Which LLM to Use for GenAI? (Hint: It Doesn't Matter at First)
Don’t get stuck picking between GPT, Claude, Gemini, or Mistral. Use this 5-step guide to choose the right LLM based on use case, cost, latency, and privacy.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
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?
Task | Good Fit |
|---|---|
Writing, summarizing | GPT-4o, Claude, Gemini 1.5 |
Q&A over documents | GPT-4o, Claude 3 Opus, Gemini 1.5 |
Complex reasoning | Claude 3 Opus, GPT-4o |
Multimodal inputs | GPT-4o (vision), Gemini 1.5 |
API calling / function tools | GPT-4, Claude, Gemini 1.5 |
Code generation | GPT-4o, Claude 3 Opus, Code LLaMA |
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.
Model | Context Window |
|---|---|
GPT‑4o | 128K tokens |
Claude 3 Opus | 200K tokens |
Gemini 1.5 Pro | 1M tokens (streamed) |
Mistral / Mixtral | ~32K tokens |
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.
Scenario | Model Choice |
|---|---|
Real-time UI app | GPT‑3.5, Claude Haiku, Gemini 1.5 Flash |
Experimenting on low budget | Mixtral, Mistral-7B, LLaMA2 |
Production + reliability | GPT‑4o, Claude 3 Opus (higher cost) |
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.
Option | Good For |
|---|---|
Mistral / LLaMA | Open-source, privacy, self-hosting |
Cohere RAG | Enterprise SaaS, hosted in-region |
Azure OpenAI / GCP Gemini | Region-compliant, controlled APIs |
Ollama + Docker | Local experimentation, air-gapped |
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.
Model | Supports Tools? |
|---|---|
GPT‑4o | Yes |
Claude 3 Opus | ✅ Yes (JSON + tool use) |
Gemini 1.5 | ✅ Yes (function calls) |
Open-source | ⚠️ Only with custom wrapper logic |
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.
- Schedule your session
