YHK - yet another hacker news clone

With all the names for their AI offerings, I need to come up for Oxygen.

This is the most important class of models to keep watch. These are the ones that are going be to workhorses in the post-tokenmaxxing clarity

When are getting more powerful Gemini 4 Lead, and Gemini 4 Uranium models?

Their naming scheme seems a bit tough to follow. But it does seem like they finally learned their lesson and announced something at the same time it's actually available!

Maybe I'm not on the latest models (I do actually pay the min plan), but just this week I tried Gemini again. It still doesn't even get the current date/time of day correct. If it cant do that, I cant trust any output from it. I have no idea how Google fumbled so badly, but benchmark scores don't mean a thing if you cant even get your LLM to output the current date and time correctly.

You could vibe code a better LLM with Fable than what they have delivered. It is embarassing.

Aren't Google leaders embarrassed that the first thing I think of when I see their models drop is: I will see if I can call it from openrouter?

Why are there 7 different ways to call this model from Google? Which one should I use?

Is no one at the wheel calling the shots at Google and it's a PIP every man for himself? I want to try them out, don't know how.

Is this a replacement for Flash-Lite? Because it’s being used to serve answers for Search.

The new naming scheme seems shockingly poor though, “Pro” and “Flash” made intuitive sense to me, and Anthropic and OpenAI kept a sense of scale. Nobody is going to intuitively understand “Oh yes, Hydrogen is smaller than Carbon”, even though it is.

I think a new default free model for AI Mode being so close to the frontier will be a massive jump in perceived AI intelligence by the general public.

The naming of these Gemini 4 models is ridiculous.

In order of sophistication and cost, the models are Hydrogen -> Argon -> Carbon.

What kind of ordering is that?!?

With the introduction of Hydrogen, it's no longer ordered alphabetically. That would be Argon → Carbon → Hydrogen.

And it never was ordered by atomic mass, which is arguably the best ordering if you're going to name your model classes after chemical elements. That would be Hydrogen → Carbon → Argon.

It's also not ordered by prevalence in the universe. That ordering would also be Hydrogen → Carbon → Argon.

It's not ordered by electronegativity, boiling point, melting point, or any other remotely defensible chemical property I can think of.

It's not even ordered by letter count, Scrabble score, or NFPA 704 Flammability Rating, all of which would be Argon → Carbon → Hydrogen.

Google team: these names are an embarrassment

The naming scheme alone has no rhyme or reason. Model names should not be a source of cognitive load.

Honestly I cannot keep up with the naming conventions that keep changing every month.

Argon, Carbon, Hydrogen?

Luna, Terra, Sol, Astra, Betelgeuse?

This is a good blog post because of the graphs comparing directly to Terra and Sonnet for both wall-clock time and cost per task. This is the exact information I'm looking for.

My TL;DR is: seems like a better choice than Sonnet in areas outside of coding, and at high reasoning it's smarter than Terra but somewhat more expensive.

I really hope hydrogen keeps the excellent conversational ability of 3.8 flash. For brainstorming or even decoding the dense output of the powerhouse models, flash was a real delight to use. Its the first non-SOTA model that I would actually go to often over the larger models, it was fast, plenty smart enough, and generally wrote really well. Here's to hoping hydrogen keeps that up

Pretty big Gemini fan, but my initial results with Hydrogen and Carbon are disappointing.

They are trading improvements on pass@1 pointless benchmarks for worse disasters at p90/99 that you're going to hit every week in agentic workflows (if not daily):

overreach (doing more than asked), overwork (when it should have failed fast), harm (doing the opposite of what was asked), orthogonal work (doing something completely unrelated to what was asked), etc.

Google doesn't seem to be the only lab making this trade...

The set of benchmarks they released are disappointing. 10 mostly random benchmarks. Really?

So a pretty mediocre model at best on par with the open Chinese models. At least the Chinese models have a coherent naming

The orchestrator in Antigravity might be the most important part of this release. I'm excited that this is being built natively into harnesses.

Are we sure the guy that creates google naming schemes is not held hostage and those are his desperate attempts to communicate with the outside world in concealed way?

Looks like they are releasing these... periodically!

