Best LLMs for Data Analysis

Data analysis is one of the most clearly defined LLM use cases — and one where the capability differences between models are most measurable. Unlike creative writing or customer support, data analysis has right answers. The code either runs or it doesn't. The statistical interpretation is either correct or it isn't. The SQL query returns the right results or it doesn't.

This also makes it an area where benchmarks are relatively more predictive. Coding benchmarks, math reasoning, and structured task performance all correlate with real data analysis quality in ways that don't always hold for softer tasks.

What "Data Analysis" Covers

Data analysis as an LLM use case breaks into several distinct sub-tasks:

Code generation for analysis — Writing Python (pandas, numpy, sklearn, matplotlib), R, or SQL to analyze data. This is the most common AI-assisted data analysis workflow: "here's my dataframe, write code to do X."

Text-to-SQL — Translating natural language questions into SQL queries against a schema. See the dedicated text-to-SQL report for deep analysis of this sub-task.

Statistical interpretation — Explaining what a result means. "The p-value is 0.03, what does this tell us?" or "What does this correlation coefficient imply about the relationship?"

EDA (exploratory data analysis) guidance — Given a dataset description or sample, suggesting what to look at, what distributions to examine, what anomalies to investigate.

Chart and visualization reading — Interpreting existing visualizations. "What does this chart tell us about the trend?" This requires visual reasoning if the chart is an image, or code/text reasoning if the chart data is provided.

Report generation — Writing a data-backed narrative from analysis results. "Here are the numbers, write an executive summary."

Rankings

IQ vs. Accuracy for Data Analysis

Two BGI dimensions are particularly predictive for data analysis quality:

IQ (reasoning) predicts performance on complex multi-step analysis — problems where you need to chain multiple operations, handle edge cases, debug incorrect code, or work through statistical reasoning from first principles.

Accuracy predicts faithfulness to the data — whether the interpretation reflects what the numbers actually say, or whether the model elaborates beyond what's supported. In data analysis, an overconfident interpretation of ambiguous data can be as harmful as a flat error.

The best data analysis models score well on both. Models that are high IQ but low Accuracy produce fluent, confident-sounding analyses with subtle errors. Models that are high Accuracy but low IQ are conservative and correct but struggle with complex, multi-step problems.

Python and Pandas Performance

The most common data analysis workflow is Python-based: load data with pandas, clean and transform, compute statistics, generate visualizations. Model performance on this workflow is well-predicted by coding benchmark scores, particularly BigCodeBench (which includes data manipulation tasks).

Key patterns:

• Top-tier models write correct pandas idioms without being explicitly told which functions to use
• Mid-tier models often write correct but inefficient code (nested loops instead of vectorized operations)
• The gap is largest on tasks involving complex groupby operations, multi-index dataframes, and time series manipulation

For Jupyter notebook workflows specifically: Models that maintain context across multi-cell conversations perform significantly better than those that treat each prompt independently. This is more an architecture/context management question than pure model quality.

Statistical Reasoning

Statistical interpretation is where model quality diverges most sharply from pure coding ability. A model can write correct pandas code and still produce incorrect statistical conclusions.

Common failure modes:

• Conflating correlation and causation in narrative output
• Misinterpreting p-values (the classic "p=0.05 means 95% probability the effect is real" error)
• Ignoring confidence intervals and treating point estimates as certain
• Not flagging when sample sizes are too small for the conclusions being drawn
• Comparing means without checking variance or distribution assumptions

Models with strong math benchmark performance (particularly MATH and GPQA) tend to handle statistical reasoning better. This is one area where the IQ dimension rankings are a reliable filter.

Text-to-SQL

SQL generation from natural language is a major component of data analysis for business intelligence, analytics engineering, and data democratization workflows. It deserves its own treatment — see Best LLMs for Text-to-SQL for a full analysis.

The short version: SQL generation quality is well-benchmarked (Spider, BIRD, SQA), and there's significant variation across models, especially for complex queries with multiple joins, subqueries, and aggregations.

Code Execution and Iterative Analysis

The best data analysis workflows are iterative: run code, see the output, adjust based on what you see. Models that work well in this loop need:

1. Good error recovery — When code fails, diagnose the error and fix it rather than just retrying the same thing
2. Context retention — Remember what was already computed in earlier cells
3. Output interpretation — Understand what the output means and propose the right next step

For agentic data analysis (code interpreter-style deployments), these properties matter as much as raw coding ability. The coding benchmarks don't fully capture this because they don't test iterative debugging quality.

Open-Weight Options for Data Analysis

Several open-weight models perform well on data analysis tasks, making them viable for privacy-sensitive deployments where data can't leave your infrastructure:

• Code-specialized fine-tunes of Llama 3.x have strong Python performance and are practical for code generation tasks
• DeepSeek-Coder variants are specifically optimized for coding tasks including data manipulation
• Qwen-Coder is competitive on coding benchmarks with good multilingual documentation support

For teams that need to run analysis on sensitive data (personal data, proprietary business data, clinical data), self-hosted open-weight models are often the only viable path regardless of quality tradeoffs.

Related Reports

• Best LLMs for Text-to-SQL — Deep analysis of SQL generation specifically
• Best LLMs for Code Generation — Broader code generation rankings
• IQ Rankings — Reasoning dimension rankings, strong predictor for analytical tasks

Full use-case rankings at /use-cases. Methodology at /methodology.