Shared settings
Variant A (control)
Variant B (challenger)
| Metric | Variant A | Variant B |
|---|
How to budget an LLM A/B test
The cost of a model experiment is often overlooked. For high-traffic applications, even a 7-day 50/50 test with a premium model can cost hundreds of dollars more than staying on the control. Calculate the delta before starting the test, then decide if the quality signal is worth the extra spend.
Practical rule: You need at least 200–500 samples per variant for a meaningful quality signal (80% power, p=0.05, detecting a 5–10% difference). For a 10,000 req/day app, even a 10% traffic split delivers 500+ samples per day — a 3-day test is plenty. Only run longer if you are looking for rare edge-case failures.
Shadow testing first: Before live A/B, run variant B in shadow mode (same input, outputs discarded). This costs money but has zero user impact, and lets you catch catastrophic failures before they affect real users.
Related: LLM eval cost calculator · AI spend tracker · API budget planner
Frequently asked questions
How much does it cost to A/B test two LLM models?
Cost = (requests × traffic_split × tokens_per_request × price_per_token) for each variant. A 50/50 split with 10,000 daily requests, 1,000 tokens each, and models at $3/$15 vs $0.15/$0.60 per 1M tokens would cost ~$90/day vs ~$3.75/day for a 7-day test.
What is the minimum sample size for a statistically valid LLM A/B test?
For most LLM quality metrics, you need at least 200–500 samples per variant to detect a 5–10% difference with 80% power at p=0.05. For tighter differences (2–3%), you may need 1,000–2,000 samples per variant.
How do I choose a traffic split for LLM A/B testing?
Start with 10/90 or 20/80 if the new model is untested. Move to 50/50 once you are confident the new model is not worse. Never put 100% on the new model without a shadow test first.
Should I A/B test prompts or models first?
Test prompts first — they are free to change and can dramatically affect quality. Once you have a stable best-prompt for each model, then compare models.
What metrics should I track in an LLM A/B test?
Track: task success rate, latency (p50/p95), output length, cost per successful output, and a human eval sample. Avoid using one LLM to judge another in the same family — use a different judge model or human raters.