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.
OMG, it rolled out on my paid Google Workspace account already too! What a change from ~"wait 8 weeks or more from google.com rollout to <yourdomain> rollout"
I don't think it's good scheme to follow but google is just following the trend of anthropic and OpenAI set up. I don't think anyone can understand Anthropic's Model naming scale with Haiku, opus, fable and Mythos. Similar with the OpenAI tera and other models in their line up.
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??
Same impression here. If this is Google's strategy we'll have to migrate to Luna, because costs are already high for our workload on 3.8 Flash (which in itself was already a 40% increase compared to 3.7 flash due to being more verbose and thinking more) and this now doubles cost once again.
Depends on your needs. Gemini Flash has best bang for your buck on visual tasks and best multilingual support/intelligence for non-Western/non-East-Asian languages; better than paid or OSS alternatives.
Yes I gave for granted textual tasks but they're not the only ones. If I remember correctly, mistral large should be good as well on image tasks while being cheap(er). I never had to do anything with less common languages tho
Price per token is dropping (though anthropic are doing dastardly things such as using new tokenizers that use 30% more tokens), efficiency will likely increase.
If the subscriptions (of all of the big companies - unlikely?) give a poor amount of value in the future, will move to trusted/open router and use a harness that uses the latest cheap good models for most things, more expensive models as orchestrators.
Already there are non-SOTA models that should be sufficient for most non-coding tasks, and soon for coding, also.
The future is bright, in that respect, without subsidizing the ridiculous IPOs.
Why is this exchange reminding me of that You Can See Everything documentary?
The analogy made sense to me: Tokenmaxxing policies were the result of an irrational drive by execs who just craved an, ahem, explosion of metrics due to the excitement over new technology. Now that cost sensitivities are setting in, there’s a growing sense of unease that maybe they were in an altered state when they created the mess and now that the excitement has passed they have to clean it up.
I think he's just saying tokenmaxxing is a temporary hype lol, probably fueled by subsidized subscriptions. When that's over, these cheaper models might get more usage.
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?
My guess, by atomic weight. Hydrogen = "sonnet". Carbon = "opus". Argon = "Fable" only argon 4 just happens to be kinda shit? But also it's google so who the hell knows. They could be just going alphabetical argon, then carbon, then hydrogen as release order follows the alphabet. That'd be kinda dumb but so is their access/subscription strategy so it'd be on brand.
It will be the same as these smaller models from OAI and Ant. Strong in benchmarks but pretty bad in real world use.
I've read this same comment every time a new Luna came out, and the difference between free GPT (Luna) and paid (Sol and Astra) is still outrageous for general use.
All the labs are holding back releases now, not necessarily to play games, but to make sure alignment is right. These models are teetering on the edge of "One person can seriously damage digital infrastructure" and an event like "Teenage kid uses Claude to bring Wells Fargo to it's knees for 6 hours" would be absolutely catastrophic for the whole industry.
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.
I feel like people use medium models thinking they are saving money, but in the end, it ends up wasting more time and tokens theough the increased rate of issues/bugs.
If the highest tier model makes even one or two fewer mistakes that require human intervantion, that almost makes it a no-brainer choice for production.
I've been doing some research lately on my own (aside from the major benchmarking going on) and I've definitely found cases where medium models do much poorly and cost more than the higher models. But on the other hand, if I can give the medium models certain skills or other resources to complete a task then they perform better in cost than the higher model (which, given the same resources, sometimes gets more confused than the medium does).
At least for my use case of evaluations of a set of features.
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
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.
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.
Qwen 4 Pro preview has been beating fable-5.2 in my tasks. This model is available to try on the free tier. I can't even use argon or carbon on my $10 google sub. Why doesn't google show off their top models with a few free prompts a day to get us hooked? Let the dog see the rabbit.
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.
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.
Per Business Insider[0], Argon and Carbon were part of an alphabetical lineup:
> An internal document shows that Google has tested a series of Gemini 4 models, internally named Argon, Barium, and Carbon.
Then I suppose they tacked on Hydrogen since it's supposed to be a "light" model and hydrogen is the lightest element. I agree that it's super confusing though.
