The rapid advancements in artificial intelligence (AI) have raised critical questions about evaluating and understanding the capabilities of cutting-edge models. With new models frequently emerging, it becomes essential to assess their comparability, particularly since many achieve similar scores on standard benchmarks. However, discrepancies in ranking and performance suggest that some models might possess unique strengths and weaknesses. Identifying capabilities that are vital for real-world AI applications but remain challenging for most models is crucial for progress in AI research and deployment. To tackle these assessment challenges, our recent open-source release, "EUREKA: Evaluating and Understanding Large Foundation Models, " presents a comprehensive analysis of twelve advanced proprietary and open-weight models. At the heart of this analysis is the Eureka framework, designed for standardized evaluations of large foundation models beyond mere score reporting. This framework supports both language and multimodal assessments and allows developers to create custom pipelines. By fostering transparent evaluation practices, we aim to collaborate with the community to refine measurements for newly emerging capabilities and models. Eureka focuses on challenging and underexplored capabilities not addressed by mainstream benchmarks.

Rather than concentrating on saturated benchmarks, which limit analytical depth, Eureka emphasizes the identification of model strengths in various scenarios. This nuanced comparison reveals that models achieve comparable overall performance not through identical capabilities but through diverse complementary skills, similar to athletes excelling in different triathlon disciplines. Another aspect of model evaluation involves consistency, which is vital for user trust. Our analysis highlights that many models lack full determinism, showing variability in outputs even with controlled variables. Additionally, we identify backward compatibility issues, where even slight updates may lead to regressions in model responses, creating challenges for application developers. Key insights from our findings reveal that no single model excels in all areas, although models like Claude 3. 5 Sonnet and GPT-4o 2024-05-13 perform well across multiple dimensions. Notably, the evaluated models display distinct strengths in instruction following, yet struggle with factual precision and grounding during information retrieval. The observations underline the necessity for continuous improvement in AI models and the importance of addressing the gaps seen even among top-tier models. EUREKA not only provides a snapshot of current AI evaluations but also sets the stage for future collaborations with the open-source community to enhance measurement standards for evolving capabilities and models.