Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah-Hill Smith

What It Covers

George Cameron and Micah-Hill Smith explain how Artificial Analysis became the independent benchmarking standard for AI models, covering their methodology for measuring intelligence, speed, cost, hallucination rates, and openness across hundreds of models and providers.

Key Questions Answered

• •Independent Benchmarking Economics: Artificial Analysis runs evaluations costing hundreds to thousands of dollars monthly, using mystery shopper policies with unidentified accounts to prevent labs from optimizing specific endpoints. They maintain independence by never accepting payment for better rankings while monetizing through enterprise subscriptions and private benchmarking services.
• •Intelligence Cost Deflation: GPT-4 level intelligence now costs 100-1000x less than at launch, yet total AI spending increases simultaneously. This paradox occurs because frontier models use 10x more tokens through reasoning chains and agentic workflows, creating a smile curve where both cheap commodity intelligence and expensive frontier capabilities grow.
• •Hallucination Measurement Innovation: The Omniscience Index scores models from negative 100 to positive 100, deducting points for incorrect answers rather than rewarding guesses. Claude models show lowest hallucination rates at 15-20%, while intelligence level shows no correlation with hallucination tendency, revealing post-training recipe differences between labs.
• •Agentic Benchmark Methodology: GDP-VAL AA uses 220 sub-tasks across 44 white-collar job scenarios, running models through their open-source STIRRUP harness with code execution, web search, and context management. Models in custom harnesses outperform their official chatbot versions, with Gemini 3 Pro using 95% confidence intervals requiring multiple evaluation runs.
• •Hardware Efficiency Reality: Blackwell generation GPUs deliver 2-3x throughput gains over Hopper for most workloads, not the marketed 4x, with actual improvements varying by model sparsity. Total parameter count correlates more strongly with knowledge retention than active parameters, suggesting sparse models like Kimi K2 at 3% activation still benefit from larger total sizes.