AI Evaluator
The State of AI Evaluation
As Large Language Models (LLMs) approach parity in general reasoning, the battleground has shifted to evaluation. It is no longer enough to claim "state-of-the-art"; performance must be proven in public arenas, rigorous cross-corporate testing, and specialized benchmarks. This dashboard explores how AIs are judged, how they compete, and how they learn from one another.
LLM-as-a-Judge
Using superior models (like GPT-4) to grade the outputs of smaller or newer models. Fast, scalable, but prone to bias.
Public Arenas
Crowdsourced blind tests (e.g., LMSYS) where humans vote on the better answer. The "Elo Rating" gold standard.
Strategic Learning
How models identify competitive gaps and use synthetic data from rivals to improve via fine-tuning.
Why this matters now?
With the release of GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, the gap between top models has narrowed to slim margins. Evaluations now focus on nuance, speed, cost, and safety rather than just raw intelligence.