What we tested these two models on, what those tests found, and the published rates and limits that hold whatever the job is. Three jobs have a full write-up, and the lead changes with whether the work is exact or wide.
Aug 28, 2026 · 6 min read
We are not claiming one of these two is the better model. Which one leads depends on whether the job needs an exact answer or a wide one.
Three jobs have a full write-up behind them, and eleven further articles put one of these two models against a different one. The cards below open all three, and the index further down lists the rest, so nothing here has to be taken on trust. Several of the sharpest margins come from OpenAI's own launch table, and every row that rests on it says so.
What we compared is set out underneath. First the published rates and limits both models bring to any job, then the model-level dimensions where an evaluation or a documented capability separates these exact versions. Those hold whatever you are doing. Which of the two to reach for does not, which is why the jobs come first.
One thing to be clear about before the tables. This page compares the two models through their APIs in one neutral setup, not the apps around them, so a spreadsheet add-on or a meeting recorder is not part of anything here.
Neither model wins in general, so this pair is settled one job at a time. Each card names a job we tested, says which model took it and why, and opens the full test behind that answer. Three jobs on this pair have that test, and the index further down carries the rest of the library.
Both models, at different stages. GPT-5.5 for code, exact arithmetic and structured office files. Gemini 3.1 Pro for a very large dataset, for a chart or a recording in the same context, and for cost once the volume is real.
Learn moreA tie on the core job. Both pull the same decisions, owners and next steps out of a transcript. GPT-5.5 writes the version that goes to a client, and Gemini 3.1 Pro is the one to run when the transcripts are long or many and the bill matters.
Learn moreGemini 3.1 Pro for breadth and cost across a hundred or more languages. GPT-5.5 when a glossary term or a brand tone has to be followed to the letter. Which one leads also moves with the target language.
Learn moreThe published figures both models bring to any job. The last column reads them for the pair rather than for one task.
Figures from OpenAI and Google documentation. Both price lists were re-fetched at the source on 28 August 2026, and the limits and modalities are carried from the three task pages. Gemini's output price includes its thinking tokens and the two vendors count text differently, so cross-model cost arithmetic is directional.
The general layer, underneath the jobs above. These are model-level dimensions measured on the exact versions, so they hold whatever the job is. The three widest margins come from OpenAI's own launch table and are marked vendor-reported.
The coding, mathematics and office-file rows come from OpenAI's own launch table, where OpenAI both ran the tests and reported the competitor's score. Google published no equivalent table for the reverse, so those three margins should be read as the vendor's own account rather than as a neutral result.
Every article on this site that puts one of these two models under a graded test, grouped by model. The three on this exact pair are the cards higher up the page.
Where else we tested GPT-5.5
Where else we tested Gemini 3.1 Pro
The three widest margins on this page were produced by one of the two vendors, and one of the two models is still a preview endpoint.
OpenAI ran the coding, mathematics and office-file evaluations and reported Gemini's scores in the same table. That is not fabrication and it is not neutral either, and Google published nothing equivalent for the reverse direction, so the shape of the evidence favours whoever published more. The long-context row rests on one independent comparison rather than a graded board, and the translation rows lean on a competition that scored Google's dedicated translation model rather than this exact version.
Gemini 3.1 Pro is also still labeled preview with no announced shutdown date, so a result measured today can change without a version bump. GPT-5.5, meanwhile, is no longer the model OpenAI recommends: the GPT-5.6 family is. Both versions are served and everything here describes them accurately, but a team choosing today should price the current models in the same test.
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 GPT-5.5 against Gemini 3.1 Pro.
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