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
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?
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.
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.
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.
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 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.
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!
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.
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.
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.
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?
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.
"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?
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??
In the Gemini app it's replaced Flash. Lite is still there.
But that's an old model, and if they replace it they have no smaller element to use for it's name. I guess it would have to be Hydrogen Light.
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.
Yeah, and they rug-pulled 3.7 very quickly, silently redirecting to 3.8 Flash. Raised our prices and broke some workflows.
Isn't something open weight cheaper (and probably better as well)?
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
The fact that there is still a flash lite dropdown in Gemini points to them keeping the flash lite model family.
So all models will be named after elements except Flash Light? Or maybe the next version will be named Hydrogen Light?
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 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
Disagree, Opus-tier models are the workhorses. You can get a ton done with them on a $100 subscription.
And for those on API billing? Opus is still quite pricy for what you're getting done with it.
If you're on API billing then presumably you're a business, and $1,000 a month is still reasonable for that quality of work
Why stay on API billing? A $200 monthly subscription has a crazy amount of usage and more than enough for a couple of concurrent projects.
Because you can't get subscription if your company has more than 150 users.
Enterprises with more than 150 seats have to pay API costs.
> And for those on API billing?
Opus-tier on open models and local hardware.
For users who pay subscription costs, 100% right.
But for enterprises, who have to pay API costs with no discounts, no way.
Assuming the price would converge to the subscription price, not the API price.
I like that you gave an argument and careful reasoning instead of just a vague claim based on common meme about ejaculation.
How is tokenmaxxing like ejaculating exactly? I don't follow the analogy.
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.
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.
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.
3.8 flash was easily the best general everyday person model yet though
3.8 flash was never served to free users on google though?
I think this is a completely different class from Luna and is much more akin to 6.1 Sol in terms of capabilities and general intelligence.
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.
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.
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.
Same, I either use xhigh on best model, or use cheapest and fastest models (mercury/jev/haiku/luna)
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.
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.
Isn't $2 the intro price for Argon and Carbon?
The orchestrator in Antigravity might be the most important part of this release. I'm excited that this is being built natively into harnesses.
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.
[0] https://www.businessinsider.com/google-employees-test-new-ge...
Density at standard temperature and pressure? Carbon admittedly has a number of forms at STP but they're all solid and thus heavier than argon gas.
Density of the most common (on Earth) allotropes at STP, perhaps?
Or maybe they just pick them out of a hat.
nodejs release names
alphabetical skipping the weird names
next would be iron krypton and lithium
+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.
We'll know they've achieved super intelligence when tech companies suddenly have sensible product names.
Yeah, they really fumbled the naming scheme for this batch of models.
At this point even using Starbucks sizes would be better.
I don't know why but this post reminded me of the show(books) Altered Carbon.
Also totally agree. What a bad way to name models...
I now believe that they're trolling.
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!
Next will be gemini 4 cornholio if they keep following this naming
When are getting more powerful Gemini 4 Lead, and Gemini 4 Uranium models?
Gemini 4 Plutonium will have nerfing half life?
Looks like they are releasing these... periodically!
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
So a pretty mediocre model at best on par with the open Chinese models. At least the Chinese models have a coherent naming
By what metric?
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.
Honestly I cannot keep up with the naming conventions that keep changing every month.
Argon, Carbon, Hydrogen?
Luna, Terra, Sol, Astra, Betelgeuse?
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.
It is no more confusing than the naming schemes used by OpenAI and Anthropic
OpenAI - Celestial body size.
Anthropic - Literary scale.
Google - random elements with no clear relation to model value/capacity.
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.
Indeed. That's why when I had to name my product I named it after a cat.
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 don't believe so. From my use I think it is a Flash model through and through.
I have found it a good subagent workhorse for code implementation, with some higher level model doing the spec and review.
No. It has the same price with 3.8 flash (with no introduction price it seems)
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?
Their super smart recursively improving ASI models didn't tell them this naming is absolutely terrible?
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)
With all the names for their AI offerings, I need to come up for Oxygen.
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.
What? I just asked it what time it was in my Gemini app and it told me.
What is your prompt?
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.
It's in the second paragraph of the article.
"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?
Whichever one suits your needs.
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.