Best LLM for Data Analysis
Interpreting datasets, writing SQL and Python analysis scripts, and generating insights.
Get started freeData analysis LLMs must reason correctly about numbers, generate accurate and executable code, and translate raw data into clear, actionable narrative. Unlike writing errors that are easy to spot, quantitative mistakes can go undetected - making model reliability and confidence calibration especially critical for data workflows.
What to look for in a Data Analysis LLM
- 1Accuracy of quantitative reasoning and calculations
- 2Quality of SQL and Python code generation
- 3Ability to interpret charts and structured data
- 4Clear, concise data-driven narrative generation
Top 4 AI Models for Data Analysis
Ranked by performance on data analysis tasks
GPT-5.5
OpenAI's smartest and most capable model yet for agentic coding, knowledge work, and computer use, delivering a new class of intelligence at GPT-5.4 latency.
Compare with top pickClaude 4 Opus
The flagship model, focused on deep reasoning, large-scale coding and sustained multi-step agentic workflows.
Compare with top pickGPT-5.4
OpenAI's frontier model for complex professional work with best intelligence at scale for agentic, coding, and professional workflows.
Compare with top pickClaude 4 Sonnet
A balanced-hybrid reasoning model tuned for everyday assistant and high-volume tasks.
Compare with top pickData Analysis Model Comparisons
Head-to-head comparisons filtered for data analysis performance
GPT-5.5 vs GPT-5.4
for Data Analysis
GPT-5.5 vs GPT-5.2
for Data Analysis
GPT-5.5 vs GPT-5.1
for Data Analysis
GPT-5.5 vs GPT-5.3 Codex
for Data Analysis
GPT-5.5 vs GPT-5.2 Codex
for Data Analysis
GPT-5.5 vs GPT-5.1 Codex
for Data Analysis
GPT-5.5 vs Sora 2
for Data Analysis
GPT-5.5 vs Sora 2 Pro
for Data Analysis
GPT-5.5 vs GPT-5
for Data Analysis
GPT-5.5 vs GPT-5 Codex
for Data Analysis
GPT-5.5 vs GPT-5 Mini
for Data Analysis
GPT-5.5 vs GPT-5 Nano
for Data Analysis
GPT-5.5 vs GPT-5 Pro
for Data Analysis
GPT-5.5 vs GPT-4.1
for Data Analysis
GPT-5.5 vs GPT-4.1 Mini
for Data Analysis
GPT-5.5 vs GPT-4.1 Nano
for Data Analysis
GPT-5.5 vs GPT-OSS 120B
for Data Analysis
GPT-5.5 vs GPT-OSS 20B
for Data Analysis
GPT-5.5 vs GPT Image 1.5
for Data Analysis
GPT-5.5 vs GPT Image 1
for Data Analysis
GPT-5.5 vs GPT Image 1 Mini
for Data Analysis
GPT-5.5 vs o4-mini
for Data Analysis
GPT-5.5 vs o3
for Data Analysis
GPT-5.5 vs o3-mini
for Data Analysis
GPT-5.5 vs o1
for Data Analysis
GPT-5.5 vs o1-pro
for Data Analysis
GPT-5.5 vs GPT-4o
for Data Analysis
GPT-5.5 vs GPT-4o mini
for Data Analysis
GPT-5.5 vs GPT-4o Audio
for Data Analysis
GPT-5.5 vs GPT-4o mini Audio
for Data Analysis
GPT-5.5 vs GPT-4 Turbo
for Data Analysis
GPT-5.5 vs GPT-3.5 Turbo
for Data Analysis
GPT-5.5 vs Gemini 3.1 Pro
for Data Analysis
GPT-5.5 vs Nano Banana 2
for Data Analysis
GPT-5.5 vs Gemini 3 Pro
for Data Analysis
GPT-5.5 vs Nano Banana Pro
for Data Analysis
GPT-5.5 vs Gemini 2.5 Pro Experimental
for Data Analysis
GPT-5.5 vs Gemini 2.5 Flash
for Data Analysis
GPT-5.5 vs Nano Banana
for Data Analysis
GPT-5.5 vs Gemini 1.5 Pro
for Data Analysis
GPT-5.5 vs Gemini 1.5 Flash
for Data Analysis
GPT-5.5 vs Gemini 1.0 Pro
for Data Analysis
GPT-5.5 vs Claude 4.7 Opus
for Data Analysis
