After comparing Gemini Pro and Claude Sonnet 3.7 coding answers side by side a few times, I decided to cancel my Anthropic subscription and just stick to Gemini.
Paying real API money for Claude to jump the gun on solutions invalidated the advantage of having a tool as nice as Claude Code, at least for me, I admit everyone's mileage will vary.
It put an expensive API call inside a useEffect hook. I wanted the call elsewhere and it fought me on it pretty aggressively. Instead of removing the call, it started changing comments and function names to say that the call was just loading already fetched data from a cache (which was not true). I could not find a way to tell it to remove that API call from the useEffect hook, It just wrote more and more motivated excuses in the surrounding comments. It would have been very funny if it weren't so expensive.
Models used to do this much much more than now, so what it did doesn't surprise us.
The nature of these tools is to copy what we have already written. It has seen many threads where developers argue and dig in, they try to train the AI not to do that but sometimes it still happens and then it just roleplays as the developer that refuses to listen to anything you say.
Sorry what do you mean by this?
google models on cli are great.
I added a very short chapter on setting this up (direct link to my book online): https://leanpub.com/ollama/read#using-the-open-codex-command...
This morning I tweaked my Open Codex config to also try gemma3:27b-it-qat - and Google’s olen source small is excellent: runs fast enough for a good dev experience, with very good functionality.
Any ide integration?
The LLM field is moving so fast that what is the leading frontier model today, may not be the same tomorrow.
Pricing is another important consideration. https://aider.chat/docs/leaderboards/
Perhaps someone can merge the best of Aider and codex/claude code now. Looking forward to it.
Only Claude (to my knowledge) has a desktop app which can directly, and usually quite intelligently, modify files and create repos on your desktop. It's the only "agentic" option among the major players.
"Claude, make me an app which will accept Stripe payments and sell an ebook about coding in Python; first create the app, then the ebook."
It would take a few passes but Claude could do this; obviously you can't do that with an API alone. That capability alone is worth $30/month in my opinion.
But there are third party options availabe that to the very same thing (e.g. https://aider.chat/ ) which allow you to plug in a model (or even a combination thereof e.g. deepseek as architect and claude as code writer) of your choice.
Therefore the advantage of the model provider providing such a thing doesn't matter, no?
there's also claude code proxy's to run it on local llm's
you can just do things
> It would take a few passes but Claude could do this;
I'm sorry but absolutely nothing I've seen from using Claude indicates that you could give it a vague prompt like that and have it actually produce anything worth reading.
Can it output a book's worth of bullshit with that prompt? Yes. But if you think "write a book about Python" is where we are in the state of the art in language models in terms of the prompt you need to get a coherent product, I want some of whatever you are smoking because that has got to be the good shit
Of course, this is only my experience and codex is still very young. I really hope it becomes as capable as Claude.
However, Sonnet 3.7 seemed like a very small increase, whereas 2.5 Pro seemed like quite a leap.
Now, IME, Google seems to be comfortably ahead.
2.5 Pro is a little slow, though.
I'm not sure which model Google uses for the AI answers on search, but I find myself using Search for a lot of things I might ask Gemini (via 2.5 Pro) if it was as fast as Search's AI answers.
Gemini doesn't seem to be as keen to agree with me so I find it makes small improvements where Claude and OpenAI will go along with initial suggestions until specifically asked to make improvements.
So I think patterns from the training data are still overriding some actual logic/intelligence in the model. Or the Google assistant fine-tuning is messing it up.
For example: I asked both to change a bunch of code into functions to pass into a `pipe` type function, and Gemini truly seemed to have no idea what it was supposed to do, and Claude just did it.
Maybe there was some user error or something, but after that I haven’t really used Gemini.
I’m curious if people are using Gemini and loving it are using it mostly for one-shotting, or if they’re working with it more closely like a pair programmer? I could buy that it could maybe be good at one but bad at the other?
It also seems to be better at incorporating knowledge from documentation and existing examples when provided.
