What we tested these two models on, what each test found, and the published rates and limits that hold whatever the job is. Two jobs have a full write-up so far, and both of them split between the two models rather than going to one.
Aug 25, 2026 · 6 min read
We are not claiming one of these two is the better model. On both jobs we tested, the answer was to use each of them for a different part of the work.
Two jobs have a full write-up behind them, and nine further articles put one of these two models against a different one. The cards below open the two, and the index further down lists all nine, so nothing here has to be taken on trust. The two vendor knowledge-work figures come from different harnesses, and the page says so instead of ranking on them.
What we compared is set out underneath. First the published rates, input formats and limits both models bring to any job, then the model-level dimensions where the evidence separates them. 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 slide add-on or a document viewer 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 which part of it and why, and opens the full test. Two jobs on this pair have that test so far, and the index further down carries the rest of the library.
Terra for the argument and Gemini for the wording. Terra decides what the talk should argue and how the logic changes when the brief moves. Gemini is the faster and cheaper way to produce the slide copy once the structure is settled.
Learn moreTerra for exact policy meaning, Gemini for cost. Terra holds the more direct translation evidence and starts replying sooner. Gemini costs well under it and generates about twice as fast, once a bilingual check on your own replies clears it.
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 with speed measured independently. Terra's cached input rate is stated here in the fuller form two other pages use.
The general layer, underneath the jobs above. These are model-level dimensions, so they hold whatever the job is. The two knowledge-work figures come from different vendors at different settings, and that row says so rather than presenting them as a match.
OpenAI's presentation benchmark material concerns GPT-5.6 Sol rather than Terra and cannot be inherited across tiers, so it is not used here. Terra is also absent from the preliminary category leaderboards where Gemini ranks well, so those tables prove nothing about this pair.
Every article on this site that puts one of these two models under a graded test, grouped by model. The two on this exact pair are the cards higher up the page.
Where else we tested GPT-5.6 Terra
Neither model has been graded publicly on a specific document job on this pair, so every quality row above is a proxy.
The capability index is a composite rather than a grade on any real deliverable. The two knowledge-work figures are each vendor reporting on itself, at different top settings and through different harnesses, which is why the row calls itself directional. The long-context recall result is Google's own with no matching independent figure for Terra at the same length. And a benchmark lead in reasoning says nothing about whether the language it produces sounds like a person.
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-5.6 Terra against Gemini 3.6 Flash.
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