"The pace of change in the burgeoning generative AI world is blisteringly fast. It's often hard to keep up with everything, even if it's your full-time job. Readers tell me that one area they find particularly confusing is the wide array of poorly-named AI models. What in the heck is the difference between GPT-5.1, Opus 4.5, Gemini 3, etc.? And why would you use one over the other?"
"The model is the underlying AI engine that does the intelligence processing. The application is the tool you use. But in the same way that different vehicles use different kinds and brands of engines, different applications use different models. For example, I gave the exact same prompt that produced the above diagram to ChatGPT's image generator (on the left) and Midjourney's image generator (on the right), and we got these results:"
Generative AI model development is extremely rapid, producing many differently named models that vary in strengths. Different models tend to outperform others depending on task type — for example, image generation, code creation, or research and analysis. Applications and models are distinct: the model is the underlying AI engine and the application is the user-facing tool, and different applications use different models. App integrations frequently introduce additional, often costly AI subscription layers. Practical workflow and the choice of application matter more than obsessing over specific model version numbers. Image generators can produce vastly different outputs from the same prompt.
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