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 ranking on the closest benchmark flips with the effort setting.
Aug 28, 2026 · 6 min read
We are not claiming one of these two is the better model. On the closest graded board the order changes with the effort setting, which is reason enough not to name a winner from a chart.
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 a vendor describing its own model rather than on a measurement, 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 a board 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 sending tool or a newsletter platform 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 for the email itself, because a length limit and a no-hype rule depend on the model reading them literally. Grok 4.5 is the cheaper way to generate the variants around it at $6 per million output against $10.
Learn moreClaude Sonnet 5 for combining many team updates into one calm company voice. Grok 4.5 is the better value on a routine update packet and worth testing when the wanted register is livelier - with firm boundaries in the prompt.
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 xAI documentation, both re-fetched at the source on 28 August 2026. The two vendors count tokens differently, so cross-model cost arithmetic is directional.
The general layer, underneath the jobs above. The first row is the reason this page refuses a headline verdict: the same benchmark ranks the pair differently depending on how each model was configured.
The index and benchmark figures were re-fetched on 28 August 2026. They moved since the cold-outreach page was written, which is corrected on that page in the same change, and they are not on a machine schedule, so they can move again.
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
The one graded board that covers this pair ranks it two different ways depending on configuration, and nothing public grades the question both task pages actually turn on.
Take the effort setting seriously before reading any chart about these two. Claude Sonnet 5 scores 1,383 at maximum effort and 1,193 at high on the same knowledge-work board, and Grok 4.5 sits at 1,313 between them, so a comparison that names one winner has chosen a setting on your behalf. The composite index separates them by a single point, which is a tie, and the Sonnet run spent about five times the output tokens getting there.
What nothing public covers is voice. Neither board grades whether a model holds a stated register - warm but not chatty, direct but not pushy - which is the whole question on an outreach email and an all-hands update alike. Both task pages say so and both end with a blind test on your own copy for exactly that reason.
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 Grok 4.5.
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