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"
If one of the dimensions it improves upon is inference cost per unit of work, then switching over to it would save money, and the sooner you do that, the more you save.
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.
This is so dorky, but I have a comparison with frisbees. In Disc golf each brand does its own naming scheme related to the speed / stability of discs. One company (Prodigy) produced a _roughly_ ordered approach and people very regularly commented on how confusing it was. By comparison, other brands (which got no critiques) have literally arbitrary names matching only a theme (e.g. electricity, animals, ...mental disorders/phenomena). It took me a very long time to come to grips with it. I ultimately think with branding... it only matters that people who use the system can identify things as distinct. I think the subset of minds that wants a strictly logical relationship to ordering is fleetingly small. To most people its additionally not even relevant.
My only real complaint with Google is they regularly change names for seemingly no reason. I'm still out here saying "Gemini" when I type "agy" and I don't bother saying "Anti gravity" because I assume that name won't exist in a year or two.
I think the others are mostly pretty clear. A haiku is a very short poem; a sonnet is a longer poem; an opus is an artistic masterwork ("Magnum opus"), so not necessarily longer but certainly grander. Mythos is slightly more tenuous but still goes in the direction of "cultural awe", something that the highest forms of writing aim for. Fable isn't a perfect name TBH, it sounds much less grand than "mythos" to my ears despite being roughly the same product AFAICT, and with a dangerous hint of "I might make things up".
I think OpenAI's names are clearest. In most people's understanding, moon < Earth < sun < stars.
More importantly, Anthropic has been consistent with their naming convention for years (two, yes, but plural years) now. OpenAI and Google’s habit of changing naming conventions semi-regularly makes their products harder to grok.
> but google is just following the trend of anthropic and OpenAI set up.
They botched it.
A haiku is a short poem. A sonnet is a longer work. An opus is a great work. Mythos is a connected body of works.
As with Open AI, Luna --> Terra --> Sol --> Astra from smallest to largest. Very straightforward.
With Google it's totally incoherent. Hydrogen --> Argon --> Carbon!?
Hydrogen is small, but it's the most abundant element in the universe by far. Argon is >3x larger than carbon by molecular mass, though carbon is more abundant. It's impossible to determine what's supposed to be smaller and what's supposed to be larger.
Yea, impossible to know whether it's supposed to go by atomic mass, frequency or some other metric. Plus most people off the top of their heads don't really have these stats memorized. With OpenAI and Anthropic it's way more straightforward. If you stop the average person on the street and told them to order the three by mass, most couldn't do it. But they could order Moon/Earth/Sun/Astra
Yeah. Atomic mass: Makes no sense, argon is larger than carbon. Frequency: Makes no sense, argon is least frequent of the three and hydrogen is everywhere, so carbon would be in-between rather than the flagship.
Letters of the alphabet: Also makes no sense. There was a theory that Argon = A, that there was an unreleased B model (Bismuth or Barium or something), Carbon = C, etc. (Similar to Intel's MMORPG-inspired Alchemist --> Battlemage --> Celestial...) Hydrogen broke this notion.
when you put it this way, it sounds like they are operating on an XY axis (at least) vs a continuum. Argon big, complicated, slow AND expensive; Carbon abundant, widely available, can do anything, costly as a result; Hydrogen, abundant, volatile, cheap. can't imagine how insane Nitrogen must be
I find theirs to be a lot more excusable, given it's obvious that the Sun is bigger than the Earth, which is bigger than the Moon. Anthropic less-so, but if you know the meaning of each, you can infer it somewhat well.
What about it makes no sense? I find it to make the most sense, given it's about text generation and their models are named from shortest text to longest.
Look at how many AI adjacent products Google talks about in this press release along with its model names. Anthropic and OpenAI are guilty of it as well, so many models and ways of working with them right now, letting the users figure out the nuances of each.
Earlier today I found myself asking ChatGPT to research and explain exactly what Anthropic Playground is for and how specifically it may differ for my tasks from Anthropic Workspace, Chat, Code, and API credits. I chose ChatGPT Sol 6.1 high for this task going on gut feel, hoping it was the best model to handle that job without wasting my precious Claude tokens.
