LLMEval

LLMEval is a research initiative from Fudan University's NLP Lab that builds rigorous, peer-reviewed evaluation frameworks for large language models. The suite spans three main benchmarks: LLMEval-Fair (220,000+ graduate-level questions across 13 academic disciplines, tested against 59 LLMs with contamination-resistant methodology), LLMEval-Med (a physician-validated clinical reasoning benchmark), and LLMEval-Logic (a Chinese logical reasoning benchmark with adversarial hardening). Code, datasets, and leaderboards are released openly via GitHub and HuggingFace, with associated papers at AAAI, EMNLP, and ACL.

This is academic infrastructure rather than a commercial SaaS, so there's no dashboard, no API key, and no pricing tier — you download the dataset and run it yourself. That makes it best suited to ML researchers, model builders, and serious AI teams who want defensible eval numbers grounded in published methodology rather than vibes. The contamination-resistant design is the main draw: many public benchmarks have leaked into training corpora, and LLMEval-Fair is explicitly built to mitigate that.

If you're shipping a model in regulated domains (medicine, formal reasoning) or publishing a paper, the Med and Logic tracks give you a credible, citable yardstick. Less useful if you want a one-click LLM-judge SaaS like Braintrust or Langfuse — you'll be writing the harness yourself.