Comment by Someone1234
15 hours ago
I'm very confused about their model market position/naming schema.
First we had:
> Luna (Small), Terra (Medium), Sol (Big).
Which was fairly smooth after you got used to their planets.
But then they added Astra as the "Bigger," which was mostly still self-coherent:
> Luna (Small), Terra (Medium), Sol (Big), Astra (Bigger)
But then things go confusing, they released Sol-6.1 which sometimes is superior to Astra, and discontinued Terra which made it look like this:
> Luna (Small), Sol-6.1 == Astra
But with Sol-6.1 costing the old Terra-6.0 price, while, as I said performing as good as the old "Bigger" model in many non-3D/UI situations.
Now they're re-introduced Terra, as a 6.1 release, making this even more confusing.
Did Microsoft's chief branding officer move over to OpenAI?
I work at OpenAI and I can try to explain our logic.
In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes. Despite that complexity, much of the variation in performance is explained by just a single variable: model size. In general, models that are bigger will (a) be more performant / reliable, (b) cost more per token, and (c) have better recall of esoteric facts. As you note, our models are ordered:
Luna < Terra < Sol < Astra
Even today, I'd recommend this as a general starting point for picking models. If you care about quality only, pick Astra. If you're a bit price sensitive, pick Sol. If you're very price sensitive, pick Terra or Luna.
Of course, model size doesn't capture all variation. Models that are trained later might have new data or new techniques or new architectures that earlier models lack. You can see this with models from all providers - Sonnet 5.5 outperforms Fable 5.1 in ways, and Gemini 3.8 Flash outperforms 3.1 Pro in ways.
In the case of GPT-6.1 Terra, I'd say Terra is probably better than Astra and Sol at technical communication, similar on vision and computer use (but much faster!), and a bit worse on most other things. For API developers, it's a nice midrange option if you want something cheaper than Sol but a lot smarter than Luna (especially for vision / computer use / communication). For subscribers, it's not a bombshell release - if you're hitting usage limits, maybe it helps you go a bit further, and if you want clearer communication or faster computer use, it can be a nice option to select.
If you have suggestions on how to name our products better, I'm all ears. Our intention is to make things clear and simple, even if we miss the mark sometimes.
Unfortunately, a lot of the complexity is somewhat irreducible and due to to the actual complexity of the products. Scaling model size is different from scaling reasoning effort which is different from scaling multi-agent count. So if we just swept all of those under a single name of "more IQ", it would make the naming simpler but the experience much worse, as important details would be obfuscated from end users.
Do you then recommend Sol for technical documentation?
What's confusing to me in addition to the naming is that there are no published guidelines I can use, as far as I can see. Put yourself in the user's shoes. On what basis should one pick a model for a given task?
I can understand smaller models being dumber and faster. That aligns well with scenarios where you are willing to compromise; e.g. you're classifying content or asking a model to format text into a table.
But when you want "maximum intelligence, cost and time be damned", there's the implication that bigger models are smarter on a linear scale, and every provider's model release naming is currently conflicting with that expectation.
This isn't just a naming problem, but a communication problem. If I knew that Astra was the best at coding but a bit bad at writing, and Sol was almost as smart but better at technical writing, then that's the kind of thing I expect to be documented.
One possible way: A simple matrix with horizontal columns like "writing", "coding", "math", rows are the models, and each box is a number of stars from 1-5.
Instead, this sort of thing becomes lore, shared by word of mouth.
> Do you then recommend Sol for technical documentation?
I don't know, to be honest. Technical documentation is such a multidimensional skill, and I also don't know your budget. I'd imagine Terra might have the most readable prose, but Astra might be more likely to catch bugs or mistakes to highlight. If the documentation is about a more esoteric language or situation, Astra might have a greater edge than if it's a common language or situation. If you have a long list of documentation guidelines, that might also give more of an edge to Astra. I wouldn't want to make a recommendation without doing a lot of testing, and I haven't done a lot of testing.
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> On what basis should one pick a model for a given task?
It's high-level (and doesn't break out coding/writing etc), but https://developers.openai.com/api/docs/models says:
> If you're not sure where to start, use GPT-6 Astra, our flagship model for complex reasoning and coding. Choose GPT-6.1 Sol to balance intelligence and cost, or GPT-6 Luna for cost-sensitive, high-volume workloads.
That page also seems to be updated after each new release.
> Put yourself in the user's shoes. On what basis should one pick a model for a given task?
