For business AI tools I advise picking a model per task instead of choosing one "best" LLM: the market offers a huge choice for every taste, and I run a mix of different models - Claude, Codex, GLM, Gemini and others. Each one has its own job
Fixating on a single model is the most common mistake at the start. A business buys one subscription, tries to cover everything with it - from emails to analytics - half the tasks come out forced, and the conclusion becomes "AI does not work". But it is not the AI that fails - it is the wrong tool for that job
Which model does what
Claude is the wise owl that sees far ahead: it writes the strategy and architecture of your future project brilliantly. This is the model for the stage where you need to think before building: break the task into parts, spot the risks, plan the sequence of steps
ChatGPT is a fine rottweiler: it grabs a task and does not let go until it is done. Most often I use it as an auditor for the work Claude has completed - one model builds, the other checks, and the result gets noticeably more reliable
GLM handles repetitive tasks: it writes solid text and works as an excellent auditor too. Where work repeats day after day - drafts, checks, routine - it performs no worse than bigger names
Gemini is what I use for image generation, and Groq as my voice model: every voice message in my system goes through it. The voice assistant runs on separate models of its own
What this means for your business
Here is how I would start: take one real work task and run it through two or three different models. The difference in results shows right away which task types are "yours" and where a model struggles - and the mix assembles itself
Compare three things: the quality of the result on your own task, the response speed, and the price of the plan once volume grows. A test on someone else's examples says nothing about your practice - only your own task shows the truth
The only thing I have not got to yet is local models. Those need serious hardware, and most businesses do not need them at the start: cloud models are enough, and a move to local infrastructure makes sense only when strict data-privacy requirements appear
To work out which neural networks fit your own processes, book an [AI consulting session](https://matintellect.com/en/services/ai-consulting)
