What we tested these two models on, what those tests found, and the published rates and limits that hold whatever the job is. Two jobs have a full write-up, and the lead changes with how explicit the instruction is.
Aug 28, 2026 · 6 min read
We are not claiming one of these two is the better model. Which one leads depends on how explicit the instruction is and how long the queue behind it runs.
Two jobs have a full write-up behind them, and fourteen further articles put one of these two models against a different one. The cards below open both jobs, and the index further down lists the rest, so nothing here has to be taken on trust. Where a row rests on documented behaviour rather than a measured result, it says so plainly.
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 a measurement or a vendor's own description 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 document editor or a file upload 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. Two jobs on this pair have that test so far, and the index further down carries the rest of the library.
Claude Sonnet 5 first. A conservative edit depends on the model doing what was asked and no more, which is what Anthropic documents. GPT-5.6 Terra is the pick when the brief is loose or the queue is long.
Learn moreClaude Sonnet 5 for the sustained narrative that has to hold an approved structure to the last page. GPT-5.6 Terra for fast requirements analysis and an adversarial read of weak logic before the draft exists.
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 Anthropic and OpenAI documentation. Both price lists were re-fetched at the source on 28 August 2026, and the limits are carried from the two task pages. Sonnet 5 counts text differently from older Sonnet versions, so cross-model cost arithmetic is directional.
The general layer, underneath the jobs above. Two rows are measured and the rest rest on what each vendor documents about its own model, which is the honest state of the evidence for this pair rather than a gap in the research.
Only the last two rows rest on numbers. The behaviour rows come from each vendor's own guidance about its own model, which is a weaker kind of evidence than a graded board and is the reason both task pages end with a blind test on your own material.
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 Claude Sonnet 5
Where else we tested GPT-5.6 Terra
Almost every quality claim about this pair comes from a vendor describing its own model rather than from anything measured against the other one.
Nothing public grades these two versions on editing, on proposal writing or on prose quality. The literal-instruction and intent-inference rows are each vendor's own account of its own model, and both accounts are plausible and unverified. The one composite index that scores both separates them by two points at maximum effort, which is inside the range where configuration and prompt shape decide the outcome. The speed figure is a single endpoint measurement at maximum effort and will look different at lower settings.
What the page can settle is the money and the limits, and those are published on both sides and re-checked here. If cost across a long source pack is what decides your choice, this page answers it. If the answer turns on which model holds a voice or a structure better, it does not, and no page on the internet does either.
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 Claude Sonnet 5 against GPT-5.6 Terra.
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