Create a realistic eval suite for your use case using your actual data that you manually label, with easier, more complex, rarer samples, and run each model you are considering against it and decide based on the usual confusion matrix, cost, latency, or whatever metrics matter to you. There's really no other way to know – and you might be surprised that cheaper models can often perform your task(s) relatively well at a fraction of the cost.
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> Do you then recommend Sol for technical documentation?
Every model I've tried is just abysmal at this, for what it's worth.
If any do it well please let me know and I'll give it a spin.
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You can start with Artificial Analysis benchmarks but, then you need to test in the real tasks anyway.
I assume having a person write words for other people to read is not feasible?
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> I'm all ears. Our intention to make things clear and simple, even if we miss the mark sometimes.
Thanks. I think the very response you wrote here is a better form of marketing for the software engineers than what openAI puts out there. When everything is groundbreaking and everything tops charts, nothing is.
I suspect a lot of target demographic (developers) prefer simpler and honest communication about not just the models high points but low points too. With the linked Terra post, if I saw 1 chart that clearly highlighted the usecases for which Terra is _worse_ than higher tier models, I would be more convinced overall.
Everything trying to occupy the top spot in my brain's limited "top model" representation space is not helpful.
While I generally agree that it'd be nice to just have "here is the Pareto optimal model for your use case. Here is the specifics we optimized each model for" so users don't have to do benchmarking and research as much, they're getting much better at explaining. It's basically what the commenter above said.
The website linked starts out with:
> We’re introducing GPT-6.1 Terra, a fast, efficient model that combines detailed visual reasoning and precise computer use alongside clearer, more natural writing. A significant step up from GPT‑5.6 Terra, it excels at the communication and computer-based tasks that fill the workday, from drafting proposals and interpreting technical drawings to filling out forms and editing videos.
If you go father down, it has graphs and details with specifics of this, including:
> We measured GPT-6.1 Terra’s computer-use capabilities... At medium reasoning effort, GPT-6.1 Terra scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost. It even performs on par with GPT-6 Astra at only one-tenth its price per token.
and
> On MMMU-Pro... It even nearly matches the scores of our most capable frontier model, GPT-6 Astra, and GPT-6.1 Sol at much lower cost.
The 4-tier naming is honestly as clear as you can get. I have no idea what people are so confused about.
Terra and Luna are dirt cheap and good enough for 50% (or more) of the typical junior dev's workload, and great for routine work. Sol and Astra have so much written about them at this point that it should be obvious what those are good at. The large/gigantic models for things that need full intelligence and slightly more branching thinking.
The obnoxious/confusing thing is that models are released individually rather than as a family.
Luna < Terra < Sol < Astra, fine. But does Terra 6.1 beat Sol 6? Does Fable 5.1 beat Opus 5.5? If I build something around this Terra 6.1 release, then when Luna 6.2 is released in a month, should I swap?
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> The 4-tier naming is honestly as clear as you can get. I have no idea what people are so confused about.
I wouldn't necessarily say that. Atmospheric mass may be obvious to us, but I suspect it's not nearly as universal knowledge as one may assume.
And I actually assumed at first that they were based on distance from us (terra < luna < sol), so I kept confusing terra and luna.
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I think the main problem is surfacing two topics:
- Understanding how effort and class interact (Is a high effort terra above a low effort Sol? Are larger models mostly always better regardless of effort?)
- understanding when (if ever) a new model tends to be better than an older but higher class one. Is an old astra better than a new terra?
If increasing effort and increasing model class produce improved results in a different way, I miss making it explicit in which way. In other words: What kind of issue warrants going up in effort? what kind of issue warrants going up in model class? What kind of issue is unlikely to be solved by either?
OpenAI itself cannot answer these questions. A prospective price-sensitive (or speed-sensitive) user has to experiment to find the cheapest (or fastest) model that is sufficient for each task.
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>effort / performance curves by model
Oh, easy:
https://deepswe.datacurve.ai/
Artificial Analysis has their own curves which are pretty similar.
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> Is a high effort terra above a low effort Sol
You can literally see this in the charts from the article
Any news on when Terra gets into the hands of Free and Go users, maybe replacing 5.6 Terra?
Also, I think the naming is great. Like you said, Anthropic faces the same issues you do. Opus is much better at Fable in a lot of stuff without being quite as huge or as smart.
Congrats on the launch.
Since internally you probably have a fairly good intuition for what these models do well, what about assembling a benchmark suite (with multiple dimensions) that produces results that show that intuition, but in a 'somewhat objective' way?
