This page compares two current models on one job: turning a meeting transcript into clear notes and an action list. It looks at accuracy, cost, prompting and long transcripts, and it ends with a fair way to test them on your own meetings.
Jul 24, 2026 · 10 min read
GPT-5.5 is usually the better choice when the minutes go straight to a client or an executive and need to read clean. Gemini 3.1 Pro is usually the better choice when the transcripts are very long or very many and cost matters most.
On the core job, pulling decisions, owners and next steps out of a transcript, the two models tie. Public tests find they name the same decisions and tasks with almost no misses6. The split shows up in finishing and in cost, not in accuracy.
Both can take about a million tokens in one pass2, 3, so a whole transcript fits either model. Gemini 3.1 Pro costs less per token, which matters on long or frequent meetings4, while GPT-5.5 tends to write the more polished, client-ready summary7. Many teams use both: Gemini for the bulk draft, then GPT-5.5 for the final polish.
You need decisions, owners and next steps captured cleanly after every meeting. Both models are accurate on that core job, so start with whichever your team already uses.
You process all-day sessions, quarterly reviews and large archives. Gemini 3.1 Pro reads a full transcript in one pass and costs less per token, which adds up at volume.
Your minutes go to stakeholders, so tone and finish matter. GPT-5.5 tends to write the more polished, publishable recap with less hand editing.
You feed transcripts through the API and want the model that needs the least cleanup. Run both on your real meetings and keep the one that saves the most work.
This page compares the two models through their API, feeding each the same meeting transcript as text. It does not judge any app built around them.
A meeting-recorder app, live transcription inside a video call and calendar integration are features of the app, not the model. The same model can behave very differently inside a recorder product, in the API or in a workspace, so judging those would compare wrappers, not the models.
So the setup here is simple and identical for both: one text transcript in, structured notes and an action list out. Gemini 3.1 Pro can also take raw audio or video directly3, which we note where it matters, but the verdict rests on the text-transcript job both models do the same way.
The model facts that actually affect a meeting-notes job. Tool and app features are left out, since they change with the product around the model.
Figures from OpenAI1 and Google3, 4 documentation, July 2026. The two vendors count tokens differently, so cross-model cost math is directional, not exact.
The answer changes by subtask, not by brand. This is the main analysis: which model has the edge on each part of a meeting-notes workflow, and what backs it up.
Better-choice calls come from an independent meeting test, independent readability scores and the two vendors' pricing, cited on each row. Where accuracy ties, the row says so.
A useful test is boring on purpose. Same transcripts, same prompt, same setup, same scoring. Then judge what your team pays for: did it catch every decision and task, invent nothing, read clearly and need little editing.
Choose three to five real meetings: a short standup, a long planning call, a client call. Have them in text form, since GPT-5.5 needs a transcript rather than raw audio.
One prompt that asks for decisions with who agreed and action items with owner and deadline. Do not hand one model a richer version. If you change it mid-test, change it for both.
Run both through the API with the full transcript in one shot and no extra chat history. Keep temperature and other settings the same, since a chat product can add hidden tweaks.
Check coverage and accuracy first, then clarity and how much editing each draft needs. For minutes that go outside the team, hide the model names and have a person review.
Meetings you can run yourself, with the pattern the evidence suggests. It sums up independent tests and vendor pricing rather than promising a fixed result.
One caution from the tests: if a decision is only implied, GPT-5.5 can state it more firmly than it was actually agreed, so a quick human check still matters for high-stakes minutes.
The best prompt is not the same for both. GPT-5.5 does well from one detailed ask, while Gemini 3.1 Pro often does better with a summarize-then-refine pass.
GPT-5.5 follows a multi-part instruction in one go, so spell out the sections and the fields you want. Ask for Decisions and Action items as separate lists, with who agreed and the owner and deadline on each line, and it will usually deliver that on the first try7.
Gemini 3.1 Pro returns accurate points but can list them flat, with no sense of what is most important6. It has improved at following explicit formatting instructions8, so a good pattern is to ask for the summary first, then a second prompt that sorts the sections, puts the key decisions first and marks critical items. Because it takes huge inputs, you can also feed several related transcripts at once and ask for one combined summary.
A GPT-5.5 prompt: one clear structured ask
Summarize the meeting transcript below.
List two sections:
1) Decisions - each with who agreed to it
2) Action items - each with the owner and any deadline
Use bullet points under each heading.
Be concise but clear, and use only what the transcript says.A Gemini 3.1 Pro prompt: summarize then refine
First pass:
Summarize the meeting transcript below. List all decisions and all
action items. For each action item, give the owner and any deadline.
Refine pass:
Now organize it into two sections: Major decisions and Action items.
Put the most important decisions first, and mark critical ones with [!].
Use bold for the responsible person on each line.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 case that matches most of your meetings, then start with the model on that branch.
A starting point, not a rule. Test on your own transcripts before you commit.
If your meetings are long or you process many every day, start with Gemini 3.1 Pro. It reads a full transcript in one pass and lists $2 per million input tokens against $5, so the savings add up over a busy week4.
If your minutes go to clients or executives and need to read clean, start with GPT-5.5. It tends to write the more polished, publishable summary with less hand editing7.
If you only need the decisions and tasks captured, either model is safe, since both are accurate on that core job6. And for a high-volume workflow that still needs a finish, use Gemini for the bulk draft and pass the important meetings through GPT-5.5 for polish.
If we reduce it to one line: Gemini 3.1 Pro is the better choice for scale and cost, and GPT-5.5 is the better choice for polish and format.
That is a fair read of the current evidence. Both models are accurate at capturing decisions, owners and next steps. What differs is the finishing and the budget6. On general reasoning they score close to level5, so neither struggles with a complex meeting.
Two caveats matter. Benchmarks move fast and each vendor can claim a lead on a different test, so treat scores as directional. And the real answer is to test on your own transcripts, with a human spot-check on any minutes that carry legal or financial weight. A fair test needs the same setup for both models: the same transcript, 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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