Artificial Analysis Intelligence Index
The Artificial Analysis Intelligence Index is a benchmarking framework developed by Artificial Analysis that evaluates and compares large language models and AI systems using standardized performance metrics. The index assesses models across multiple dimensions including latency, throughput, cost per token, and inference efficiency. By providing objective, comparative data on model performance, it enables developers and organizations to make informed decisions about which models best suit their specific use cases and constraints.
Evaluation Methodology
The index evaluates AI models across both technical performance and economic factors. Technical metrics focus on speed and efficiency characteristics such as time-to-first-token, throughput, and inference latency. Economic metrics measure cost-effectiveness, including the cost per million input and output tokens. This dual approach allows users to understand not only how fast a model performs but also its practical cost implications for deployment and operation.
Scope and Application
The Intelligence Index covers a broad range of AI models and systems, enabling comparisons across different providers and model sizes. Organizations use the index to identify optimal models for their particular requirements, whether they prioritize speed for real-time applications, cost efficiency for high-volume inference, or balance between multiple factors. The framework supports the growing need for transparent, comparable data in an increasingly crowded marketplace of AI models.
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”