diverse quality issues. And we actually want to explore this. And so, we actually have a leaderboard that you can go to on our website right now. It's called the LLM leaderboard. And what we do is we take all of the major new models that come out and we evaluate them. We give them, you know, 4,000 or so coding tasks and we evaluate them using all of the metrics that SonarCube uses to evaluate code, right? We look at their correctness, complexity, the way the rate at which they're solving the tasks we assign them, and then our classic things maintainability, reliability, and security. And we're able to kind of graph all of these models across these different axes and show you where models perform well and where they have room to improve. And what you're looking at on the screen right now is actually Claude Opus 4.6 and Claude Sonnet 4.6. If you're a Claude customer, you might be toggling between these two models to control your token burn rates. And you'll see that Claude Sonnet is actually quite good from a correctness standpoint, from from solving tasks, and from a standpoint. But if you're requiring higher levels of maintainability or higher levels of security, if you're trying to get a lower complexity out of your code, you might benefit from switching to Opus for tasks like that. And so we run these kinds of analyses across a lot of different models, and you're always able to go to our website to get the latest analyses that we run. I think we're actually doing the latest Claude and and OpenAI models pretty soon.