That could solve the 'more IQ' problem, because you'd actually have different dimensions (e.g. for 'raw intelligence', technical writing, emotional understanding, etc) that you could use to communicate the differences in models better.
(Of course people would instantly start complaining that the benchmarks numbers are probably fake etc, but it is what it is.)
I think different domain benchmarks already exist and are widely used.
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> If you have suggestions on how to name our products better, I'm all ears. Our intention is to make things clear and simple, even if we miss the mark sometimes.
I'm not the one you were talking with but catching you is godsend since I just ran into a lot of confusion on the naming myself. I think some of it has to do with how they are labeled in the app.
I think simple numbers in naming is great, even 6.1 is fine bc I understand that if I want the latest model, I just need to look for the biggest number. Where I got confused is in the "work" tab, I saw 'Astra' is set as the default. I see the label for it is:
"Frontier intelligence for your most demanding tasks."
To me that reads "this is the 'best' model." Then I see Sol-6.1 which is labeled:
"For coding and every day work."
Then Terra-6.1 is:
"Terra 6.1 Codex Model" (tbh this one might be the most confusing, I'm aware of codex the product but Sol is the one labeled good at coding)
From my POV as a basic plan-based user wanting to do something like...
> design a robust event-driven system, here's the details [...]
I honestly have no idea which one I should use.
Would Astra-6 produce a better design than Sol-6.1? Is the '.1' a big step up from Sol-6? (I'm guessing no but I truly have no idea). I would assume the answer is Astra-6 would produce the best one, even though the labels don't imply it is necessarily geared towards coding?
But tbh the "default" seems to always work for me... and after reading your comment, typing this one out, being forced to think about it... it doesn't seem too bad given your challenges in communicating the purposes of each model and wide audience of consumers. I'm also sure anyone could find a million problems with decisions I've made in software I've built so I try to not be too harsh :)
Try not using putting two variables in the product name. Stagger the release and make sure you don’t release a more up to date version in the middle so that it looks like a “medium” model is more advanced than a “big” model. People see bigger numbers they think better.
Medium v10 vs Big v2 makes it look weird and confusing. Especially when there are a sea of influencers and twitters with vibe assessments who will tell you new models best olds models.
Can’t you just start at the top or bottom.
Release new big for paying customers -> release new medium for paying customers -> release new small for everyone
Rinse and repeat. We’d all be much happier
It's not gonna happen but:
> Despite that complexity, much of the variation in performance is explained by just a single variable: model size.
please release model size :p
Out of interest, are there things where Luna is noticeably better than it would otherwise be expected to be?
Internal evals are still running but GPT-6.1 Terra is an AWESOME release for anyone using the API in agentic production applications where cost matters.
Personally, I pretty much exclusively use claude for coding work (sorry!). Our application is running almost 100% on OpenAI API (except for some fallback models). It is the best API on the market. The models are excellent particularly for any application that is cost sensitive.
Please can I ask about one parameter you didn't mention here? All of your models have both a reasoning effort setting and also a non-pro vs pro setting. I have some intuitive feel (from experimenting) for the difference a larger vs smaller model makes, and why that's different from adjusting reasoning levels, but I have no real feel for how pro vs non-pro is different in behaviour from adjusting effort.
I think you can think of Pro approximately as running multiple instances of a non-pro model, than giving all their outputs to a final judge model which combines them into a better one.
I use OpenAI models all the time (via Codex) and I've never seen this pro toggle. Where are you seeing that?
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> In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes.
Maybe stop using version numbers then and just name models like you'd name children.
This was lovely. Can we have more of this style of communication?
Instead of benchkmarks it'd be useful to get a sort of Radar chart across measured capabilities like computer use, communication, coding, etc.
We're probably headed to further specialized agents where a single ordering or even model size won't mean a lot.
It would be interesting to know which model is internally viewed as being best at jokes, I have a personal humor benchmark and so far models will either steal jokes (from comedians like Maria Bamford), or write something very unfunny.
When I was first learning to have agents interact I had a little script to orchestrate a model telling a joke, a critic collaborator helping develop material and an audience emoji rank them.
A favorite…
Prompt: “Tell a joke about mother in laws”
Gemma2 response: “What’s the best kind of mother in law? A dead one.” 0_o
Thank You for OP asking the question and thank you parents for some insight.
>If you have suggestions on how to name our products better......
