GPT-6 Astra was recently released. I got access to it on Friday evening, European time, and have been using it extensively ever since. In this article, I'll share my first impressions of the model and the techniques that I utilize to get as much as possible out of the model to maximize its effectiveness. I'll also share some of the downsides I've experienced with the model and how I'm attempting to deal with them in the coming weeks, when I'll be using the model more and more, and we'll be trying to maximize how productive I can be with the model.

Why use GPT-6 Astra? Firstly, I always like to cover why you should care about the topic of an article. In this case, it's because GPT-6 is the latest release from Open AI, which is one of the frontier labs. This naturally makes it one of the LMs that you immediately want to try out to see how well it performs on my workflows. My workflows mostly consist of coding tasks, though I do have some other tasks such as using my computer, moving around in the browser, and doing research, so measuring how well it works for deep research and so on.

My first impressions of GPT-6 Astra: Immediately, once I started using the model, I started testing a few things: Running it on tasks that I've done before, running it on some new tasks (feature implementations and bug fixes), and starting to look for refactoring opportunities. My first impressions will thus be based on my experience when running these three tasks. I would like to note, however, that first impressions might not give the best picture of how good the coding model is. I remember when GPT 5.6 Sol was released, I got extremely good first impressions, but over time, I started to notice some quirks.

So when it comes to running tasks that I've done before and verified, I would say GPT-6 was, of course, able to do them all. But one thing I actually noticed in favor of GPT-6 is that it achieved it way faster, and I don't think this has anything to do with inference speed. In my experience, this just seems like the model is more effective at utilizing its tokens and is more able to complete tasks quickly. This impression was further verified once I started to run the model on new tasks as well. It just seems like the model was able to complete tasks super quickly while also doing it correctly, at least when comparing it to both the previous generation of Open AI models and when comparing it against Claude Fable 5.