio Models — Music & Voice
Audio models split into two distinct types: music generation models and voice synthesis models. They're built differently and serve different purposes — but both take text as input and produce audio as output.
Music generation models (Suno v6, Udio) produce complete songs with instrumentation, vocals, and production from a text description of genre, mood, and style. Suno’s current model is v6, which Suno calls its “flagship model”.Suno, v6 release notes, 9 Sep 2026, read at source 16 Sep 2026: “v6 is our flagship model and is reliable, precise and consistently delivers polished music across every genre and style.” suno.com. An earlier version of this page gave a song length for Suno v5.5, a model Suno has since retired. Udio still makes songs, but since its partnership with Universal Music Group it says “downloading of audio, video, and stems has been disabled”.Udio Help Center, Changes associated with the Universal Music Group partnership, read at source 22 Sep 2026: “Note that downloading of audio, video, and stems has been disabled”.First-hand: until 22 Sep 2026 this sentence said Udio “focuses on stem separation — exporting individual instrument tracks for post-production use”.
Voice synthesis models (ElevenLabs AI Studio) generate realistic human speech from text. The voice can be chosen from a library, cloned from a sample, or designed from scratch using demographic and style parameters. An earlier version of this paragraph gave a 30-second sample length and called ElevenLabs the most realistic voice synthesis available, indistinguishable from human speech; neither had a source, and both were removed.
Large Language Models (LLMs)
Large language models process and generate text. They understand and produce language across every format — prose, code, lists, structured data, dialogue. Unlike image and video models, LLMs maintain context across a conversation and can follow complex multi-step instructions.
LLMs are the backbone of the content pipeline. They write captions, generate voiceover scripts, write system prompts for other AI tools, draft briefs, and handle every text-based task in the workflow. They also power the meta-prompting workflows — using an LLM to improve prompts you'll use in image and video models.
WHAT THEY DO WELL
Writing, editing, summarizing, analysing, coding, structured output, reasoning, creative copy, brand voice matching, research synthesis, and any task that starts and ends with text.
WHAT THEY CANNOT DO
Most cannot generate images, video, or audio themselves; a chat product that does usually hands the job to a separate model, though some model families blur the line, such as Google’s Nano Banana, which Google calls a Gemini image model. Access real-time information without search tools. Guarantee factual accuracy on specific claims. Remember earlier sessions, unless the product has memory switched on, and some now do by default.Anthropic, Claude’s memory works everywhere, and you decide what’s in it, 25 Aug 2026, read at source 17 Sep 2026: “Memory is on by default on Free, Pro and Max plans across web, desktop, and mobile.” An earlier version of this list said LLMs cannot maintain memory between separate sessions, or generate images, video or audio natively, without qualification.
How These Types Work Together
A complete AI content production pipeline uses all four types in sequence. The LLM writes the brief and caption. The text-to-image model produces the visual. The text-to-video model animates it. The audio model adds voiceover and music. Each type hands off to the next — no single model does everything.
Understanding the type of each model also tells you what kind of prompt to write. Image models need visual specificity — camera, lighting, subject description. Video models need motion language — movement, duration, camera direction. LLMs need role, context, and format. Audio models need genre, mood, tempo, and instrumentation. Different model types require fundamentally different prompt structures.
No single model does everything. A full pipeline runs LLM, image, video and audio models in sequence, and each type needs a different prompt structure.
More prompts, when something changes.
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