+1 to this the naming is totally opaque and ridiculous.
There is no hint of what each one does. I get that these were internal code names, fine, but to everyone outside Google it's not helpful to have these meaningless names.
ChatGPT and Claude aren't much better - there is still quite a lot of assumed knowledge there assuming you know what e.g. a sonnet is and what an opus is - but at least if you squint there is something there.
Google's names just seem to be randomly generated.
Maybe they were worried that the 3.1 Pro model being around for so lunch ruined the brand? But 3.8 Flash was pretty decent I thought so it's not like "flash" was burnt. I dunno.
The choice is really odd. Hydrogen and Carbon could have an organic chemistry theme going if they chose Oxygen, but then they go with Argon? Anthropic and Open AI have the names figured out in terms of obvious size: Luna->Terra->Sol->Astra and Haiku->Sonnet->Opus->Fable->Mythos makes intuitive sense. I have no idea how the Google model size class works.
I know some people are still a bit confused by astra, sol, terra, and luna, but at least you can explain the reasoning and it makes sense. Google's new naming scheme is just pure luna...cy.
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!
looking as the price, seems like google wants to share the AI bowl with other companies, so generous!
though I suspect they keep the price this high to increase the profit margin for the shortened release cycle (instead of 6 months I think they will release newer checkpoints in 3 months or less from now on).
still, my application now permanently use deepseek models instead. no point using gemini, neglectable real life improvements for extremely higher price.
Argon, Carbon, now Hydrogen. If Google keeps climbing the periodic table, the roadmap writes itself: Californium finds gold in your codebase (enterprise only)
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?
No one on HN seems to get that agentic workflows are not the only name in the game. Everyone doing agentic development is either on a plan or with a custom enterprise offering, either way using higher-performing models. These entry level models are (also) for building apps on top of while you are paying API prices.
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.
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?
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.
Another day, another nonsensically named google product.
I quit using Gemini models months ago because they are incomprehensible to understand in terms of when to use them and how to navigate the terrible GCP and Gemini UI. It’s a Kafkaesque nightmare.
OpenAI is at least arranged by size of celestial feature. Anthropic did arrange them by size of a written work. But I think Fable and Mythos made it a little weird. As for Google, no idea other than Hydrogen is small so at least that kind of tracks.
Having said this, I don't defend this trend at all and also think a model picker ought to be good enough with at most three tiers rather than five or so with sometimes like five effort levels each for 25 combos. I refuse to believe we need this granularity.
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)
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.
I asked about the time of market open for premarket trading for nasdaq trades. While it correctly determined when the pre markets would open, it then said it WAS open already by referencing the current time which was off by an hour. I told it where I was located and it then shifted by two hours. finally I told it to look it up and it got it correct.
"for all tiers of Google AI Studio, the Gemini API, Gemini for business, the Gemini app, Google Search, and paid Google AI Pro and Ultra subscribers in Google Antigravity."
So which one of these six options is the one I should use?
Same question as whether you should be using the latest Claude Opus model from Claude Chat, Claude App, Claude Code (via Claude Pro/Max/Ultra subscription), Claude Code (via API), Claude Platform, Claude on AWS Bedrock, Claude on Azure Foundry, Claude Managed Agents, Claude Cowork, etc?
When you call it through OpenRouter you're using the API.
You know how Claude Code is like a little program that your computer can run which lets you talk to Claude? Well Gemini is actually really similar! If that's more your speed, you should use Antigravity.
But if all of this is feeling really overwhelming and you just want something simple, I would recommend just Googling it! Hydrogen will be the one generating the response :)
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.
Thanks any Googlers who had a hand in this
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??
If the subscriptions (of all of the big companies - unlikely?) give a poor amount of value in the future, will move to trusted/open router and use a harness that uses the latest cheap good models for most things, more expensive models as orchestrators.
Already there are non-SOTA models that should be sufficient for most non-coding tasks, and soon for coding, also.
The future is bright, in that respect, without subsidizing the ridiculous IPOs.
Opus-tier on open models and local hardware.
But for enterprises, who have to pay API costs with no discounts, no way.
