Best AI for Data Analysis

Data analysis sits at the intersection of coding and reasoning, making it a uniquely demanding use case for AI models. The best models for data work can write correct SQL queries, build Python data pipelines with pandas and NumPy, interpret statistical results, and explain findings in plain language. They need both technical precision and the ability to understand what the data actually means in context.

Math and coding benchmarks are your best guide when choosing a model for data analysis. Models with strong scores on GSM8K, MATH, and coding benchmarks tend to produce more accurate calculations, catch edge cases in data transformations, and write cleaner analytical code. Look for models that can handle multi-step quantitative reasoning, since real data analysis rarely involves a single simple calculation.

Consider what kind of data work you do most. If you primarily write SQL queries against databases, a fast mid-tier model may be perfectly adequate and much cheaper than a frontier model. If you need to build complex statistical models, interpret ambiguous results, or generate executive-level insights from raw data, investing in a frontier model will pay off. Models with code execution capabilities or vision features can also be valuable for iterating on charts and visualizations.