Best LLM for every budget, updated daily

LLM API costs for equivalent performance drop, potentially saving users money on AI services.

Artificial Analysis has launched an updated daily index to help users find the best Large Language Models (LLMs) for their budget. The index plots various LLM API prices against their performance scores, highlighting models on the "value frontier." These are models where no cheaper option performs as well or better. The analysis considers blended API costs, combining input and output token prices, and presents this data visually. Users can filter models based on specific criteria, such as minimum performance scores, and view individual model variants. The index is updated daily using data fetched from Artificial Analysis's free data API via a GitHub Actions cron job. The project's code and data are publicly available on GitHub.

The daily update and public dataset empower users to continuously track and select the most cost-effective LLMs, a critical factor for scaling AI applications. By identifying models on the "value frontier," businesses can optimize their AI infrastructure spending without compromising on capabilities. This transparency allows for agile decision-making in a rapidly evolving market. The focus on blended API pricing and the exclusion of certain discounts suggests a move towards standardized, transparent cost evaluation. This approach benefits users by simplifying comparisons and preventing reliance on complex, variable pricing structures that can obscure true costs. It signals a market trend towards more predictable and accessible LLM pricing models.

deepExtract

The daily update and public dataset empower users to continuously track and select the most cost-effective LLMs, a critical factor for scaling AI applications. By identifying models on the "value frontier," businesses can optimize their AI infrastructure spending without compromising on capabilities. This transparency allows for agile decision-making in a rapidly evolving market. The focus on blended API pricing and the exclusion of certain discounts suggests a move towards standardized, transparent cost evaluation. This approach benefits users by simplifying comparisons and preventing reliance on complex, variable pricing structures that can obscure true costs. It signals a market trend towards more predictable and accessible LLM pricing models.