Too late for that. I hope you guys can at least stick to these naming scheme for at least another 2-3 years going forward. Not everyone follows AI / LLM intensely, and it seems there is a new naming changes every year or so.
At least conceptually OpenAI's naming is easier to understand than Claude.
It terms of capability, it would be great to make it clear somewhere that this is 6.1 Model "+" technical communication, vision and computer use improvements. So that at least we know what we should try with these new release and choose a better model for certain workload.
Right now, other than pricing, it is confusing as hell. And in hindsight I wonder if there are fatigue in choosing one people just go with Open Model and stick with it if it is good enough.
>> If you have suggestions on how to name our products better, I'm all ears
<version>-<size>
Where <version>: 6.0, 6.1, etc., and <size>: xs | sm | md | lg
E.g. 6.1 Terra = 6.1-sm
If you eventually make specializations like "math" and "jokes" then I would call it something like 6.1-jokes-sm.
This is absolutely the most practical piece of information I learned today
I actually do have a recommendation, which is to do something like a new mode (i know i know): Auto xlow Auto low Auto med Auto high Auto xhigh Auto max
With an optional flash mode.
Then as you release models, you pick the model and thinking level that you believe is pareto optimal for cost/performance.
So this auto updates each release, like Terra was probably off this list entirely, but maybe this would appear now in Auto medium or something like that.
I know from looking at charts that each model seems to have sweet spots (like I always use this model in this thinking level and if i want to go cheaper i go here). If you picked those for us, it would be interesting. I do think you should keep the model families for those wanting more consistent experiences, but for many devs they may want to just pick auto something and have it update over time. I think nice if it actually just said in parenthesis what it was, something like: Auto Medium (Terra 6.1 High)
I am not sure if everyone would agree here though. I'm obivously advocating for more naming schemes, and i understand the irony.
Just to be clear, I think the current mapping for OpenAI is something like:
xlow = Luna Low
low = Luna xhigh
medium = Terra medium
high = Sol medium
xhigh = Sol xhigh
Max = Astra something?
Appreciate that these things have orthogonal measurements; with that in mind, given that the name signifies size, what does version number signify?
I.e. are all version numbers independent and Model X 7.5 is not necessarily of the same generation or cut off as Model Y 7.5? Or do all of your 6.1 models have something in common - cut off data, generation, baseline technology, approach, architecture, or something else?
Thanks! :)
These days, version number signifies a few things at once: the order of release, the relationship to other releases, and the magnitude of improvement. So something like 6.1 would indicate it was launched after 5.6, may share some similarities to other 6.1 models in terms of training data / personality (which is why it's not 5.7), and isn't a giant enough leap to merit the headlines and expectations that would come with a 7.
Back in the day we used numbers to also sometimes indicate size, which could make it extra confusing: e.g., we launched GPT-4.1 (an incremental update on GPT-4) after GPT-4.5 (a new model that was a big jump over GPT-4).
What makes this even more confusing is reasoning effort.
You have Luna, Terra, Sol and Astra, each with 4+ reasoning levels. That's ~20 options to choose from.
On a repetitive task like customer support or something, where you have decent benchmarks, you can plot cost versus performance, but you can't really do that on coding.
This makes a lot of sense.
It’s probably just unavoidable. Like when Apple release a new MacBook Air first and it is (in some ways) faster than the old MacBook Pro.
I think the planets thing is a bit confusing though. I hadn’t really thought about it being based on the size of the planet. I assumed it was based on how far away they were with terra therefore being the base one.
I have gone deep in dealing with naming and names.
Versions are good for backend but when communicating I want 2 or 3 levels of detail.
Class_Generation_Modifier
Astra_8_Fast
That's it.
This means: Class - Tells me what it can do, how well. Generation X end of life is ~18 months. Modifier: Variation of options.
And the modifier can be dropped if you add more classes.
I like to use OpenAI models for ingesting a customer brief, usually a paragraph or two and then a read of a several customer files (eg spreadsheets and docs etc) and turning it into a system design blueprint. I'm currently using Astra for this phase but it's incredibly expensive relative to the other tasks that come after this. Would you recommend I stick to Astra or can I downgrade to say Sol or Terra for this job?
I'm just worried the system design won't be as good if I choose a lesser model so to speak.
Edit: typos/grammar
Straight up - why not just name them Cheapest, Cheaper, Expensive and More Expensive? Cost seems to be the main driver of the intuitive difference between models.
