There is no single best LLM. The right engine depends on the task, your data, your budget and your risk tolerance. A structured comparison prevents expensive mistakes.
Start with the task
Summarising emails, answering customer questions, extracting invoice data and writing code all place different demands on a model. Define the task and what a good result looks like before comparing engines.
Criteria to compare
- Quality: test on your own real examples, not just public claims
- Cost: usually charged by the amount of text processed, so estimate real volumes
- Speed: important for live chat, less so for overnight batch jobs
- Context size: how much text the model can consider at once
- Languages: check performance in the languages your customers use, such as Arabic or Urdu
- Privacy and hosting: where data goes, how long it is kept and whether it is used for training
- Reliability: uptime, support and how often the provider changes models
Run a small bake-off
Prepare 20 to 50 realistic examples, run them through two or three candidate models and score the results with the people who will use them. This takes days rather than weeks and gives far better evidence than reputation.
Avoid lock-in
Design your system so the model can be swapped later. Models improve and prices change quickly, so flexibility is a real advantage.
We help teams compare and integrate LLM engines through our AI & Automation service. Talk to us about your use case.