Which AI model should you be using?
We often get asked by our clients which AI model they should be using. We explain why our answer is always that the best model is the one you are already using. Plus, we share a summary of what each feature is for every model, so you know how to get the most out of what you currently have.
Author: Asta Vallis and Chris Desai
Read length: 4 minutes
Date: 26th May
Spark AI is a strategy-led consultancy helping agency and brand teams move from fragmented experimentation to organisation-wide capability. Our blog provides the strategic techniques, insights and industry discussions needed to navigate AI with confidence.
Why do we say the best AI model is the one you already have?
The models are constantly evolving and competing hard with each other. When one pulls ahead, the others close the gap quickly. That makes "which model is best right now?" the wrong question, because the answer keeps changing, and will continue to do so.
What does not change is the value of everything you have built inside your current tool.
Context is everything the model knows about how you work: your brand, your tone, your clients, your preferences.
Fluency is your own knowledge of how to get the best out of it: which prompts work, which features to reach for, where things live. That familiarity comes from daily use and it shows up directly in the quality and speed of your work.
Neither transfers when you switch. A tool that scores higher on a benchmark but knows nothing about your business will almost always produce weaker work than one you have been building a working relationship with for months.
Are you using your model well?
Since we are telling you to stick with one model, it is worth ensuring that you know all of its functionalities. Beyond chat, there are features built specifically for reusable context, structured research, multi-step tasks, and automated workflows. We have put together a plain-English breakdown for each model describing what each feature does, how to think about it, and a real example of when to use it. We will keep it updated as the tools evolve.
How do you improve the outputs your AI model produces?
It is easy to let your model learn your preferences by accident. Correcting things mid-conversation and repeating the same instructions each time creates outputs that are almost right, but requires more work than necessary.
A quick audit can change that. It is about two things: tightening your instructions so the model knows how to behave, and feeding it the right data so it has something to work with.
A quick audit can help change that.
Open up your chosen model and find a tool you use regularly, whether that is a Project, a Custom GPT, a Skill, or a Gem. Ask it:
What is missing from my context that would strengthen the reliability of your outputs?
What assumptions are you making about the way I work that could be made explicit in your instructions?
What do I always correct you on mid-conversation that could be built into your instructions so you get it right first time?
Then add context yourself:
Upload or paste an example of an output you were happy with and explain why it worked.
Share a writing guide, a brand document, or a style reference so the model produces work with consistency.
Frequently Asked Questions
What is data and why does it matter?
Data is everything you feed your LLM to help it understand your specific needs. Brand guidelines, tone of voice documents, example videos, anything that helps shape what comes back to you. The more relevant data you put in, the less your model is guessing, and the more it produces work that is tailored to you.
Should I use a paid AI subscription for work?
Yes definitely. Paid subscriptions means that conversations are not used to train the model, and your data is not stored in ways that could expose sensitive client or company information. If you are putting client briefs, internal strategies, or confidential materials into an AI model, a free tier carries real risk.
Should everyone in my team use the same AI model?
Yes it is important to all use the same model. Different models across a team mean different levels of context, different working habits, and no shared foundation to build on. It also makes it harder to share prompts, workflows, and custom setups, so every time someone builds something useful, it stays siloed rather than benefiting the wider team.