Same reason that Apple doesn't name its series of phones the iPhone cheap, iPhone regular, iPhone semi-expensive, and iPhone expensive. It's poor branding, isn't easily extended to future models, and gets confusing when comparing across model generations, where an iPhone 14 expensive might be cheaper than an iPhone 16 cheap.
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"In the case of GPT-6.1 Terra, I'd say Terra is probably better than Astra and Sol at technical communication" -- you're doing the thing here too, underspecifying your own models: 6.1 Terra is better than…which Sol, 6.1, 6, 5.6, just the latter two, all of them? This 2D bluriness is a big part of the confusion.
it depends on the task and it's hard to say
Despite Opus 5.5 being newer and "better" if you read benchmarks, I've found Fable to be better for some things (subjective stuff)
> Models that are trained later might have new data or new techniques or new architectures that earlier models lack.
This seems like one of those cases, where CalVer would be appropriate, and you likely have ideas what you aim for while training, so technical model names (ids?) could include aliases to most things you said. Like 2026q4-xlarge-researcher, 2026q4-medium-humanitarian, 2026q4-medium-coder, 2027q1-small-analyst etc.
This kind of heuristic is actually extremely useful - and mostly what I try to develop over a couple weeks as the new models come out. I would love if we could get a fairly standardized take like this on the regular.
Of course we have far too many benchmarks (of questionable utility), but it would be helpful to get OpenAI's take on where it slots in, and how the landscape changes as these get rolled out.
I think what a lot of developers want is a kind of LTS release model that they can build a product over for 4 years and not worry about deprication.
> important details would be obfuscated from end users.
I don't expect the vast majority of your users care. I would guess they want a rough proxy for "cost" and will assume cost=>capability are roughly linked in a linear fashion.
it is very possible that the users weighted by spend do, and I'm therefore simply wrong. But, 2c
In your own response you mentioned GPT-6.1 Terra, but I've seen GPT Terra-6.1. I would start by the part that is the most powerful:
GPT 6.1 X > GPT 6.0 X GPT Y Astra > GPT Y Sol > GPT Y Terra > GPT Y Luna
This order is very important.
Use colloquial terms: Big, Fast, Genius, Affordable, etc.
This is legally dangerous for the company because those words have generally well understood meanings, so a lawyer could argue that they promise a certain level of service. It’s safer to use vague words to describe the product variants.
Trmendous, huge, bigly
What prevents OAI (and Ant for that matter) from releasing new version for all models at once?
Instead, we got Opus 5.5, Sonnet 5.5 and Haiku 5.5 released weeks apart, and so are Sol 6.1 and Terra 6.1?
My guess is nothing, and they're currently just releasing them individually as soon they're ready instead of all at once so that they hit the market more quickly. AI moves quickly enough that a delay of a couple weeks could result in significant lost revenue.
Lower models typically use distilled outputs from higher model to improve .
all training runs have to at some level share the compute capacity ; while they have more than enough to run many large runs concurrently, they don’t have enough compute to run everything concurrently.
> I work at OpenAI and I can try to explain our logic [...] general, models that are bigger will be more performant.
> If you have suggestions on how to name our products better, I'm all ears. Our intention is to make things clear and simple, even if we miss the mark sometimes.
How about naming the models:
"Small", "Medium", "Large" and "Very Large"
Or refer to them by parameter count like other companies (e.g. this is 6.1 5T)
If I have a clothing store, and rather than name the clothes sizes Small, Medium and Large I decide to call them Urgon, Planfar and Fangor I shouldn't be surprised if people think my size naming is confusing or silly. And IMO it's not a great explanation to say "oh it's easy, if you are skinny then you should get Urgon, but then if you are a bit more of a medium build you should get a Planfar, but if you want something a bit baggier than Fangor will really work out for you"
The problem with small, medium, large naming is that they don't last. In a few years our names would have to advance to labels like giant, behemoth, gargantua. This is different than clothing, where we know that in five years humans will be most the same size as today.
As a point of comparison, GPT-3 was launched in a series of small, medium, large, and XL. Back then, XL meant a mere 1.3 billion parameters!
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Reminds me of astronaut sizing charts for ... their waste management devices.-
The length of your explanation shows just how bad of a naming scheme this is.
> Our intention is to make things clear and simple, even if we miss the mark sometimes.
Well you definitely missed that mark extremely badly here.
What would you have like to seen instead?