I think there need to build these adjacent things to create lock in. I can switch between Claude, GPT and pretty much and model on OpenRouter in little time, especially for dev work. With all of these being weeks apart in progress on the models and open-weight models seemingly getting closer and closer there is almost no moat. Without a moat they cannot charge high prices - it's a commodity. Having lots of my data in their system and having other exclusinve features like Claude Design or Whiteboard and what not, they create something that I cannot keep if I switch to a different vendor for a cheaper model.
Yes the naming is nonsensical, because the intelligence ranking is this (with atomic numbers in parentheses for those that don't have the periodic table memorized):
Carbon (6) > Hydrogen (1) > Argon (18).
Double nonsense because it appears Hydrogen is in fact a smaller model than Argon, in which case this should obviously be released as "Gemini 4.1 Hydrogen" or something.
What they probably should've done if they really wanted to use elements is to do noble gases, and start these first three models in the middle (to leave room for bigger/smaller models in the future).
So you'd have:
Argon => Argon (18)
Hydrogen => Krypton (36)
Carbon => Xenon (54)
Not exactly because as noted it appears hydrogen is in fact the smaller model, but then this should be (again) 4.1 or something.
And they'd leave space for a truly massive model if needed (Radon) and two slots for much smaller models, with Helium (2) presumably being a model that could be run on basically any consumer device.
Agreed on the naming issue and while it was novel at first it will inevitably get worse as more models get release, until decide to abandon it all together. It’s like they are setting themselves up for failure on that front lol
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
I want to go all in the Gemini ecosystem but several things keep me going back to OpenAI:
* the Gemini app is glitchy on iPhone (my chats frequently fail or are lost if I background the app)
* the Work equivalent (I guess task?) is not as powerful. I often have ChatGPT Work to arbitrary computation and even install ML on the short-lived VM.
* there is no simple integration between antigravity and the Gemini app. I want a way to continue working away from my server while using the familiar app. No I don’t want VNC.
* ChatGPT sites are cool for putting up proof of concepts with zero effort
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.
It's definitely not the case that small models only do well in benchmarks. We're working on a battle system on top of OpenCode, so have data for real people doign real work.
Right now DeepSeek Flash 4.1 Flash has a 64% win rate against GPT 6.1 Sol. Small data still, but it has 33 wins vs. 18 losses, which is confidence interval of 52% - 78% win rate.
Maybe I’m spoiled but I found it repulsively dumb. Some of the highest rates of hallucinations since GPT-3. Incredibly lazy. Heavily biased to parametric knowledge and speed over accuracy. I would rather have no AI at all than use that.
People keep saying this, but inference itself is not what is being subsidized. The expense for electricity/hardware/datacenter operations/etc for an inference token is less than what they charge for it, so that cost doesn’t HAVE to go up to be profitable.
The thing that is being subsidized is the TRAINING costs, and that is because they aren’t selling enough tokens yet to pay for the training costs. This is just like most other growth businesses, though, and if enough people use enough inference, it can be self sustaining.
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?
It's not, Gemini 4 Carbon is better than Gemini 4 Argon.
Argon internally was called Barium (Argon was a different model) so the models were originally named in alphabetical order. Then they change their mind and decided to call the lightweight model following the atomic weight so Hydrogen after they have released (A)rgon and (C)arbon. I imagine that this lightweight model is the original Argon.
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.
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.
> Teenage kid uses Claude to bring Wells Fargo to it's knees for 6 hours" would be absolutely catastrophic for the whole industry.
It would (rightfully) be catastrophic for wells fargo. And great for the industry.
If your security model relies on children not having access to things state actors can leverage, your in for a rude awakening.
And hopefully this time for them they don't have to release a new rev the week after.
I wonder now that Terra-6 is out how it would compare to Sol-6. (Which was replaced a week latter by sol-6.1 after chat got dropped opus 5.5 to incredible fanfare then sonnet 5.5 that radically upped the medium model ability).
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.