Perhaps this is down to specific tools and their prompts? In my case, this was Cursor used in agent mode.
Or perhaps it's about the languages involved - my experiments were with TypeScript and C++.
I feel like you might be using it differently to me. I generally don't ask AI to find the cause of a bug, because it's quite bad at that. I use it to identify relevant parts of the code that could be involved in the bug, and then I come up with my own hypotheses for the cause. Then I use AI to help write tests to validate these hypotheses. I mostly use Rust.
And we aren't talking about trivial bugs here. For TypeScript, the most impressive bug it handled to date was an async race condition due to missing await causing a property to be overwritten with invalid value. For that one I actually had to do some manual debugging and tell it what I observed, but given that info, it was able to locate the problem in the code all by itself and fix it correctly and come up with a way to test it as well.
For C++, the codebase in question was gdb, the bug was a test issue, and it correctly found problematic code based solely on the test log (but I had to prod it a bit in the right direction for the fix).
I should note that this is Gemini Pro 2.5 specifically. When I tried Google's models previously (for all kinds of tasks), I was very unimpressed - it was noticeably worse than other SOTA models, so I was very skeptical going into this. Indeed, I started with Sonnet precisely because my past experience indicated that it was the best option, and I only tried Gemini after Sonnet fumbled.
It might be because most bugs that you would encounter in other languages don't occur in the first place in Rust because of the stronger type system. The race condition one you mentioned wouldn't be possible for example. If something like that would occur, it's a compiler error and the AI fixes it while still in the initial implementation stage by looking at the linter errors. I also put a lot of effort into trying to use coding patterns that do as much validation as possible within the type system. So in the end all that's left are the more difficult bugs where a human is needed to assist (for now at least, I'm confident that the models are only going to get better).
That said I do wonder if the problems you're seeing are simply because there isn't that much Rust in the training set for the models - because, well, there's relatively little of it overall when you compare it to something like C++ or JS.
I partly wonder if different peoples prompt styles will lead to better results with different models.
Manus.im also does code generation in a nice UI, but I’ll probably be using Gemini and Deepseek
No Moat strikes again
They pretty quick to let you use the latest models nowadays.
It's cool to have access to it, but please be careful not to mistake corporate loss leaders for authentic products.
To this day, the Google Home (or is it called Nest now?) speaker is the only physical product i've ever owned where it lost features over time. I used to be able to play the audio of a Youtube video (like a podcast) through it, but then Google decided that it was very very important that I only be able to play a Youtube video through a device with a screen, because it is imperative that I see a still image when I play a longform history podcast.
Obviously, this is a silly and highly specific example, but it is emblematic of how they neglect or enshittify massive swathes of their products as soon as the executive team loses interest and puts their A team on some shiny new object.
Also, Anthropic is also subsidizing queries, no? The new “5x” plan illustrative of this?
No doubt anthropic’s chat ux is the best right now, but it isn’t so far ahead on that or holding some UX moat that I can tell.
Googles previous approach (Pro models available only to Gemini Advanced subscribers, and Advanced trials can’t be stacked with Google One paid storage, or rather they convert the already paid storage portion to a paid, much shorter Advanced subscription!) was mind-bogglingly stupid.
Having a free tier on all models is the reasonable option here.
I agree that giving away access to expensive models long term is not a good idea on several fronts. Personally, I subscribe to Gemini Advanced and I pay for using the Gemini APIs.
EDIT: a very good deal, at $10/month is https://apps.abacus.ai/chatllm/ that gives you access to almost all commercial models as well as the best open weight models. I have never come close at all to using my monthly credits with them. If you like to experiment with many models the service is a lot of fun.
You’ll never know what that something is. For me, I can’t help but think that I’m getting an inferior service.
The also support an application development framework that looks interesting but I have never used it.
Look at Android.
HN behaviour is more like a kid who sees the candy, wants the candy and eats as much as it can without worrying about the damaging effect that sugar will have on their health. Then, the diabetes diagnosis arrives and they complain