"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?"
"Also: Claude Code made an astonishing $1B in 6 months - and my own AI-coded iPhone app shows why To be honest, trying to fully understand the detailed differences between each of the AI models will send almost anyone screaming into the woods. But it's fairly easy, especially with examples, to understand which models to choose for different tasks. That's what we're going to do in this article."
"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: As you can see, ChatGPT's result is simple and fairly clear, whereas Midjourney went both overboard while also simultaneously not actually following the prompt."
The generative AI landscape evolves extremely quickly, creating confusion over many model names. Different models perform better for distinct tasks such as image generation, coding, or research. Applications and AI models are separate: models provide the intelligence engine while applications are the user-facing tools. App integrations frequently add extra AI subscription layers that increase cost. Image generator examples show that some models produce clear diagrams while others excel at conceptual imagery but fail at precise prompts. Choosing models by demonstrated strengths and optimizing workflows matters more than obsessing over version numbers.
Read at ZDNET
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