ChatGPT vs Analistable for asking questions about spreadsheets

The short version

Both are reasonable tools. The difference is less about which AI is smarter and more about where the data lives and how repeatable the result is. If you have already read our hub on answering questions across spreadsheets, this page applies those criteria to the most common choice people face.

How ChatGPT handles a spreadsheet

When you attach an Excel or CSV file, ChatGPT's data analysis feature copies the file into a temporary sandbox and writes Python, usually pandas, to read it. You can watch the code, ask follow-ups, request a chart or ask for a cleaned file back. For one-off exploration this is excellent: it copes with messy headers, can reshape a table, and explains its reasoning in prose.

• Strengths: very flexible, good at cleaning and charting, handles free-form requests (“find anything odd in this”).
• Limits: file size and count limits apply and vary by plan; the sandbox is reset between sessions, so a long piece of work may need re-uploading; results depend on the code it chose to write that time.
• Privacy: the file content is sent to OpenAI. How long it is kept and whether it may be used for training depends on your plan and settings, so check your workspace policy before uploading customer or payroll data.

How Analistable handles the same question

Analistable reads your files (or a Google Sheet, GA4 or Search Console report) in the browser and loads each one into DuckDB-WASM as a table. When you ask a question, the AI receives the schema, meaning table and column names and types, plus sample rows only if you allow them. It returns a SQL query, the query is checked to be read-only, and DuckDB runs it locally. The answer table is computed from your full data on your own machine.

• Strengths: rows stay in the browser; every answer is a SQL query you can read; joins across several sheets are first-class; a saved model reloads the same sources so next month's question is one click.
• Limits: it answers questions rather than editing files, so it will not hand back a cleaned workbook; it is less suited to open-ended “tell me a story about this data” prompts; very large files are bounded by your browser's memory.

Worked example: the same cross-file question

Suppose you have orders.csv (order_id, customer_id, total) and customers.xlsx (customer_id, region). The question is “revenue by region last quarter”. Sample data:

ChatGPT would typically read both files with pandas, merge on customer_id and group by region. Analistable writes the equivalent SQL and shows it:

SELECT c.region,
       COUNT(*)        AS orders,
       SUM(o.total)    AS revenue
FROM orders o
JOIN customers c ON c.customer_id = o.customer_id
GROUP BY c.region
ORDER BY revenue DESC;

Both give £2,500 across six orders if the keys are clean. The practical difference shows when keys are not clean: an inner join silently drops orders whose customer_id is missing from the customer file. In either tool, ask “how many orders have no matching customer?” before trusting a total. Our guide to joining two spreadsheets on a common column covers that check in detail.

Privacy and compliance

If your files contain personal data, the deciding question is whether you are allowed to send them to a third-party AI service. Many companies permit ChatGPT Enterprise or Team but not personal accounts; others forbid uploads entirely. Analistable's design sidesteps the question for the rows themselves: only the schema, and optional samples you switch on, reach the AI. That does not make it automatically compliant with your policy, but it reduces what leaves the device to column names.

Column names can be sensitive too (“hiv_status”, “salary_band”). If they are, rename them before loading in any tool.

Reproducibility and checking the answer

A ChatGPT answer depends on the code generated in that conversation. Ask the same question tomorrow and it may filter dates differently or treat blanks another way. You can mitigate this by asking it to show the code and saving that code yourself.

Analistable keeps the model (sources plus metadata) and shows the SQL for each answer, so you can re-run it after refreshing the files. SQL is also easier for a colleague to review than a long Python cell. If you already use SQL elsewhere, the same query patterns from joining multiple tables in SQL apply.

File size, shape and messy headers

ChatGPT's sandbox has file size limits and a finite amount of memory, and both vary by plan. For a few thousand rows that never matters; for a multi-million-row export it can, and the usual workaround is to ask for a sample or pre-aggregate before uploading. It is, however, forgiving of awkward layouts: title rows above the header, merged cells and notes in the margin are all things it can be asked to strip with pandas.

Analistable is bounded by your browser's memory instead. DuckDB is a columnar engine, so a few hundred thousand rows of a typical export are comfortable on a modern laptop. It expects a reasonably tidy table, one header row and one record per row, so a heavily formatted report may need its title rows removed first. If you are merging many workbooks rather than asking questions of them, the free combine Excel files tool is a quicker route.

Which to choose

1. Use ChatGPT for exploration, cleaning, charting or a quick look at a file with no sensitive data.
2. Use Analistable when the question spans two or more sheets, needs to be asked again on fresh data, or the rows should not be uploaded.
3. Use both: explore a sample in ChatGPT, then run the real question on the full data locally.

Frequently asked questions

Does ChatGPT keep my uploaded spreadsheet?

Uploaded files are processed in a temporary sandbox, but retention and training use depend on your plan and data controls. Check OpenAI's current policy and your organisation's settings before uploading sensitive data.

Can Analistable see my data?

The rows are loaded into DuckDB in your browser and are not stored on Analistable's servers. The AI receives column names and types, plus sample rows only if you enable them.

Which is more accurate?

Both can be wrong if the question is ambiguous or the join keys are messy. Accuracy improves when you can read the code or SQL behind the answer and check row counts before and after a join.

Can ChatGPT join several spreadsheets?

Yes. Upload the files and ask it to merge on a named column; it will use pandas. Name the key column explicitly to avoid it guessing.

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