Gemini-2.5-pro-preview-06-05

That being said, I'm starting to doubt the leaderboards as an accurate representation of model ability. While I do think Gemini is a good model, having used both Gemini and Claude Opus 4 extensively in the last couple of weeks I think Opus is in another league entirely. I've been dealing with a number of gnarly TypeScript issues, and after a bit Gemini would spin in circles or actually (I've never seen this before!) give up and say it can't do it. Opus solved the same problems with no sweat. I know that that's a fairly isolated anecdote and not necessarily fully indicative of overall performance, but my experience with Gemini is that it would really want to kludge on code in order to make things work, where I found Opus would tend to find cleaner approaches to the problem. Additionally, Opus just seemed to have a greater imagination? Or perhaps it has been tailored to work better in agentic scenarios? I saw it do things like dump the DOM and inspect it for issues after a particular interaction by writing a one-off playwright script, which I found particularly remarkable. My experience with Gemini is that it tries to solve bugs by reading the code really really hard, which is naturally more limited.

Again, I think Gemini is a great model, I'm very impressed with what Google has put out, and until 4.0 came out I would have said it was the best.

- "Something went wrong error" after too many prompts in a day. This was an undocumented rate limit because it never occurs earlier in the day and will immediately disappear if you subscribe for and use a new paid account, but it won't disappear if you make a new free account, and the error going away is strictly tied to how long you wait. Users complained about this for over a year. Of course they lied about the real reasons for this error, and it was never fixed until a few days ago when they rug pulled paying users by introducing actual documented tight rate limits.

- "You've been signed out" error if the model has exceeded its output token budget (or runtime duration) for a single inference, so you can't do things like what Anthropic recommends where you coax the model to think longer.

- I have less definitive evidence for this but I would not be surprised if they programmatically nerf the reasoning effort parameter for multiturn conversations. I have no other explanation for why the chain of thought fails to generate for small context multiturn chats but will consistently generate for ultra long context singleturn chats.

I'm thinking of cancelling my ChatGPT subscription because I keep hitting rate limits.

Meanwhile I have yet to hit any rate limit with Gemini/AI Studio.