How is tokenmaxxing like ejaculating exactly? I don't follow the analogy.
The analogy made sense to me: Tokenmaxxing policies were the result of an irrational drive by execs who just craved an, ahem, explosion of metrics due to the excitement over new technology. Now that cost sensitivities are setting in, there’s a growing sense of unease that maybe they were in an altered state when they created the mess and now that the excitement has passed they have to clean it up.
Haiku, sonnet, opus.. is ok
terra, Luna, Sol... is also ok..
but this?
A = argon for initial release B was skipped because you can't name a product boron C = carbon D = ??????
They might be going for atomic weight, but the average person couldn't say whether argon or carbon was heavier.
I've read this same comment every time a new Luna came out, and the difference between free GPT (Luna) and paid (Sol and Astra) is still outrageous for general use.
The free default was 3.5 flash lite.
If the highest tier model makes even one or two fewer mistakes that require human intervantion, that almost makes it a no-brainer choice for production.
At least for my use case of evaluations of a set of features.
Feel like this pricing only makes sense for a product where you don't even select the model. Maybe Antigravity is like that.
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.
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.
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.
> An internal document shows that Google has tested a series of Gemini 4 models, internally named Argon, Barium, and Carbon.
Then I suppose they tacked on Hydrogen since it's supposed to be a "light" model and hydrogen is the lightest element. I agree that it's super confusing though.
[0] https://www.businessinsider.com/google-employees-test-new-ge...
Or maybe they just pick them out of a hat.
alphabetical skipping the weird names
next would be iron krypton and lithium
There is no hint of what each one does. I get that these were internal code names, fine, but to everyone outside Google it's not helpful to have these meaningless names.
ChatGPT and Claude aren't much better - there is still quite a lot of assumed knowledge there assuming you know what e.g. a sonnet is and what an opus is - but at least if you squint there is something there.
Google's names just seem to be randomly generated.
Maybe they were worried that the 3.1 Pro model being around for so lunch ruined the brand? But 3.8 Flash was pretty decent I thought so it's not like "flash" was burnt. I dunno.
At this point even using Starbucks sizes would be better.
Also totally agree. What a bad way to name models...
though I suspect they keep the price this high to increase the profit margin for the shortened release cycle (instead of 6 months I think they will release newer checkpoints in 3 months or less from now on).
still, my application now permanently use deepseek models instead. no point using gemini, neglectable real life improvements for extremely higher price.
The labs use a pretty clear progression: Luna -> Terra -> Sol -> Astra; Haiku -> Sonnet -> Opus -> Fable/Mythos. Google doesn't make it easy.
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
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?
Argon, Carbon, Hydrogen?
Luna, Terra, Sol, Astra, Betelgeuse?
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.
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 have found it a good subagent workhorse for code implementation, with some higher level model doing the spec and review.
I quit using Gemini models months ago because they are incomprehensible to understand in terms of when to use them and how to navigate the terrible GCP and Gemini UI. It’s a Kafkaesque nightmare.
The naming scheme alone has no rhyme or reason. Model names should not be a source of cognitive load.
Anthropic - Literary scale.
Google - random elements with no clear relation to model value/capacity.
Having said this, I don't defend this trend at all and also think a model picker ought to be good enough with at most three tiers rather than five or so with sometimes like five effort levels each for 25 combos. I refuse to believe we need this granularity.
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)
You could vibe code a better LLM with Fable than what they have delivered. It is embarassing.
What is your prompt?
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.
So which one of these six options is the one I should use?
You know how Claude Code is like a little program that your computer can run which lets you talk to Claude? Well Gemini is actually really similar! If that's more your speed, you should use Antigravity.
But if all of this is feeling really overwhelming and you just want something simple, I would recommend just Googling it! Hydrogen will be the one generating the response :)
AI studio if you want to just try it out for some code things (think of it as a web IDE)
API if you want it to use it as an API.
For business if you are an enterprise.
Antigravity
Or just you know - just go to Google.com and search for something. It's really not hard.
This instapost "wah I don't know how to use it wah" from anyone as soon as Google release a model is getting tiring - change the record please.