Gemini vs GPT

How we compared them
Model against model

We make no claim that one of these two providers is better. Each provider ships a line-up, and the answer changes with the model and with the work.

Eight articles on this site put a Gemini model against a GPT model on a named job, and one of them is a parent page carrying a full head-to-head on the pair we tested most. The cards below open the four with the clearest answers, and the index further down lists the rest. Where the public evidence is thin the pages say so rather than filling the gap with a verdict.

Underneath the cards are the facts that do not move with the job: what each line-up holds and costs, then the dimensions where a graded board or a documented capability separates two specific versions. This is the pairing where those constants pull hardest in opposite directions, since one line-up takes more kinds of input and the other returns more in one pass.

One thing to be clear about before the tables. Every test behind this page ran through the providers' APIs in one neutral setup. It is not a comparison of the apps around them, so a file upload, a browser extension or a spreadsheet add-on is not part of anything here.

Where the answer is clearest
Four tests worth reading first

Each card names a job we put the two providers' models through, says which model took it and why, and opens the article behind that answer. The method is the same in all four: one prompt, one setup, and score what comes back before editing it.

The line-ups
What each provider ships

The published figures behind every test on this page, at the level of the line-up rather than one version. Where the models differ from each other, both ends are named.

Head to head
Where the evidence separates models

These rows sit underneath the jobs above rather than competing with them. Each one compares two named versions on a dimension that holds whatever the job is, because that is the level the evidence exists at. A provider-level version of this table would be a guess.

Everything we tested
The other four articles

Every remaining article on this site that puts a Gemini model against a GPT model, grouped by the kind of work rather than by version. The four above are not repeated here.

What this cannot tell you
The limits of everything above

Every figure here is published and every verdict is attached to a job somebody tested. That still leaves three things this page cannot do for you.

Gemini's advantage on price is dated: the rates it is billed at today run to December 31 2026 and Google publishes higher ones from January. Half of the Gemini tests behind this page are on a model still labeled preview, with no shutdown date announced, which is a real constraint for a team that cannot deploy one. And the capability index above is a composite rather than a grade on your work, so treat a five-point gap as a direction and not a margin.

Playgram is not the right buy for everyone either. If one person needs one model and nothing else, a single vendor subscription is simpler and cheaper than a workspace built for a team.

The safest last step is to test the shape of your own material rather than a generic prompt from the internet. A fair test needs the same setup on both sides: the same sources, 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, because most teams end up running one model in one app and the other somewhere else on a separate subscription, which tilts the comparison before the first answer arrives. The cleaner the setup, the more the difference you see is really one model against the other.

Frequently asked
questions

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