Artificial Analysis released Intelligence Index v4.1, a comprehensive framework for evaluating AI models across nine distinct benchmarks. The system measures intelligence through GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, and other specialized tests. Models receive lightbulb icons to indicate reasoning capabilities. This standardized approach enables apples-to-apples comparisons across different architectures and providers.

The index incorporates an Openness score ranging from 0 to 100, tracking whether model weights are publicly available or commercially restricted. Cost analysis calculates weighted average pricing per task, factoring in input, cache hit, and output token rates. Provider-specific caching models vary significantly: Anthropic charges separate cache write fees, while Google Vertex adds hourly storage costs.

Token efficiency metrics include output tokens per task and speed measurements in tokens per second. Latency tracking captures time to first token including reasoning model 'thinking' time. Context window size evaluates retrieval-augmented generation suitability, with larger windows supporting complex workflows. These granular measurements help developers select models matching their performance and budget requirements.

The framework's methodology provides detailed breakdowns of each evaluation's implementation and weighting. This transparency allows teams to understand exactly how models score across different dimensions. For engineering teams choosing between proprietary and open-weight models, these standardized benchmarks offer concrete data rather than marketing claims.