AutoBench is a platform for transparent, data-driven evaluation of large language models (LLMs). It helps organizations compare model quality, cost, and speed using automated, iterative benchmarks designed to produce robust and statistically meaningful results. Users can evaluate public models or connect private endpoints, choose relevant subject areas, and generate difficulty-balanced prompts. Models assess one another’s responses, while AutoBench applies a weighting process to refine scores and stabilize rankings.

The platform provides domain-specific leaderboards and efficiency metrics, including average answer cost, response duration, and P99 latency. Results are available through an interactive dashboard and downloadable CSV files, with options to use the data in Hugging Face Spaces or internal business intelligence tools. AutoBench also offers custom benchmarking for enterprises and LLM labs, allowing teams to test models against their own use cases and data, compare cost-quality trade-offs, and monitor performance over time. The company reports strong correlations between its public benchmark results and established evaluations, including AAII and LMArena.