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.

> 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.

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.

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?

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.)

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.

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

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 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.

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.

"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.

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

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.

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! :)

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.

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.

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.

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.

> 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.

> 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.

The length of your explanation shows just how bad of a naming scheme this is.

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?

> 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"

Use colloquial terms: Big, Fast, Genius, Affordable, etc.

> If you have suggestions on how to name our products better, I'm all ears

Terra 2026.2

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.

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.

> 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.

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.

What do you understand by “technical communication”?

naming suggestion: small, medium, large, x-large

it's almost like ya'll let the AI name it ;)

> 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.