Evaluate & Benchmark
Multi-Variant LLM A/B Testing
Statistical A/B/n testing for 3+ model variants with ANOVA, Bonferroni correction, and sub-millisecond routing overhead.
The problem
Testing 3+ model variants at once usually breaks either statistical rigor or user experience consistency.
- The same user gets routed to a different model variant every time they refresh.
- With three variants running, nobody trusts the winner because the math wasn't done right.
- A/B testing tooling built for buttons doesn't know what to do with LLM outputs.
What NEO built
NEO built deterministic MD5-hash routing for consistent bucketing, async queue-based logging, and ANOVA with Bonferroni-corrected pairwise tests to find the winning variant.
The result
<0.5ms routing overhead
Runs at <0.5ms routing overhead while sustaining 1000+ req/s and delivering full statistical analysis in about 2 seconds.
From the blog · 8 min
Beyond A/B: Building a Multi-Variant LLM Testing Framework with Statistical Rigor
NEO built a production-ready A/B/n testing framework for LLMs that supports 3+ model variants, uses ANOVA and Bonferroni correction for statistical analysis, and adds under 0.5ms routing overhead.
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Set up an A/B/n test across these 3 model variants with consistent user bucketing and proper statistical correction, and tell me which one actually wins.
Paste it in · review the plan · get the diff
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