Evidence,NotGuesswork
AutoBench delivers dynamic, collective-judge evaluations so labs and enterprises can choose the right LLM with confidence, speed, and clear cost–quality trade-offs.
Our mission
Help labs and enterprises choose the right LLM for the job, faster, cheaper, and with evidence you can trust. AutoBench turns evaluation into a repeatable system that keeps pace with the model race, using Collective-LLM-as-a-Judge to deliver rankings aligned with gold-standard benchmarks.
Why AutoBench exists
Static leaderboards get gamed. Human reviews are slow, subjective, and expensive. Meanwhile, new models land every week, making ad-hoc trials impossible to scale. AutoBench offers a dynamic, automated alternative that generates fresh questions, lets models judge each other, and converges to stable rankings, so you can make procurement and product calls with confidence.
How it works
AutoBench runs end-to-end LLM evaluations on your topics and use cases. In a typical pass, we generate questions across domains (logic, coding, history, science, creative writing), collect answers from the models you select, and compute a consensus ranking that's strongly correlated with human-driven benchmarks like Chatbot Arena and MMLU, delivered in hours, not weeks.
- 01
Select models & domains
Bring any mix of commercial, open-source, or proprietary endpoints.
- 02
Auto-generate Q&A
Fresh, difficulty-balanced prompts each run (no test-set memorization).
- 03
Collective-judge loop
Multiple LLMs score and weight peers; ranks stabilize iteratively.
- 04
Decision-ready output
Download CSVs and dashboards for quality, cost, and latency trade-offs.
What makes us different
Collective-LLM-as-a-Judge
Models judge each other to dilute single-model bias and reflect the state of the ecosystem.
Dynamic by design
New prompts every run resist benchmark gaming and stay relevant as capabilities shift.
Proven alignment
Correlates ~80% with established standards (e.g., ~83% vs Chatbot Arena, ~75% vs MMLU, ~79% vs AAQI).
Speed & efficiency
A 20-model sweep with 267 questions has been delivered in ~7 hours—with results that track human-curated leaderboards.
How we work with you
Managed runs
We operate the benchmark for you (private to your data and endpoints) and deliver decision-ready reports.
In-cloud deployment
Prefer to run it yourself? Set it up in your environment and plug in your model list.
AutoBench your models today
We invite you to explore the code, run the benchmark, contribute to its development, and join the discussion on the future of LLM evaluation. Explore our resources on Hugging Face.
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