How to Choose the Right Large Language Model for Your Product

There are now many capable large language models from different providers, plus open models you can host yourself. Choosing between them can feel overwhelming. In practice, the decision comes down to a handful of factors you can test.

Define the job first

A model that writes excellent marketing copy may be poor at extracting data from invoices. Write down exactly what the model must do, with real examples, before comparing options.

The factors that matter

• Quality on your task: measured on your own examples, not public benchmarks.
• Speed: how quickly responses arrive, which matters for interactive features.
• Cost: price per request at your expected volume.
• Context size: how much text the model can consider at once.
• Privacy and data handling: where data is processed, how long it is retained, and whether it is used for training.
• Reliability: uptime, rate limits, and support.

Test with an evaluation set

Collect a set of real inputs with expected outputs. Run each candidate model against it and compare results. This turns a subjective debate into a clear comparison and gives you a way to test future models too.

Use different models for different tasks

Many products use a small, fast, inexpensive model for simple steps such as classification, and a larger model only for complex reasoning or writing. This often improves both speed and cost.

Hosted or self-hosted?

Hosted models from major providers are the fastest way to start and usually the most capable. Self-hosting open models can make sense for strict data requirements or very high volumes, but adds infrastructure and maintenance work.

Avoid lock-in

Models improve rapidly and prices change. Build your product so the model can be swapped without rewriting the application: keep prompts organised, use an abstraction layer, and rerun your evaluation set when switching.

The best model today will not be the best model next year. Design for change.

MOKRIN helps clients select models by testing them on real examples, then builds products that can move to better or cheaper models as they appear.