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Discussion (61 Comments)Read Original on HackerNews
Judging from my own frustrating attempts to use Gemini for vibe-coding, it seems like Google is badly over-sold (i.e., under-provisioned).
From all those promos giving away their pro-level subscription with phones; spinning up a mid-level subscription to undercut other providers and (probably most significantly) putting AI queries into ever search response because their flagship search product had become useless; they're promising a lot more processing to customers than they can reliably deliver.
The recent iterations seem to be intended not to push the capabilities forward, but to deliver capabilities at the current level while consuming less resources. That will allow them to maintain their trajectory until (I'm expecting) they get the huge infusion of extra compute resources from Space X later this year.
If I'm right, then I expect we should see Google start pushing forward again (rather than more of this lateral stuff) by the end of the year.
There's no way these are using a significant amount of compute. I'm not 100% convinced they're actually LLM-generated rather than an old-school Markov model. Both the relevance and accuracy numbers of the responses flirt with 0%. It's possible they just have a few million stashed responses and choose one at random, from what I can tell as a user.
https://blog.google/innovation-and-ai/models-and-research/ge...
What is interesting is how this is announced before any Gemini Pro progress. From the outside it seems as though Google cannot keep up with other frontier models.
Edit: Oh it's in the other link
https://blog.google/innovation-and-ai/models-and-research/ge...
They're very well equipped to be the volume discount store of inference.
Google's "AI Overview" search results, which started out awful, are now much improved - can be actually useful - as long as you are not asking specialist questions, and the Gemini chat and voice apps are also great for everyday use, although not sure if they are yet monetizing this (volume probably a lot less than search I'd guess).
The Apple Siri-Gemini deal is also a significant way for Google to not get sidelined.
Around here we'd come to that conclusion at least a couple of years ago, due to abusive SEO and so forth. And that understanding was becoming even more widespread.
I don't know if Google's got the later parts of the game figured out yet, but I have to think that they'd realized that Search was dying. While there's still some value there, may as well use it as a hook to pull people into what they expect to be the next era.
Aren’t Marketing/PR people at FAANG supposed to be among the best in the industry?
[1] https://www.lexiconbranding.com/case-studies/google-antigrav...
(Quoting myself from 2 days ago.)
so I think they're just focussed on releasing whatever helps their bottomline (improving search?). its not like anthropic and openai are making a lot of money being on the top of charts! this might just be my crazy pills talking though.
releasing a model worse than luna is pretty bad. Its clear that internally they did not decide coding was a thing until relatively recently.
Gemini 3.1 Pro just feels a generation behind, from when models would miss easy things and make bad assumptions. Its not actively detrimental in bad way but the opportunity cost vs using something like Opus to be productive is large.
Gemini 3.5 Flash is the most annoying model I have ever used. It loves to respond in ALL CAPS like “LOOK AT THAT” for no apparent reason. I realize it’s a flash model but I will give it a basic list of tasks and the output will simply vomit “Now I will X” “Now I will Y” “Now I will Z” over and over again filling my screen with garbage. It’s also no smarter than 3.1 Pro and consumes just as many tokens as 3.1 Pro, it’s really pointless without a newer Pro model in place.
3.1 Pro: while it's coding performance is mediocre, a lot of coding work requires minimum actual thinking. I use it often for light refactoring, boilerplate generation, testcase skeleton generation, code review, language questions ("is there a better way to write this code block?"). I don't have a corporation behind me so costs matter. Considering that I'm a Pro subscriber, it's quite cost effective.
3.5 Flash: excellent model for general (re)search. I use it for everyday tasks with Thinking instead of Flash-Lite. It's a much better version of Google Search for general queries like gaming tips, cooking, day-to-day first aid, tax and investment questions, etc.
Google is clearly aiming for cost-benefit here and considering that it gets bundled with YouTube Plus and Google One at $20/month, it's a killer deal IMHO.
PS: I don't work for Google and don't even like Google very much. But this is a good product.
Sonnet is roughly the same level as 3.1 pro for me.
rumor has it that pro is not so pro in internal evaluations and that there are internal stakeholders holding it up
Last time Google had a "everybody agrees" SOTA model was Gemini 3 in November 2025, it beat GPT-5.1, it really was better and held it for a month.
Currently Google's best model doesn't match open models, in any category (performance, price or speed), in fact the last 4 open weight champions all beat Google's best model.
Here's a plot of the evolution: https://www.reddit.com/r/LocalLLaMA/comments/1v20g29/kimik3_...
A couple of days ago I was comparing Claude vs Gemini asking both what the size of YACC generated parsing tables for ANSI C was. Claude was very gung-ho and rushed off to download a C grammar, install bison, and find out for itself (not really necessary), and then hit daily free usage limit half way through without even getting to an answer (despite this being the only thing I'd asked it that day).