Gemini 2.5 Pro's superiority is IMO largely driven by their long context support and training methodology. Compare Gemini as a beta reader for a 100k token book with GPT4.1 or Claude 4, and it becomes quite clear how much more effectively it can reason across its context than other comparable models. This also makes it much better for architecting new features into a system, since you can load a lot of the current system into the context and it'll conform to existing styles and architecture patterns more closely.

Agreed, 2.5 flash too. I analyze a large json document of metrics for pricing decisions. Typically around 200k, occtionallly up to 1M, Gemini 2.5 significantly outperforms for my task. It isn't 100%, but role playing gets close. I suppose that's a form of inference time compute.

For a 100k token context window; all those models are comparable though

gemini 2.5 pro shines for 200k+ tokens

I can confirm from first hand experience that even at 100k they are most definitely not comparable for the task of beta reading.

splitting hairs much?

> I personally think that Gemini 2.5 Pro's superiority comes from having hundreds or thousands RL tasks (without any proof whatsoever, so rather a feeling).

Given that GDM pioneered RL, that's a reasonable assumption

Assuming with GDM, you mean Google-Deep Mind. They pioneered RL with deep nets as policy function estimator. The deep nets being a result of CNNs and massive improvements in hardware parallelization at the time.

RL was established, at the latest, with Q-learning in 1989: https://en.wikipedia.org/wiki/Q-learning

i didn't say they invented everything; in science you always stand on the shoulders of giants

i still think my original statement is fair

"gdm pioneered rl" is definitely not actually right, but it's correct to assert that they were huge players.

people who knew from context that your statement was broadly not actually right would know what you mean and agree on vibes. people who didn't could reasonably be misled, i think.

We definitely plan to maintain the project for as long as there is interest in it. If you have ideas for new tasks, we'd always welcome contributions!

Thanks for the answer! As a toy project I implemented wikiracing with trl. I'll probably try to PR that to your gym. (can't say that I managed to improve score with it though)