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Do you track new AI model releases and test them as they come out?

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We're taking part in the GPT-6 Astra Challenge with OpenAI right now, which got me thinking about this.

New models drop constantly. Some people test everything immediately, others stick with what works until there's a real reason to switch.

Do you test new models right away? Do you use different ones for different tasks?

And if AI is part of your product - how do you decide when it's actually worth migrating?

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For me, the model always depends on the task.

When Astra came out, we tested how personalized the coach’s final advice was after a session. The difference was huge.

Before, changing the model didn’t change the result much. We spent more time improving the prompt, but the results were usually similar.

With Astra, I read the final advice and thought: “This really feels like my personal action plan.” For e, this is when changing the model is worth it.

 Yeah, when it's part of your product, testing is probably obligatory. And things move so fast, you have to stay on your toes constantly :) Do you feel like it puts a lot of pressure on product/tech teams - having to work at that speed?

 I don't think so, because I think new models are inspiring because they open up new possibilities. And the faster you integrate them, the more time you have to learn and move ahead while others are still catching up.

Absolutely. I test new models almost every time they drop.

Main reason: in my experience, older models often get noticeably worse once a new one launches. So testing the newest model is usually the most practical approach.

Fable was incredible when it first came out, then the quality dropped hard. I’ve noticed a similar pattern with some OpenAI models too.

So I don’t get too attached to any model. I keep testing and use whatever works best right now.

 Thanks for sharing, makes sense! Any specific model that surprised you recently?

 Astra by OpenAI

I primarily use Gemini and for me it perfectly meets the needs related to both app development and AI content generation within the app. Regarding development, it makes sense to use different models for different tasks, expensive models for planning and less expensive models for execution.

 

For AI content generation within the app, I started with the Gemini 2.5 Flash APIs few months ago, then 3.1 and now 3.5, and I could see how the output improved with each version. The latest is almost perfect for the app I am developing, but I am still curious about what new model version will bring.

 Thanks for sharing, Adrian! What are you building? Are you planning to launch it soon too?

 Thanks for asking. I used Gemini for a while to figure out which of the foods I buy is healthy for my lifestyle and which not. But it was too tedious to type questions, so I created , with tailored built-in prompts and zero typing required. I think I will launch soon, maybe next week.

Sounds interesting! Good luck with your upcoming launch. We’d be glad to check it out 🙌

We don't test on release day: the swap is the cheap part. Every OpenAI call in our product goes through one service with a JSON schema attached, so moving to a new model is a constant change in one file.

The expensive part is knowing whether the new one is better. We run the same source text through a fixed set of graders instead of reading the output, because reading the output always makes the new model look better. One grader checks that no two cards in a generated deck share an answer. Another checks the difficulty level actually matches what was asked

 Thanks for sharing your process in such detail, Siarhei! Really interesting, and I think this approach might be useful for other product and tech teams following this thread.