A/B testing AI models: how to find the best model for your use case

Which model is best for your customers? The honest answer is: you will not know until you test. Benchmarks measure general capability. Your customers have specific needs. A/B testing bridges the gap.

Setting up model A/B tests

Split incoming conversations between two or more models. Ensure the split is random and the volume is sufficient for statistical significance (typically 500+ conversations per variant). Keep everything else constant: same knowledge base, same system prompt, same integrations.

What to measure

Resolution rate is the primary metric: which model resolves more conversations without escalation? Secondary metrics include CSAT, average response quality, hallucination rate, and cost per conversation.

Common findings

In our experience, the most expensive model is not always the best performer. For straightforward support queries, lighter models often match or exceed heavier ones in resolution rate while costing 80% less. Heavy reasoning models shine on complex, multi-step queries.

The cost-performance frontier

Plot each model's resolution rate against its cost per conversation. You will find a frontier: models that offer the best resolution rate for their price point. Route simple queries to the cheapest model on the frontier, complex ones to the most capable.

Continuous optimisation

Model performance changes as providers release updates. Run periodic A/B tests, quarterly at minimum, to ensure your model mix remains optimal. The AI landscape moves fast; your optimisation should too.

The best model for your use case is the one you have tested, not the one with the best benchmark score.