Asking people to guess beforehand how much intelligence they require for a task will hopefully be a relic soon. We’re lacking useful routing techniques which accurately pick the right model for the job. Or a high intelligence model which completes simple tasks fast and cheap. Or something else.
I have some tasks that deserve a bit more reasoning but for a long time most of my needs have been met by mid-range models, rather than the big ones. There's barely any motivation for me these days to use something more advanced than Terra on low/medium for everyday work. For a personal agent doing occasional coding, I'm still on Mimo flash.
I think many people still expect their tasks to need something more fancy, but could do with a reset of expectations and try to go much lower.
Production pipelines for non-coding usage. Jagged edge is very real. Just one example, loads of tasks where Gemma 4 26ba4b wipes the floor with Deepseek Flash v4.1. On Artificial Analysis-like benchmarks, the latter is many times more "intelligent". Not everything in the world is coding and math, but those are easy to "measure". Things that are hard to measure aren't part of their benchmarks.
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.
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
In the Gemini app of over the API? In the app Ive found it to write poorly, probably a system prompt thing. I've done some stuff with Argon over the API and it wrote well.
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)
Claude is the same really. Fable is a short story, so makes no sense in this hierarchy, and Opus have to think it as derived from "Magnum Opus" to make sense, else Opus by itself simply is a piece of music by a composer and would've been more appropriate for low-reasoning model to be used by sub-agents.
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.
>For everyone: Start using it in the Gemini app model drop down
and then it says for free users there is only "automatic" and you will get Gemini 4 Hydrogen "when it's the appropriate model for the prompt". Yeah sure Google, whatever, I don't care about Gemini models anymore. Too hard to access, constant rugpulls, intransparent pricing, many, MANY outages and in general very unreliable. At this point I just want Google to fail.
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?
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 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)
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.
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
My only real complaint with Google is they regularly change names for seemingly no reason. I'm still out here saying "Gemini" when I type "agy" and I don't bother saying "Anti gravity" because I assume that name won't exist in a year or two.
I think OpenAI's names are clearest. In most people's understanding, moon < Earth < sun < stars.
They botched it.
A haiku is a short poem. A sonnet is a longer work. An opus is a great work. Mythos is a connected body of works.
As with Open AI, Luna --> Terra --> Sol --> Astra from smallest to largest. Very straightforward.
With Google it's totally incoherent. Hydrogen --> Argon --> Carbon!?
Hydrogen is small, but it's the most abundant element in the universe by far. Argon is >3x larger than carbon by molecular mass, though carbon is more abundant. It's impossible to determine what's supposed to be smaller and what's supposed to be larger.
Also, the biggest being Astra is a bit confusing. Sol is also a star.
Furthermore, unless you know they’re supposed to be ordered by size you can’t know which is best.
Letters of the alphabet: Also makes no sense. There was a theory that Argon = A, that there was an unreleased B model (Bismuth or Barium or something), Carbon = C, etc. (Similar to Intel's MMORPG-inspired Alchemist --> Battlemage --> Celestial...) Hydrogen broke this notion.
It's just completely incoherent.
Google's is just random.
Earlier today I found myself asking ChatGPT to research and explain exactly what Anthropic Playground is for and how specifically it may differ for my tasks from Anthropic Workspace, Chat, Code, and API credits. I chose ChatGPT Sol 6.1 high for this task going on gut feel, hoping it was the best model to handle that job without wasting my precious Claude tokens.
Carbon (6) > Hydrogen (1) > Argon (18).
Double nonsense because it appears Hydrogen is in fact a smaller model than Argon, in which case this should obviously be released as "Gemini 4.1 Hydrogen" or something.
What they probably should've done if they really wanted to use elements is to do noble gases, and start these first three models in the middle (to leave room for bigger/smaller models in the future).
So you'd have:
Argon => Argon (18)
Hydrogen => Krypton (36)
Carbon => Xenon (54)
Not exactly because as noted it appears hydrogen is in fact the smaller model, but then this should be (again) 4.1 or something.
