The most accurate AI for data analysis runs code, and shows it

Ask a chatbot to multiply two three-digit numbers without a calculator and it will often get it wrong. When researchers tested this in 2023, GPT-4 managed 59% (Dziri et al., Faith and Fate). Models are much better now. The principle hasn't changed: a language model predicts text, and a number is text.

That's why every major vendor routes calculation to code. OpenAI describes its code interpreter as the way to "solve complex problems in domains like data analysis, coding, and math". Anthropic's code execution tool lets Claude "perform complex calculations" in "a secure, sandboxed environment". Google tells developers to turn on code execution "whenever the model needs to perform any kind of arithmetic, counting, or calculation" (Gemini API docs, October 2026).

So our first rule is simple. If the tool doesn't run code on your data, it isn't doing analysis. It's describing numbers it has read, and sometimes ones it hasn't.

All three big assistants can do this: ChatGPT's data analysis and Claude's code execution both run Python on uploaded files. Which one you prefer matters less than whether code ran. See Claude vs ChatGPT for data analysis for a feature comparison.

Data-analysis benchmarks span a huge range, and the range is the lesson.

Two cautions. First, tuned agent systems now post much higher Spider 2.0 numbers on its leaderboards (96.70 on the Snow variant), so a 2024 headline figure is out of date. Second, the answer keys themselves are imperfect: an audit found annotation errors in 52.8% of BIRD Mini-Dev items and 62.8% of Spider 2.0-Snow items (Jin et al., January 2026).

What survives both cautions is the shape. Narrow, single-step tasks look nearly solved. End-to-end work on messy business data does not.

The error that worries us most in AI analysis isn't a bad sum. It's a clean, correctly calculated answer to a question you didn't ask.

Take churn. ChartMogul's own example has three customers paying $20, $30 and $50. The $50 one leaves. Customer churn is 33%; revenue churn is 50%. Same data, two honest answers. Stripe's subscriber churn adds new subscribers to the denominator, which gives a third. Ask an AI for "churn" and it will pick one, usually without saying which.

The same happens everywhere:

• "Users" in GA4 means active users in reports, not total users (Google Analytics Help).
• ROAS is conversion value divided by spend; ROI uses profit. A model that mixes them is off by your whole margin (Google Ads Help).
• A month might be calendar or rolling 30 days. GA4's MAU is rolling.

Irrelevant detail makes it worse. Adding one plausible but irrelevant clause to maths word problems dropped GPT-4o's accuracy from 95.2% to 63.1% (Apple, GSM-Symbolic, 2024). Real exports are full of irrelevant columns.

See return on ad spend for one metric's competing definitions.

We wouldn't choose on a leaderboard. We'd choose on five requirements, in this order:

1. It runs code you can read. Python or SQL, visible, not hidden behind a summary.
2. It works on the source, not a copy. A connection to your warehouse beats a pasted CSV that was out of date when you exported it.
3. It states its definitions. Every metric comes with the formula and filters used.
4. You can re-run it. Next month's number should come from the same steps, not a fresh conversation.
5. It leaves an artifact. A query, notebook or canvas someone else can open. Our post on why AI analysis tools need to produce artifacts, not transcripts makes the full case.

The fair counter-argument: for a one-off look at a small CSV, ChatGPT's or Claude's built-in file analysis is fast and good enough, and setting up anything heavier would be overkill. We agree. The requirements above start to matter when the number goes in a report, a board pack or a decision.

For prompts that make any of these tools state definitions up front, see prompts for data analysis.

AI analysis fails quietly. The chart renders, the number is plausible, and the error is in a filter three steps back.

A two-minute check

1. Ask for the code or SQL, then read only the WHERE clause and the date range. Wrong filters and windows cause more errors than wrong maths.
2. Check the row count. Does the number of rows it analysed match what you expect from the source?
3. Recompute one figure by hand from totals: total value divided by total spend, not an average of daily ratios.
4. Ask "what definition of [metric] did you use?" Compare it with the one your team reports.
5. Change one thing and re-run. Drop an irrelevant column or reword the question. If the answer moves, find out why.

These checks catch the common failures. They don't show you how the answer was built, step by step. Count does: connect it to Claude or ChatGPT and each claim in the answer is labelled, with a reasoning chain showing the query or calculation it rests on and any assumption it made along the way. The canvas also runs SQL and Python against your warehouse, so you can re-run the step yourself. Our guide on how to check AI answers covers the full method.

What is the best AI for data analysis?

Can ChatGPT analyse data accurately?

Can Claude analyse data?

Is AI good at writing SQL?