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> If you have suggestions on how to name our products better, I'm all ears
Terra 2026.2
Is there someone I can email directly about fine-tuning - all my models have been queued for 24/48 hours and your customer support seems non-existent
Fwiw, if you have to explain your product's name to the customers who just want to use it, that's a sign the products are confusing.
Tiny/2026-11. Medium/2026-6. Big/2026-10. XL/2026-01. It may not be sexy, but people know exactly what it is and what to use.
Disappointing absence of Galactica.
If there's no fixed ordering, can we have model names that describe the capability?
Have a model alias called "math", another "jokes". Quality labels/date suffixes to these obvious labels. Repoint as needed.
Something for people who don't want to play the cute "which part of the solar system is trending this week" game.
I promise you this would become a giant mess. No one wants to pick GPT-good-at-writing-but-not-in-documents-and-decent-at-instruction-following-but-has-some-known-quirks-to-be-aware-of-p.s.-good-at-knock-knock-jokes-MAY-2027.
Even "writing" as a single concept is very multidimensional. There's fiction writing, nonfiction for experts, nonfiction for learners, deep research, quick lookup Q&A, copyediting, writing in documents / artifacts, instruction following about style, and default quirks (e.g., em dashes, negation, loving the word "tapestry"). Some models are great writers but less diverse, meaning people prefer the first story they read but disprefer the tenth. How does one label such a model?
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What do you understand by “technical communication”?
Would you like to recommend a model that does not produce errors and can be relied on like a traditional computer, please.
You certainly know that it happened not by accident that traditional computers are producing reliable results, a lot of work went into this.
Computers must produce reliable results, they are simply not useful if not.
People will understand that at some point, but a lot of damage already has been done.
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naming suggestion: small, medium, large, x-large
it's almost like ya'll let the AI name it ;)
What if GPT 6 small is bigger than GPT 5 large?
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> If you have suggestions on how to name our products better, I'm all ears.
Hire an actual marketing person instead of letting engineers / ML researchers do things they're clearly terrible at ?
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> If you have suggestions on how to name our products better, I'm all ears.
For some reason, this is just hilarious to me. Idk if you're being serious or if this is passive aggressive but the thought of OAI actually taking advice from a random HNer and basing their naming scheme off that is peak.
This is one of the last bastions of semi-serious discussion on the internet, and it's full of incredibly high level engineering talent.
Where else would you go? I'm honestly scratching my head thinking of someplace that wouldn't run you out on a rail, but that would give serious feedback. Kernel mailing lists are out, maybe there's something like that which I can't remember off the top of my head, but...
Also, nomenclature is incredibly hard. There are only two hard problems in computer science: cache expiration and naming things.
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I don't understand what's so confusing about this.
The lineup of models is still the same.
There are four models: luna, terra, sol, and astra.
Pricing increases for each level. This new 6.1 Terra is 50% cheaper than Sol 6.1.
They are lowering prices for newer releases, so yes if you compare Sol 6.1 to 5.6 Terra it won't look right, but why are you doing that? Look at the same release version for comparable numbers.
Each model improves in successive releases but they maintain pricing and capability coherency within a release version.
Yes, Sol 6.1 is supposed to rival Astra for some work, which is what you'd expect from a later versioned release model against a prior release bigger model.
Assuming they release Astra 6.1 at some point, I expect it'll be better than Sol 6.1, at a higher price.
I don't understand what people are asking for here? No reductions in pricing? Different model names on every version release? Not releasing any models for a version number until they're all ready?
Hey! I think it's confusing because there is a mix of three components:
All 3 of these are a one-dimensional balance of price/speed, and capability (Loose terms). The confusion is in how these overlap, how they're announced/marketed etc. So you go from 1 dimensions to 3; you can visualize it as a box. For a given task, how do you choose where in this box you select? Which regions are your favorite? Can you map each region of this 3D space to specific tasks? Label them with intuitive names?
Given the operations of the model are opaque, can you see how someone (most people?) might be confused?
But what's the ask? This is the cleanest way I see of them doing it.
I see a ton of criticism and not a single person coming up with anything better, unless it's to take away options, like removing effort level.
What about buying Intel CPUs? A car? Electrical components?
If you're not just using this at a consumer level to ask about cooking recipes (and if that's your use case, you really don't care about API pricing, or any of this, and can just use the default), and are using it professionally, then this seems like a low bar of effort to understand what you're buying.
I do grant you the relationship between model and effort level has an unclear relationship, but there are also tradeoffs in most complex technical products that are not always clear; this is exacerbated by the fact that this product is stochastic.
