This page compares two current models on one job: data analysis. It looks at accuracy, coding, large datasets, cost and prompting, and it ends with a fair way to test them on your own data.
Jul 17, 2026 · 11 min read
GPT-5.5 is usually the better choice when the work leans on code, exact math and structured files. Gemini 3.1 Pro is usually the better choice for very large datasets, mixed media and cost at scale.
That split shows up across official positioning and eval tables1, 2, the Gemini model card3 and independent trackers6. It is why many teams stop trying to pick one model for every kind of analysis.
The practical move is to match the model to the job. For heavy scripting, precise calculations and spreadsheet-style files, start with GPT-5.5. For large data dumps, charts and other non-text inputs, start with Gemini 3.1 Pro. For a broad mix, explore with Gemini, then hand the exact math or final code to GPT-5.5.
You summarise tables, find patterns and turn raw data into a readable brief. Gemini reads large inputs cheaply and explains them clearly, which fits fast exploratory work.
You want the model to write and debug analysis code, run calculations and chain tools. GPT-5.5 has the stronger coding and math record for that precise work.
Your data includes charts, PDFs, screenshots or recordings, not only numbers. Gemini reads text, images, audio and video natively, so it folds them into one analysis.
You run analysis at scale or on a tight budget. Gemini's lower per-token price adds up, while GPT-5.5 earns its higher price on the jobs that must be exact.
This page treats each one as a data-analysis model reached through its API, not as a whole app. So it weighs the parts of analysis that show up in real work.
Those parts are accuracy on numbers, coding for analysis, how each model handles very large inputs, how well it reads mixed media, how it follows a data question, structured output, speed and cost. Official sources come first, then independent tests with clear methods.
We left tools out of the spec table on purpose. A code sandbox, a spreadsheet add-on, file upload and a database connector depend on the app around the model, so the same model can behave very differently in a chat product, in the API or inside a workspace. Judging those here would compare wrappers, not the model.
The model facts that actually affect a data-analysis job. Tool features are left out, since they change with the app around the model.
Figures from OpenAI and Google documentation with an independent pricing tracker, July 2026. OpenAI also lists a higher-accuracy GPT-5.5 Pro at $30 in / $180 out per million1. The two vendors count tokens differently, so cross-model cost math is directional, not exact.
The answer changes by the kind of analysis, not by brand. This is the main analysis: which model has the edge on each part of a data workflow, and what backs it up.
Better-choice calls come from official model pages and vendor eval tables, with an independent tester for behaviour at extreme context. Vendor eval numbers are self-reported, so read them as directional and test your own data.
A useful test feels boring. Same data, same question, same setup, same scoring. Then judge what your team actually pays for: did it read the right columns, get the numbers right, hold the format and need less hand editing.
Use analysis your team really does: summarise a sales CSV, find anomalies in a sensor log, answer a question from a report, and build a short chart-backed brief. Skip toy prompts, since they do not show how a model behaves on your data.
One prompt that states the data, the goal and the output format. Neither model gets a richer version. If you change the prompt mid-test, apply the change to both.
Same data and the same place to run them, whether that is the API or a shared workspace. Otherwise you are testing the app around the model, not the model.
Verify each result against the source data, note any invented trend, and count the edit time before the output is usable. Hide the model names while scoring, and for high-stakes work have an expert review blind.
Jobs you can run yourself, with the pattern the public evidence suggests. It sums up official docs, launch demos and independent tests rather than promising a fixed result.
One reality check on range: OpenAI's launch showed GPT-5.5 turning a gene-expression dataset of 28,000 genes across 62 samples into a research report, a heavy multi-step analysis1. It shows what the models can reach, not a guarantee, so test the shape of your own data.
The best prompt style is not the same for both. Matching the prompt to the model does more for a clean result than the model choice alone.
GPT-5.5 does best when you state the data and the end goal plainly and let it work out the steps1. Give it the table and the question, ask it to show its calculations, and it will usually compute the changes and return a tight summary. Say the output format you want, since it follows format rules well only when you name them.
Gemini 3.1 Pro does best with the data first and the question after. Label the dataset clearly, set a role like a data analyst, then ask for the analysis and the exact output shape, such as a JSON object or a markdown table3. For a hard problem, break it into steps, and if the answer runs long, ask it to focus on the two most important findings.
A GPT-5.5 prompt: state the goal and let it work
You are analyzing sales data for a finance team.
Data:
[paste the CSV rows here]
Task:
Find the notable trends and anomalies in this data.
Do this:
- Compute month-over-month change for each region
- Flag any value more than 3 standard deviations from the mean
- List the calculations you ran before the conclusions
- Return the result as 5 short bullet points
If a number looks wrong, show your working rather than guessing.A Gemini 3.1 Pro prompt: data first then the task
Role: You are a data analyst helping interpret survey results.
Dataset:
[paste the table here, clearly labeled]
Task:
1. Summarize what the data looks like
2. Identify the country with the most volatile growth
3. Identify the country with the steadiest growth
Output:
Return a markdown table with the columns Country, Trend and VolatilityRank,
then two sentences explaining each pick.Neither model is perfect. The useful question is where each one adds cleanup work, and what to change in the prompt or the workflow.
A quick decision flow. Find the job that matches most of your work, then start with the model on that branch.
Use it as a starting point and test on your own data before you commit
If your work is heavy on scripting and precise calculations, GPT-5.5 is the better default. Its coding and math records are stronger in the vendor evals2, and it holds structured formats well for release-ready analysis.
If your work involves images, audio or other non-text data, Gemini 3.1 Pro is the safer default. It reads those formats natively3, so it folds a chart or a recording into the analysis without extra steps.
If your datasets are very large or your budget is tight, start with Gemini for its steadier long-context behaviour7 and lower per-token price6. And if each analysis is high-stakes, use GPT-5.5 for the precise work and add a check on any number before it ships. Many teams run both: Gemini to explore, GPT-5.5 to verify.
If we had to reduce it to one line: GPT-5.5 is the more precise analyst and coder, and Gemini 3.1 Pro is the broader, cheaper workhorse for large and mixed data.
That is a simplification, but it is a fair read of the current evidence. Two caveats matter. The benchmarks disagree by task, and most of the head-to-head numbers come from OpenAI's own eval tables2, which are self-reported. And Gemini's independent evidence is thinner so far, since much of it comes from Google's own demos4 rather than wide third-party testing.
This page does not assume anything about hidden training data or private tuning. Where the evidence was missing or one-sided, the tables say so rather than guessing.
The safest final step is to test the shape of your own data, not a generic prompt from the internet. A fair test needs the same setup for both models: the same data, the same prompt, and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first answer even comes back. The cleaner the setup, the more the difference you see is really GPT-5.5 against Gemini 3.1 Pro, and not just which one happened to be easier to reach that day.
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