Which LLM to Use for GenAI? (Hint: It Doesn't Matter at First)

Every week, clients ask us:

“Should we use GPT‑4, Gemini, Claude, or an open-source model for this AI product?”

And every time, our answer is the same:

It doesn’t matter.

Not at the beginning.

Unless you're doing something bleeding-edge, 90% of GenAI results don’t depend on which model you pick.

In fact, chasing LLM upgrades is often a sign that:

• The prompting is unclear

• The data is unreliable or unstructured

• The system design isn’t set up for real feedback loops

Teams change models because it’s easy—not because it drives value.

So how should you choose an LLM?

Here’s our practical, field-tested checklist we use across enterprise and product environments.

1. Match the Model to the Use Case

First ask: What job are you hiring the model for?

If the model can do the job well enough, move forward.

Fine-tuning or switching can come later—if you even need it.

2. Know Your Context Window Needs

If you're building apps that deal with long documents, complex conversations, or multi-turn chats, context window is key.

For anything involving PDFs, policy docs, contracts, or transcripts, go with models that support 100K+ tokens.

It’s the difference between answering based on 2 paragraphs vs. 20 pages.

3. Latency & Cost Constraints

If you're building real-time apps (e.g., chatbots, copilots), or have tight budgets, avoid overkill.

Use lightweight or open models where they get the job done.

You can also mix-and-match: lightweight models for basic queries, high-end ones for escalations.

4. Privacy, Hosting & Compliance

This matters more than people realize.

If you’re in healthcare, finance, gov, or need on-prem deployments—open-source or self-hosted models may be the only viable path.

Don’t build GenAI products on models that you can’t legally use in your industry.

5. Function Calling & Tool Use

Many GenAI apps need more than plain text output.

They need:

• Calling APIs

• Triggering external actions

• Tool usage or multi-agent flows

Make sure the LLM supports structured function calling, tool use, and ideally streaming output if needed.

If your app calls APIs or performs logic chains, model capabilities matter.

TL;DR – Choosing the Right LLM Isn’t About the Logo

• Start with GPT-4o, Claude, or Gemini

• Don’t swap models unless you’ve fixed prompting, structure, and UX

• Match model to the use case, not Twitter hype

• Factor in cost, speed, tokens, and privacy

• If your infra is solid—you can swap LLMs later in <1 hour

Bonus: When to Upgrade the Model

Upgrade only when:

• You’ve validated that the current model fails on specific edge cases

• You've ruled out prompt, chunking, or toolchain issues

• You’re at a scale where speed or cost truly matter

• You need a bigger context window

Otherwise? Ship it. Test it. Learn.

Final Thought

Most AI product teams overthink model choice and underinvest in prompt strategy, data structure, and end-user UX.

Model is just one piece of the system. Don’t let it be a bottleneck.

Book a 30‑minute Data / AI Audit. 

We will provide you roadmap to get value from your genAI and Data initiatives fast and push to production.

No gimmicks just experience and aligning to business objectives.

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