Why are engineers using this as a professional tool expecting this to be bought like a consumer gadget and then blaming the maker?
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There's also this "Instant" thingy which doesn't seem to be on the model-name axis but I really can't understand exactly what it is, even after asking ChatGPT about it.
Effort (as it's used in the context, how many "thinking" tokens to burn) is still a free variable. It's not always shown to the end user but you can set it independently for any of these models.
After that you just have model "strength" and version number. But of course strength is only comparable at equal version number and the two axes are almost parallel.
Anthropic also does this, perhaps to a lesser extent.
It's confusing because the models are released one at a time rather than as a family.
Let's say I have some task that Luna 6.1 cannot do, but Terra 6.1 can. So I build my product around that. In a month when Luna 6.2 is released, then I have to figure out whether it's worth to evaluate and swap or just wait for Terra 6.2.
I think a lot of confusion comes from the discombobulated rollout of these models as well.
They released gpt-6-sol (alongside astra and luna but no terra) and 7 days later released gpt-6.1-sol making gpt-6-sol basically DOA before any real adoption. Everyone knows it wasn’t just better RL/post-training, it was a completely different model underneath. Now they randomly reintroduce terra.
I’m sorry, but if you’re not even a tiny bit befuddled by this then you must be taking whatever OpenAI is having.
Part of the confusion is that it's not linear capabilities. One model is not strictly "better" than the other. They have orthogonal capabilities.
I think the names are more of a reference to price. Luna is always cheaper than terra which is cheaper than Sol which is cheaper than Astra (for the same model number).
> I think the names are more of a reference to price. Luna is always cheaper than terra which is cheaper than Sol which is cheaper than Astra
Per token, yes. But per task it's not even necessarily the case depending on how much thinking is needed.
That's why "They have orthogonal capabilities" matters. You need to find the one that's best suited to your particular task.
But the model names are linear by price.
I guess it's the same as Opus 5.5 being in a way better than any Fable.
That's definitely a problem too.
But minor credit to Anthropic, at least they've mostly stuck to their four model names (Haiku, Sonnet, Opus, and Fable). You mostly know where a model is MEANT to be positioned from the name alone. OpenAI cannot keep it consistent from release to release.
But, yes, currently Opus Vs. Fable has the same issue as Sol-6.1 Vs. Astra.
> OpenAI cannot keep it consistent from release to release.
Hmm? They've been using the Luna/Terra/Sol/Astra scale for a few versions now and as far as I can tell it maps directly onto Haiku/Sonnet/Opus/Fable, in terms of the kind of thing it's meant to convey (if not in terms of the scale of differences).
I don’t think opus-5.5 is better than fable-5.1. I think opus is better overall because it’s much faster and cheaper than fable but if speed and cost is not a factor fable it’s probably just as if not slightly better than opus, especially at front-end design (tho the difference is marginal and no one should be really using fable day to day due to this alone).
I don't know why this is so hard to understand.
It's like iPhone 17 being faster than iPhone 16 Pro. Because it's newer and benefits from advances in chip architecture, despite being a lower model tier. Sometimes different iPhone models are released at different times.
Yeah this isn't intuitive at all.
Both companies have screwed up their naming royally. Maybe that's intentional - I'm sure it doesn't hurt Anthropic to have people using Fable a bit longer than they need to and racking up a huge bill in the process.
They're always very clear about it when a new model comes out. You have to click away a banner to continue using the service. There's not much they can do to make it any more obvious without it being a major hassle for all the people who understand versioning.
It is: Fable 5.5 will be better than Opus 5.5. Opus 6 will be better than Fable 5.5, Fable 6 will be better than Opus 6, etc.
You're comparing different generations, any 5.5 models could be better than any 5.0 model.
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It's like Intel i3/i5/i7/i9, or nvidia 50/60/70/80.
It is meant to give a rating inside a generation. Different generations are not necessarily comparable. an i7 is better than an i5, but not if you compare an i7 7xxx with an i5 14xxx.
A nvidia xx80 card is better than a xx60 card, but not if you compare a 1080 to a 5060.
The only difference for OpenAI is that not every generation has every model.
> Did Microsoft's chief branding officer move over to OpenAI?
Obviously not, otherwise the four models would be called Copilot, as well as the harness.
You're just not paying attention. If you run copilot in copilot using copilot effort, it performs much better than copilot in copilot using copilot effort. This is clearly explained if you visit copilot.com using copilot and then search for copilot.