GPT-5.5 vs Claude 4.6 Sonnet
for Data Analysis
GPT-5.5 vs Claude 4.5 Sonnet
for Data Analysis
GPT-5.5 vs Claude 4.5 Haiku
for Data Analysis
GPT-5.5 vs Claude 4.6 Opus
for Data Analysis
GPT-5.5 vs Claude 4.5 Opus
for Data Analysis
GPT-5.5 vs Claude 4.1 Opus
for Data Analysis
GPT-5.5 vs Claude 4 Sonnet
for Data Analysis
GPT-5.5 vs Claude 4 Opus
for Data Analysis
GPT-5.5 vs Claude 3.5 Sonnet
for Data Analysis
GPT-5.5 vs Claude 3.5 Haiku
for Data Analysis
GPT-5.5 vs Claude 3 Opus
for Data Analysis
GPT-5.5 vs Claude 3 Sonnet
for Data Analysis
GPT-5.5 vs Claude 3 Haiku
for Data Analysis
GPT-5.5 vs Grok 4
for Data Analysis
GPT-5.5 vs Grok 3
for Data Analysis
GPT-5.5 vs Grok 3 Mini
for Data Analysis
GPT-5.5 vs Qwen3-Max
for Data Analysis
Found your model? Now build a data analysis tool that actually works.
Knowing which LLM is best for data analysis is step one. Step two is shipping a tool your team actually uses - not copy-pasting the same prompt into ChatGPT every day.
- Powered by GPT-5.5 - swap any time
- No coding. Live in minutes.
- Share with your team - one tool, everyone aligned
Frequently asked questions about Data Analysis LLMs
Which LLM is best for data analysis tasks in 2026?
GPT-5.5 and Gemini 2.5 Pro are the top data analysis LLMs in 2026. GPT-5.5 leads on quantitative reasoning and complex multi-step calculation tasks. Gemini 2.5 Pro is particularly strong on interpreting structured data, large tables, and multi-modal inputs like charts and graphs. Claude 4 Opus is the best choice when generating analysis narratives and executive summaries alongside the data work.
Can an LLM write correct SQL queries?
Yes, modern LLMs are highly capable SQL writers for standard query patterns - joins, aggregations, window functions, CTEs, and subqueries. Accuracy improves significantly when you provide your schema, sample data, and clearly state what question you want answered. GPT-5.5 and Claude 4 Opus are the most reliable for complex SQL with edge cases and performance optimisation requirements.
Is GPT or Claude better for Python data analysis?
GPT-5.5 is generally stronger for Python data science tasks, producing more idiomatic pandas, numpy, and scikit-learn code. Claude 4 Opus tends to write cleaner, better-documented Python with more thorough error handling. For Jupyter notebook workflows that mix code and narrative explanation, Claude 4 Opus is often preferred for the quality of its inline comments and narrative.
Which model handles large data tables and CSV files best?
Gemini 2.5 Pro handles large structured inputs most effectively due to its 1M token context window and strong performance on multimodal data including pasted tables, CSV samples, and chart images. For very large files, chunk your data or use a vector database with RAG rather than pasting raw content directly.
Can I trust LLM-generated data analysis and calculations?
Treat LLM outputs as a first draft that requires verification. LLMs can make arithmetic errors, misinterpret units, and confidently state incorrect statistical conclusions. Always validate calculations programmatically and cross-check key findings against your source data. Use LLMs for exploration, hypothesis generation, and code scaffolding - then verify the outputs before presenting to stakeholders.