The short answer
The best AI for data analysis in November 2026 is OpenAI's ChatGPT, because it writes and runs Python in a Jupyter notebook environment on your uploaded spreadsheets, PDFs and data files, shows the code for review, and draws interactive bar, line, pie and scatter charts. Anthropic's Claude is the runner-up for careful reasoning over long material and live Dashboards (beta) on paid plans. Claude's free plan, with code execution and file uploads on Claude Sonnet 5.5, is the best free option.
| Our pick | ChatGPT (OpenAI): Python analysis, interactive charts, Plus plan for GPT-6 Sol and higher limits |
|---|---|
| Runner-up | Claude (Anthropic): Claude Opus 5.5 on Pro, Dashboards (beta) from Pro |
| Best free option | Claude Free: Sonnet 5.5 and Haiku 5.5, code execution, files, limited usage every 5 hours |
| Inside Excel or Sheets | Copilot in Excel or Gemini in Google Sheets; see our spreadsheets page |
| Files ChatGPT accepts | .xls, .xlsx, .csv, PDFs, .json, .xml, .yaml, .txt, .md |
| Training on your data | ChatGPT and Claude personal plans train unless you switch it off; business plans do not by default |
The short version: which AI to use for data analysis
For most people who need to make sense of a spreadsheet, a survey export or a sales file, start with ChatGPT on the Plus plan. It does the analysis by writing and running code, which means real calculations rather than guesses, and you can check that code. If your analysis is mostly reading and reasoning over long reports and messy material, or you want charts that refresh from connected business apps, choose Claude. If you cannot pay yet, Claude's free plan gives you code execution and file uploads at no cost.
If your data lives in a workbook you keep editing, you may be better served by the AI built into the spreadsheet itself. We cover Copilot in Excel and Gemini in Google Sheets in the best AI for spreadsheets.
Our pick: ChatGPT
Why it wins
OpenAI's help centre describes the process plainly: for data analysis tasks, ChatGPT "writes and runs Python code in a stateful Jupyter notebook environment". It can summarise rows, columns, trends and outliers, create tables for row-by-row review, run calculations, transformations and statistical analysis, and explain its assumptions and possible next steps. It can also show pandas tables as interactive tables when that helps.
Charts are the second reason. ChatGPT creates charts from uploaded data, and bar, line, pie and scatter charts can switch to an interactive version (select Switch to interactive chart). Since 7 October 2026, GPT-6 and Intelligent UI mean answers can also combine text with visuals, diagrams and small interactive tools such as calculators inside the chat.
It accepts the files you actually have: spreadsheets (.xls, .xlsx, .csv), PDFs, and text and data files such as .json, .xml, .yaml, .txt and .md. Where plugins are available for your account, you can attach files from Google Drive, OneDrive and SharePoint. For bigger jobs, ChatGPT Work can create documents, presentations and spreadsheets as finished files.
Who it suits
Analysts, marketers, finance and operations people who want fast answers from exports and need to show their working. Plus, Pro, Business and Enterprise use GPT-6 Sol in the Chat tab (Go, like Free, uses GPT-6 Luna); Business adds admin controls and no training on business data by default.
The catch
ChatGPT may pick an analysis method or chart type you did not intend on the first try, so name the method, grouping or column you want. The Python environment cannot reach the web or call APIs, so upload the data you need. It may not reliably read exact values from scanned PDFs or images of tables, and a large or formula-heavy file can upload but still be too complex to analyse completely. On personal Free, Plus and Pro accounts, Improve the model for everyone is on by default (OpenAI lists the same opt-out for Go): switch it off in Settings, Data controls. Free uses the smaller GPT-6 Luna with limited uploads. See our ChatGPT guide.
Runner-up: Claude
Why it is excellent
Claude is strongest where analysis is as much judgement as arithmetic: reading a 60-page report alongside a spreadsheet, spotting what does not add up, and writing a careful explanation. It runs code on every plan, including Free, and since September 2026 Anthropic has been rolling out, starting with Pro, Max and Ultra, the ability for any conversation to take on a whole task and hand back files such as a spreadsheet with working formulas, a document with the findings or a deck.
On Pro and above, Claude Dashboards (beta) builds charts from data platforms or connected apps such as Salesforce, and each chart shows its query and when it was last refreshed, which makes it easy to check where a number came from. Claude Opus 5.5, released on 22 September 2026, is available from the Pro plan.
Who it suits
People who analyse long documents and mixed material, teams that want refreshable charts from business systems, and anyone who wants the finished output as a file to share.
The catch
Every plan has usage limits, and heavy models and long tasks use them faster; even Ultra keeps a weekly limit. On Free, Pro, Max and Ultra your content can be used for training unless you turn off Help Improve our AI models in Settings, Privacy. Dashboards is still in beta. Read the Claude guide and ChatGPT vs Claude.