“Introducing our first mobile devices: Pocket Copilot Luna for on-the-go inference, and our new tablets, Copilot Sol+ and Copilot Astra Duo.”
If you daily all these models (and have a modest budget, not some >$400 corporate allowance), then it's pretty obvious that Luna is small and fast, and Sol 6.1 is the big one for jobs that need significantly above average intelligence, comparatively to a human engineer. I use both constantly. Astra is really not relevant for the average dev except to do some moonshot task with a vague verification loop.
Sure Sol 6.1 on low/medium can be considered "cheap" considering its abilities, but it's clearly not meant for the same type of tasks as Luna. You just grow an intuition for when something is solvable with a Luna-sized model. I'm guessing Terra is good for the tasks that you'd typically use Sol 6.1-low for, or for Luna type tasks (or other not-novel problem) where you want to reduce the risks of suboptimal decision making. It's exactly what I'd go with for feature additions where the correct solution is already mostly designed/specced-out.
The way to think of this is that (current) inferencing hardware has natural cut points of efficient scale (assuming some optimal architecture which at this point is almost always an MOE variant), so you always have an incentive to provide 3-4 model sizes (which nicely enough often means diff speed to answer also).
You are also rolling the training of your models by size and the lat est small model can sometimes beat an older model.
For a while we thought you just train the big model first then distill smaller models from it but since we've learnt the importance of on policy RL (basically the way a small model figures out a problem is very diff from a big model so it's best to RL optimize each model distinctly)
Name means size which means price
Yes, a more modern smaller model can beat an older bigger model at some tasks, that's what can be confusing.
Training models is still not a precise science.
If you scale a model down, or switch out attention with linear attention, or introduce KV-compression, etc., you don't really know how it will affect model performance before you've trained the model.
And once you've spent millions training it, you might as well make it available if it's pareto optimal for some tasks. It also gives you more insight seeing how people use it in practice.
Flip it around. The number (generation) is the most important. Just like it's often with hardware.
And some variants (e.g., the medium one) being skipped or delayed for a certain generation doesn't really change the overall size branding.
Agree, it's confusing. I hear people discussing which model to use for what tasks and my head starts to spin because there are so many properties to account for and it's generally hard to reason about LLMs.
"Speed, quality, price: pick two". I think I'd rather see a choice like that than the current convolution of $-per-token, sub quota, latency, simulated reasoning level, model size, context size, etc, etc.
>But then things go confusing, they released Sol-6.1 which sometimes is superior to Astra, and discontinued Terra which made it look like this
I don't want to rush to their defense here, but, when model families are getting refreshed every 6 weeks, there is going to be significant overlap, particularly with these staggered releases
The moon is smaller than the earth. The earth is smaller than the sun. The sun is smaller than all of outer space.
Next AGI test, when model companies have consistently good version branding.
And low/medium/high/xhigh/ultra reasoning. And pro mode and ultra-speed versions. There's at least 25 options to choose from.
Wait I always thought terra was the smaller model and the further away you went in space the bigger the model (luna is further away so it’s better than terra)
It's especially confusing because Sol 6.1 is mostly just Astra but 10x slower at 1/10th the price - unless you pay for it to be as fast as Astra - at which point it's the same speed and price...
It's nice that they have Terra which seems to fill a void that's been needed since Sol 6 focused on cost (at the expense of quality), Sol 6.1 got a huge boost in quality, but became unusably slow unless you paid Astra prices - at which point, why not just use Astra?
Now at least we have a viable other option again.
Huh? I have not observed Sol 6.1 and Astra 6 to be the same.
You're not alone on this. They have too many models, and too many effort levels, it starts to feel like what to chose.
the only confusing bit was dropping terra for a bit,
Luna (Small), Terra (Medium), Sol (Big), Astra (Bigger)
is perfectly clear, sol beats astra sometimes because Astra 6.1 was scrapped. and note that opus 5.5 beats fable 5.1 most of the time too without a skipped model.
No idea, i still try to figure out how to use the right one per task.
Different classes and different generations. Isn't it roughly the same as what car manufacturers do? Or even Macbooks? 2014 macbook pro has worse performance than 2026 macbook air, this doesn't confuse consumers.
I suspect 6.1 Astra is the model that kept showing up in the news stories about an internal unreleased model trying to hack the Pentagon or whatever.
also am I the only person who remembers Do Kwon and the Terra/Luna debacle?? how about Gaia/Selene (admittedly harder to non-native english speakers)
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