As this is a) being served in Search, b) supposed to be 2.5x faster than 3.8 Flash, c) the element with lowest atomic weight; I deduce that, despite being more capable than 3.8 Flash, now the “entry level” model set to replace 3.5 Flash Lite.

But it is priced the same as the non-discounted 3.8 Flash, considerably higher than what anyone is paying today for 3.8 Flash or 3.5 Flash Lite or the deprecated 3.0/2.x Gemini models. How does this work? Is Google abandoning the “fast and cheap” tier? That seems like a mistake since plenty of workflows are absolutely price inelastic, meaning when the 3.8 Flash discount ends at EOY, lots of current enterprise Gemini customers would have to take their business to another shop??

Are we going to talk about naming and confusing ways to access it, or are we going to discuss how capable the model is?

Every single time a Google model comes out, an army of Anthropic shills sidetrack the discussion.

Can we look at what the model does and discuss whether Google is still in the game or not? (naming models and whether you need to access it by clicking a drop-down menu or making a prayer ain't really relevant)

Argon, Carbon, now Hydrogen. If Google keeps climbing the periodic table, the roadmap writes itself: Californium finds gold in your codebase (enterprise only)

The pricing on these models make really little sense. This is $1.5. Argon is $2. Carbon is also $2 for now.

Feel like this pricing only makes sense for a product where you don't even select the model. Maybe Antigravity is like that.

Personally I found my usage of llms bimodal, I only have uses for the top frontier models (for research and development) or for the cheapest and fastest ones (to use in production for consolidated, simple flows). I am curious what people find the mid tier models useful for.

Their super smart recursively improving ASI models didn't tell them this naming is absolutely terrible?

Terrible naming convention on the low-end, because what do you do if you make something smaller than Hydrogen next? Gemini 5 Electron?

On the high-end, I feel like there is an implicit promise that if they keep releasing larger models and go past Oganesson (118), they are on the hook for AI-discovering a new stable element, so that is fair at least.

Good they are able to replace Flash. And cool this is available to everyone via search. But I still don’t understand Googles strategy with these new models.

For pro sub, Argon is still very limited and Carbon is only available on Ultra. This is not competitive at all. To say nothing of also being locked into Antigravity.

It's starting to feel like OpenAI is in 3rd place now. Really eager to see how / if they can claw their way back up.

The era of "ai le bad" jokes might just be over

Billions of people will now be exposed to a model that crushes sonnet 5.5 on benchmarks (in google ai search suggestions).

I kinda miss dumb ai in a way actually, was a certain charm to it

I'm really enjoying all these Gemini releases. I know a lot of people keep saying Google is too far behind in the race and that they dropped the ball to early but I don't feel that way at all. I had both Opus and Fable try to find a discreet bug in my codebase and both models assured me I had nothing to worry about and was probably overthinking. Argon was the only model that got to the bottom of the matter and developed the fix for me. I constantly find the Gemini models perform better in the real world as compared to the other frontier models and I also find they have less of the AI-isms that many of us have now come to associate with ChatGPT and Claude. Gemini feels like you're speaking to a comrade. Claude feels like you're speaking to a friend and it's an important distinction I feel.

Next will be gemini 4 cornholio if they keep following this naming

Can anyone understand the logic behind the names? Argon, hydrogen, carbon, what is the mental model one should use to understand where they fit, or what comes before/after?

Haiku, sonnet, opus.. is ok

terra, Luna, Sol... is also ok..

but this?

This was rolled out RIGHT away, as when I just went to use Gemini, seeing 3.8 Flash be replaced with "4 Hydrogen" was less hype and more a thought along the line of "What [on Earth] is Gemini Hydrogen???", then me going here to figure it out.

This model naming scheme works for separate products, but using elements, molecules, etc. for products you're supposed to compare with each other... Not very helpful.

If anything, my intuition would say that Hydrogen is more powerful than Argon... I don't know. Not liking this naming scheme trend.

The naming of this stuff is so bad. Hydrogen (1) is dumber than Carbon (6), sure. But Carbon (6) is smarter than Argon (18)!

The labs use a pretty clear progression: Luna -> Terra -> Sol -> Astra; Haiku -> Sonnet -> Opus -> Fable/Mythos. Google doesn't make it easy.