Best free option: Claude's free plan
Why it is the best free choice
Claude Free includes Claude Sonnet 5.5 and Claude Haiku 5.5, file uploads, code execution, file creation, web search, memory, connectors and up to 5 Projects. That is enough to upload a CSV, ask for the analysis, get it calculated with code and download a cleaned file, without paying. ChatGPT Free can also analyse files, but with limited uploads and a smaller context window (about 27,000 tokens on GPT-6 Luna), so long files hit the limit sooner.
Who it suits
Students, small business owners and anyone analysing an occasional export.
The catch
Usage resets every 5 hours with a weekly cap, there is no Claude Opus, and Dashboards needs Pro. Switch off training in Settings, Privacy if your data is sensitive, or better, keep personal and customer data out of a free consumer plan altogether. Google's free Gemini app is another option for quick questions, though since 9 October 2026 free users are mostly answered by the smaller Flash-Lite model. See the Gemini guide.
How to choose: three questions
- Is the data a one-off file or a living workbook? For one-off exports, use ChatGPT or Claude. For a workbook your team keeps editing, use Copilot in Excel or Gemini in Sheets so the results stay in the file.
- Is it numbers, or numbers plus a lot of reading? Pure calculation and charts favour ChatGPT. Long reports, contracts or survey comments next to the numbers favour Claude.
- How sensitive is the data? Customer or employee data belongs on a business plan (ChatGPT Business or Enterprise, Claude Team or Enterprise, Microsoft 365 Copilot with a work account), where content is not used for training by default. Read how to keep your data private in AI tools.
A workflow: from raw file to a decision
- Prepare the file. OpenAI's own advice: descriptive column headers in the first row, one row per record, plain-language column names, and no unrelated tables, empty separator rows or pictures of numbers. Remove names and emails you do not need.
- Ask it to describe the data first. Before any analysis, ask for the columns, data types, row count, missing values and anything odd. This catches a wrong import immediately.
- State the question and the method. "Compare average order value by region for Q3 versus Q2, using the order_total column, excluding refunds." Vague questions get vague methods.
- Check the working. Ask to see the code and the assumptions. Recalculate one number yourself, for example a total, in your spreadsheet.
- Chart only what answers the question. Name the chart type. Ask for one chart per finding, with a title that states the finding.
- Write it up for the reader. Ask for three findings, what they mean and what the data cannot tell you.
- Save the method. Keep the prompt and code so next month's analysis is the same, or set it up in a Project.
Prompts to copy for AI data analysis
These seven prompts work in ChatGPT and Claude. Upload your file first and replace the [placeholders]. Read how to analyse data with AI for a longer walkthrough.
Prompts to copy
Check the data before analysing it
Before any analysis, describe this file: number of rows and columns, each column's name, data type and example values, missing or blank values per column, duplicates, and anything that looks wrong such as dates in different formats or totals that do not match. Do not draw conclusions yet.
You get: A data health check that catches import and quality problems before they spoil the analysis.
Answer one business question with the method stated
Question: [for example, which product category grew fastest from Q2 to Q3]. Use the columns [column names]. Exclude [rows to exclude]. Show the code you ran, a small results table, and state every assumption you made. If the data cannot answer the question, say so.
You get: A calculated answer with visible code, a table and stated assumptions.
Find trends and outliers
Look at [metric] over [time column], split by [category column]. Describe the overall trend, the three biggest changes, and any outliers more than [threshold] away from normal. For each outlier, show the rows behind it so I can check whether it is real or a data error.
You get: Trends, the biggest movements and outliers traced back to the underlying rows.
Make one chart that makes the point
Create a [bar, line or scatter] chart of [metric] by [dimension] for [period]. Title it with the main finding in plain words, label the axes with units, sort the bars from largest to smallest, and highlight [category]. Then give me the chart data as a table.
You get: A clear, labelled chart plus the numbers behind it.
Analyse survey comments with the scores
This file has survey scores in [score column] and free-text comments in [comment column]. Group the comments into no more than [number] themes, count how many comments fall in each, show the average score per theme, and quote two short example comments per theme.
You get: Themes from open comments linked to scores, with counts and examples.
Compare two files
I have uploaded [file A] and [file B]. Match rows on [key column]. List rows that appear in only one file, rows where [columns] differ, and a summary count of each. Return the differences as a downloadable spreadsheet.
You get: A reconciliation of two files with the differences in a spreadsheet.
Write the findings for a busy reader
Based on the analysis above, write a summary for [audience] in under 200 words: the three most important findings with numbers, what each means for [decision], and one clear limitation of this data. No jargon.
You get: A short, decision-ready write-up that also states what the data cannot show.