GPT-5.2 combines strong structured output, reliable code generation, and natural-language explanation to make it one of the most effective LLMs for data analysis workflows. Here's a detailed breakdown.
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GPT-5.2 is one of the best models for data analysis thanks to its combination of structured output, code generation, and clear natural-language explanation. It can write pandas, SQL, R, and visualization code, interpret results, and present findings in plain English or structured formats. It particularly shines in workflows that require moving between data manipulation code and human-readable insights. Claude Sonnet 4.5 offers more thorough explanations for complex analyses, and DeepSeek V3 is more cost-effective for computation-heavy statistical work.
GPT-5.2 generates accurate pandas, SQL, R, and Python data analysis code on the first attempt. It handles data cleaning, transformation, aggregation, and visualization code with minimal iteration needed.
When you need analysis results in a specific format, such as JSON, CSV, markdown tables, or structured reports, GPT-5.2 follows formatting instructions more reliably than any other model, making it ideal for automated reporting pipelines.
GPT-5.2 excels at translating raw numbers into clear, actionable business insights. It can take a dataset summary and produce executive-ready narratives that explain trends, anomalies, and implications in plain language.
GPT-5.2 handles statistical concepts like hypothesis testing, regression analysis, confidence intervals, and A/B test interpretation accurately. It chooses appropriate statistical methods for given scenarios and explains the reasoning behind its choices.
GPT-5.2 cannot ingest very large raw datasets directly. For datasets with more than a few thousand rows, you need to provide summary statistics or have GPT-5.2 write code that processes the data externally rather than analyzing it inline.
GPT-5.2 sometimes proceeds with analysis without flagging data quality problems like missing values, outliers, or encoding issues that a careful human analyst would catch. Always include data validation steps in your workflow.
Data analysis often involves many rounds of exploratory queries. At GPT-5.2's per-token pricing, extended iterative analysis sessions can become expensive. DeepSeek V3 handles similar analytical tasks at a lower cost.
Provide schema information and sample rows rather than full datasets to stay within context limits and get more accurate code.
Ask GPT-5.2 to generate a complete analysis script rather than answering questions one at a time, so you can run it against your full dataset locally.
Use the structured output mode to get analysis results as JSON that can be piped directly into dashboards or downstream systems.
For exploratory analysis, start with GPT-5.2 to generate the initial code and methodology, then switch to DeepSeek V3 via LLMWise for iterative refinement to reduce costs.
Always ask GPT-5.2 to include data validation and sanity checks in any analysis code it generates.
How GPT-5.2 stacks up for data analysis workloads based on practical evaluation.
Claude Sonnet 4.5
Compare both models for data analysis on LLMWise
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