Which AI model to choose in 2025: Claude, GPT, Llama and the others

You need to choose an AI model for a project, and every week a new leaderboard comes out that overturns the previous one. The uncomfortable truth is that asking which AI model to choose in 2025 is the wrong question when posed in the abstract: the answer depends on your use case, your constraints and your budget, not on the ranking of the moment. Here are the criteria we use when we integrate a model into client projects.

The main families, with no fan loyalties

The market has settled around a few big names: Claude from Anthropic, OpenAI's GPT family, Google's Gemini and, on the open-weights front, Meta's Llama alongside a very active open scene. All the major producers offer multiple sizes of the same model: large versions for complex tasks, small and cheap versions for simple, high-volume tasks. Flagship capabilities look more and more alike, and the practical difference between one provider and another has moved to other levels: price for your usage volume, quality on your specific type of task, terms on data handling, integration tooling. That's where it pays to look.

First criterion: the use case, not the leaderboard

Benchmarks measure generic tasks; your project is specific. A model that excels in reasoning tests can be mediocre at summarizing your technical documents in Italian, and vice versa. The method we recommend to clients:

• Define the task in a measurable way: classifying emails, extracting data from documents, answering from a knowledge base, generating drafts.
• Prepare a test set with your own data: a few dozen real cases, with the output you would consider correct.
• Try two or three models on the same set and compare the results against criteria decided in advance.

It's half a day of work and it's worth more than any leaderboard, because it measures the only thing that counts: how the model behaves on your problem.

Costs: the right size beats the best model

The most expensive mistake we see is using the flagship model for everything. In production, most calls involve simple tasks that a small model handles just as well at a fraction of the cost. The practices that keep the bills under control:

• Match the size to the task: small model for classifications and rephrasings, large model only where articulated reasoning is needed.
• Watch prompt length: cost grows with tokens; useless context repeated on every call is wasted money.
• Estimate volumes before starting: an acceptable per-call cost can become unsustainable multiplied by thousands of users a day.
• Design so you can switch: if the integration isolates the model behind an interface of your own, replacing it when prices change becomes a small intervention instead of a rebuild.

Privacy: where your data ends up

For many Italian and European companies this criterion weighs as much as the first two. The questions to ask the provider before signing: is the data you send used to train the models? Where is it processed and stored? What contractual guarantees exist for GDPR purposes? The main providers offer business plans with specific commitments on these points, but they need to be read, not taken for granted. If you handle particularly sensitive data, open-weights models like Llama offer the route of installation on your own servers: the data never leaves your infrastructure, in exchange for costs and operational complexity that should be budgeted honestly. Here too: it depends on the case, not on ideology.

The practical advice: start small, measure, stay free

After several projects with LLMs in production, the summary of our experience is this: choose based on a test on your own data, start with a narrow use case, measure real quality and costs, and build the integration so you can change model without rewriting the application. The market moves fast, and the freedom to change provider is worth more than any perfect initial choice. When we build custom software with AI components, we design this flexibility in from the start: the model is a replaceable piece of the architecture, not the foundation.

Want to integrate AI into your software without marrying a provider?

If you're evaluating a project with an AI model inside, from model selection to integration architecture, we can help you decide with data in hand instead of leaderboards. We build custom software with AI components in production. Book a free call: we'll analyze your use case and tell you which approach makes sense for your volumes, budget and privacy constraints.