And they'd leave space for a truly massive model if needed (Radon) and two slots for much smaller models, with Helium (2) presumably being a model that could be run on basically any consumer device.
It doesn't do anything! It's inert!
"Claude and Codex have cool names, they are more successful than us, so therefore if we give gemini cool names we'll be successful too"
-marketing drone, probably
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??
It's cheaper than the last Pro models.
I want to go all in the Gemini ecosystem but several things keep me going back to OpenAI:
* the Gemini app is glitchy on iPhone (my chats frequently fail or are lost if I background the app)
* the Work equivalent (I guess task?) is not as powerful. I often have ChatGPT Work to arbitrary computation and even install ML on the short-lived VM.
* there is no simple integration between antigravity and the Gemini app. I want a way to continue working away from my server while using the familiar app. No I don’t want VNC.
* ChatGPT sites are cool for putting up proof of concepts with zero effort
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.
Right now DeepSeek Flash 4.1 Flash has a 64% win rate against GPT 6.1 Sol. Small data still, but it has 33 wins vs. 18 losses, which is confidence interval of 52% - 78% win rate.
The free default was 3.5 flash lite.
The thing that is being subsidized is the TRAINING costs, and that is because they aren’t selling enough tokens yet to pay for the training costs. This is just like most other growth businesses, though, and if enough people use enough inference, it can be self sustaining.
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.
1) There was a false narrative called tokenmaxxing whereby one is more productive by spending more tokens
2) This narrative was fueled by discount prices on tokens (which still happens to this day on subscriptions)
3) There is a point where we collectively realize that tokenmaxxing was a state of mind that is not rational
4) We will then pay API prices because LLMs are obviously amazing but no one will offer subscriptions
5) API costs of Opus 5.5 and Fable tier become uneconomical
6) That leaves us in Sonnet/Haiku tier
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.
On the AA benchmark it kinda makes Sonnet and Terra look weak. Hopefully that translates to real world usage. Can’t wait to try it out.
Haiku, sonnet, opus.. is ok
terra, Luna, Sol... is also ok..
but this?
Argon internally was called Barium (Argon was a different model) so the models were originally named in alphabetical order. Then they change their mind and decided to call the lightweight model following the atomic weight so Hydrogen after they have released (A)rgon and (C)arbon. I imagine that this lightweight model is the original Argon.
And "Hydrogen surpasses Argon on the Artificial Analysis Intelligence Index at its default high thinking level"
Given it's twice the price of 3.8 Flash I hope they would find room for another cheaper tier, but the Hydrogen name suggests not.
I'm glad Google are coming out with some decent models but it's a slightly confusing line up.
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.
It would (rightfully) be catastrophic for wells fargo. And great for the industry. If your security model relies on children not having access to things state actors can leverage, your in for a rude awakening.
I wonder now that Terra-6 is out how it would compare to Sol-6. (Which was replaced a week latter by sol-6.1 after chat got dropped opus 5.5 to incredible fanfare then sonnet 5.5 that radically upped the medium model ability).
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.
I think many people still expect their tasks to need something more fancy, but could do with a reset of expectations and try to go much lower.
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.
Where is Gemma5?
Where is Gemma5?
Where is Gemma5?
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...
Gemini: "I am operating normally."
Smooth.
The labs use a pretty clear progression: Luna -> Terra -> Sol -> Astra; Haiku -> Sonnet -> Opus -> Fable/Mythos. Google doesn't make it easy.
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.
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
and then it says for free users there is only "automatic" and you will get Gemini 4 Hydrogen "when it's the appropriate model for the prompt". Yeah sure Google, whatever, I don't care about Gemini models anymore. Too hard to access, constant rugpulls, intransparent pricing, many, MANY outages and in general very unreliable. At this point I just want Google to fail.
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?
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 naming scheme alone has no rhyme or reason. Model names should not be a source of cognitive load.
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.
Anthropic - Literary scale.
Google - random elements with no clear relation to model value/capacity.
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.
Argon, Carbon, Hydrogen?
Luna, Terra, Sol, Astra, Betelgeuse?
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)
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.
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.