Which LLM should I use in my project?

The question comes up in every digital product conversation: which language model should we choose? GPT-4, Claude, Gemini, Llama — the list grows every month, and the decision looks too technical for anyone coming from marketing or business.

But choosing the right LLM isn't just a technical decision. It's a strategic one. It affects cost, user experience and even brand positioning.

The best AI isn't the most famous one — it's the one that solves your project's specific problem with the least friction.

The "best model" trap

Many teams start down the wrong path: they research benchmarks, read technical comparisons and pick the model with the highest score on general tests.

The problem? Benchmarks measure general performance, not your specific use case.

An Agência Rollin client wanted to implement a customer support assistant. The product team showed up excited about GPT-4 — after all, it was at the top of the rankings. But the project required answers in Brazilian Portuguese, with regional nuances and a conversational tone.

After A/B testing, Claude 3.5 Sonnet beat GPT-4 on naturalness and adherence to the brand's tone. And it cost less per processed token.

The lesson: the "best" depends on context.

Four criteria that matter more than hype

Before choosing, the team needs to answer four questions — not in order of technical importance, but in order of business impact:

1. What is the exact use case?

LLMs have different profiles. Some are better at logical reasoning, others at creativity, others at following precise instructions.

GPT-4 and o1: excellent for complex reasoning, data analysis and tasks that require "thinking in steps."
Claude: stands out in long conversations, context retention and creative writing with a human tone.
Gemini: native integration with Google tools, a good fit for teams that already live in the Workspace ecosystem.
Llama (and open-source models): full control, customization and self-hosting — essential for sensitive data.

The difference between a chatbot that frustrates and one that converts can come down to this initial choice.

2. How much will it cost at scale?

Prototypes are cheap. Products in production, with thousands of interactions a day, make those cents per token really hurt.

An illustrative case: an education platform deployed an AI tutor to grade essays. In early tests, the API cost seemed negligible — a few dollars a week.

When they launched to 10,000 active students, the GPT-4 model produced a bill of US$ 8,000 in the first month. Migrating to a combination of Llama (self-hosted) + Claude for complex cases cut costs by 70%.

Rule of thumb: if your monthly volume exceeds 1 million tokens, it's worth evaluating open-source models or APIs with negotiated pricing tiers.

3. Where will the data live?

Privacy isn't paranoia — it's compliance, reputation and, in many cases, a legal requirement.

If the project handles personal, medical, financial or proprietary data, the choice between a public API and a self-hosted model changes everything.

Public APIs (OpenAI, Anthropic, Google): maximum convenience, zero infrastructure. But the data passes through third-party servers.
Self-hosted open-source models (Llama, Mistral): full control, with LGPD/GDPR handled in-house. However, they require an infrastructure and MLOps team.

A financial sector client ruled out public APIs right away — internal policy prohibited sending customer data outside the company. The solution was Llama 3.1 running on a private cloud, with a higher upfront cost but zero risk of leaks.

4. Does latency matter?

Customer service chatbots need to respond in 1–2 seconds. Data analysis tools can take 10 seconds without any problem.

Larger models (GPT-4, Claude Opus) are slower. Smaller models (GPT-3.5, Claude Haiku, Llama 8B) respond quickly, but with less sophistication.

The classic trade-off: speed vs. quality. The key is knowing where the user experience breaks down.

The "single model" myth

Many teams assume they need to choose one LLM and use it for everything.

In practice, mature products usually rely on hybrid architectures:

A lightweight model (Haiku, GPT-3.5) for triage and simple answers.
A robust model (GPT-4, Claude Opus) for complex cases or when the lightweight one fails.
A fine-tuned model for ultra-specific tasks (e.g., writing product descriptions in the brand's exact tone).

This architecture reduces cost, improves latency and keeps quality where it matters.

An e-commerce client uses three layers: Llama 8B for search suggestions (instant), Claude Sonnet for personalized recommendations (long context) and GPT-4 for escalated support (edge cases).

Result: 60% savings versus using GPT-4 for everything, with no loss in perceived quality.

The decision isn't "which is best" — it's "which one solves this"

Choosing the ideal LLM starts with clarity about the problem, not about the technology.

Before looking at benchmarks:

Map the real use cases (not what "would be cool to do").
Estimate volume and cost at 3, 6 and 12 months (not just for the MVP).
Define your non-negotiable constraints (privacy, latency, language).
Test at least two models with real data, not tutorial examples.

And remember: the best choice today may not be the best six months from now. The LLM market is constantly moving — new models, new prices, new capabilities.

The winning strategy isn't getting the perfect model right on the first try. It's building an architecture that lets you swap, combine and evolve as the product grows.

Start by testing, not by researching

The difference between teams that implement AI successfully and those stuck in endless analysis lies in the speed of the first real test.

Build a working prototype in one week. Run it with real users. Measure latency, cost and answer quality. Then adjust.

Agência Rollin works with clients who want to bring AI into digital products — and the first conversation is never "which model should we use," but "what do you want the AI to solve?"

If your team is at that crossroads, it's worth a conversation. Sometimes 30 minutes of strategic discussion saves months on the wrong path.

Have you tested different LLMs in your project yet? The right choice may be closer (and simpler) than it seems.

Frequently asked questions

How do I choose which LLM to use in my project?

Start with the problem, not the technology. The article proposes four criteria: the exact use case, the cost at scale, where the data will live and whether latency matters.

Are benchmarks enough to choose a language model?

No. Benchmarks measure general performance, not your use case. In one client case, after A/B testing, Claude 3.5 Sonnet beat GPT-4 on naturalness in Portuguese and cost less per token.

What is the difference between GPT-4, Claude, Gemini and Llama?

According to the article, GPT-4 and o1 stand out in complex reasoning, Claude in long conversations and human-sounding text, Gemini in integration with Google tools, and Llama in full control and self-hosting.

When should I use a self-hosted open-source model?

When the project handles personal, medical, financial or proprietary data that can't pass through third-party servers. The article also suggests evaluating it above 1 million tokens a month. It requires an infrastructure and MLOps team.

Do I need to choose a single LLM for the whole product?

No. Mature products usually use hybrid architectures: a lightweight model for triage, a robust one for complex cases and a fine-tuned one for specific tasks, reducing cost and latency.

What is the first step to implementing AI with an LLM?

Test quickly: build a working prototype in one week, run it with real users and measure latency, cost and quality, testing at least two models with real data.