Stop Asking Which AI Model Is Best. Ask This Instead.

I’m the CEO of a company that sells AI infrastructure, and I get a front-row seat to how enterprises actually use AI models – and how they get stuck choosing them.

Customers ask me which AI model they should use. I tell them: start with the goal, build the infrastructure that makes it happen, and keep the model flexible.

We recently pulled usage data across our enterprise accounts:

• 76% of enterprise accounts use more than one AI model
• The average account uses 3.3 different models
• Over a third use models from multiple providers – OpenAI and Anthropic, or OpenAI and Google
• Among accounts that started on older models, 77% have already adopted newer ones

The “best” model gets dethroned in 90 days. Our enterprise customers figured that out. They stopped picking a model and started picking a platform.

The Question Everyone Gets Wrong

Every week, someone asks us: “Should I use GPT-5.2 or Claude? What about Gemini?”

I understand the instinct. There are currently 16 models on our platform alone. The comparison charts multiply. The benchmarks conflict. It feels like choosing the wrong model will sink your AI project.

So teams freeze. They spend weeks comparing, finally pick one, build around it – and then a better model drops. Rebuild or fall behind.

We call this model paralysis. 

The question isn’t “which model is best?” The question is: which platform makes any model work for your use case?

Think in Layers, Not Models

Most people treat AI as a single decision. Pick a model, plug it in, done. But an enterprise AI agent isn’t a model. It’s a stack of layers, and the model is just one of them.

Stop optimizing the thinnest layer of the stack.

What’s Actually in the Stack

Once teams stop debating models, they focus on the layers that actually matter.

Retrieval determines answer quality more than the model does. We offer four primary goals that control how your agent finds information:

• Optimal – Balanced performance. The default for most use cases.
• Speed – Sub-second responses for live chat and high-volume support.
• Accuracy – Cohere-powered re-ranking that optimizes which information the agent selects from your knowledge base. Critical for large document libraries.
• Understanding – Breaks each question into five sub-queries, searches independently, merges results. Adds latency, but the depth is worth it for legal, compliance, and research.

Support agents need Speed or Optimal. Legal advisors need Understanding. Product catalogs with thousands of SKUs need Accuracy. The model matters less than which retrieval strategy matches your use case.

The trust layer is what your CISO cares about. Anti-hallucination is a platform-level control that restricts responses to your knowledge base. Citations trace every answer to a source document. Verify Responses cross-references every claim against your sources, scored from six stakeholder perspectives.

All of this works identically whether you’re running GPT-4.1 or Claude Opus 4.6. Swap the model, keep the trust.

The experience layer is what your users actually see. Persona instructions define how your agent talks, what it covers, and how it behaves. A support agent should be empathetic and concise. A legal advisor should be precise and cautious. A sales assistant should drive toward outcomes.

We built persona templates for eight common use cases – each with copy-paste instructions, customization tips, and power tips for features like Document Analyst, Lead Capture, and Webpage Awareness.

The right retrieval goal + the right model + a well-crafted persona builds a real AI agent. Not a model choice alone.

Why This Matters More Every Quarter

The AI model landscape in 2026 looks nothing like 2024. In the past year alone, we’ve added models from three providers: OpenAI (GPT-4.1, 4.1 mini, 5, 5.1, 5.2), Anthropic (Claude Sonnet 3.7 through 4.6, Opus 4.5 and 4.6, Haiku 4 and up), and Google (Gemini 3 Pro, Gemini 2.5 Flash).

The pace is relentless.

We don’t add models on day one. We test them, optimize our retrieval and prompt infrastructure for each, and deploy when they’re production-ready. OpenAI and Anthropic models take a few weeks. Google took longer initially, but we’ve built the muscle now.

Our data tells the story. GPT-4o was dominant just months ago – 46% of all queries. GPT-4.1 already accounts for 41% and climbing.

The migration happened without a single rebuild. Customers selected the new model and their agents kept working. Same persona, same knowledge base, same integrations. 

When One Model Is Actually Fine

I’m not going to pretend model flexibility is always the priority. Some teams genuinely don’t need it:

You have one simple use case that won’t change. A basic FAQ bot on a small, stable knowledge base? GPT-4.1 on Optimal will handle it indefinitely.

Your compliance environment is locked. If procurement has already approved OpenAI and won’t revisit for two years, pick the best OpenAI model and move on.

You’re in early exploration. Testing whether AI works for your problem at all? Start with the default and iterate on your knowledge base and persona. Optimize the model later.

If any of these describe you, stop reading model comparison charts and ship something.

But if you’re running multiple agents for different teams, or your industry moves fast enough that today’s model might not fit next quarter – you need a platform that treats the model as a swappable layer. Most enterprises I talk to are in this camp.

The Real Cost of Model Lock-In

A team builds their agent around GPT-4o. Three months later, Claude Sonnet 4.5 handles their customer conversations with noticeably more natural dialogue. But switching means re-engineering prompts, re-testing every edge case, re-deploying.

The cost isn’t the model. It’s all the infrastructure built around the model that can’t travel.

On our platform, switching is a dropdown. Select your agent, open Intelligence, pick a different model. Persona, knowledge base, integrations, analytics – everything stays.

“Bernalillo County needs AI that’s both reliable and adaptable,” says Bernalillo County Assessor Damian Lara. “Using CustomGPT.ai allows us to evaluate different models without having to rebuild our infrastructure. We can simply choose the best fit based on accuracy, speed, and cost for the thousands of citizen inquiries processed through A.C.E., our chatbot.”

We barely get support tickets about model switching. When the infrastructure is model-agnostic, switching isn’t an event. It’s a setting.

The Decision Framework

Instead of “which model should I use,” ask these questions:

Question	What It Reveals
What’s your use case?	The use case determines the requirements. The requirements determine the platform. The model comes last.
What’s your primary goal?	Speed, Optimal, Accuracy, or Understanding. This matters more than which LLM is underneath.
How many agents will you run?	One agent – model flexibility is nice. Five or ten across different teams – it’s essential.
What’s your compliance posture?	Anti-hallucination, citations, verification, SOC-2, GDPR – these should persist across every model, not be rebuilt for each one.
What happens in six months?	The model landscape will look different. Will your infrastructure adapt, or will you be locked into today’s “best” choice?

Where This Is Heading

Here’s where I think the model debate ends up: it becomes irrelevant.

Not because models stop improving – they’ll keep getting better, faster. But because the platform layer will abstract the model choice entirely. You’ll describe what you need – “fast responses for customer support” or “deep analysis for legal review” – and the platform will route to the right model automatically.

We’re already partway there with primary goals. Picking “Understanding” doesn’t require you to know that the system is using GPT-5.1 with five-sub-query decomposition. You just pick the outcome you want.

The companies that win with AI won’t be the ones who picked the “best” model in Q1 2026. They’ll be the ones who built on a platform where the model was always swappable, optimizable, and ultimately invisible.

Stop asking which model is best.

Start asking what’s above the model.

Start with your use case, not the model. See how CustomGPT.ai works.