Hacker News

Packaging & pricing seem to be two things Anthropic really struggles with

do people really use luna post 100k? small models generally seems to be hallucinating hard for me once you are over that threshold.

You wouldn't believe any of that if you've used MiMo 2.6 Pro. DeepSeek 4.1 flash 1017 is better in actual use in every regard.

I just imagined reading this comment like 2.5 years ago :)

Honestly I'd much rather take this model then.

It is having improvements in other fields than coding while being more cost efficient.

If all I want is coding at a cheaper price, then deepseek is a good model for that.

I am more than okay to have a bifurcation of models good for coding and models good for everything other than coding. I strangely imagined it many years ago.

Everyone is suggesting OpenAI to release a model close to opus 5.5 yet it's releasing this. On one hand we can imagine that maybe their training cycle wasn't able to produce this whereas gpt 6.1 terra was made.

But on the other hand, I also now wish to see an OpenAI model which is good for just pure coding, like an absolute beast at it while being cheap (so the opposite of terra)

ironically, one of the few appreciations that I might give to OpenAI if they pushed this model to prod and are facing some pushback as developers like us want/only see coding models. They could've benchmaxed it and it would've been slightly irrelevant. I'd much rather see reality.

At the very least, this new model won't make things more expensive for anybody and would only make use-cases cheaper depending on what they are, so it's a fine deal for me.

I also wonder maybe this is why open source labs aren't targeting this space and why the only focus of AI labs is coding nowadays. Everything else just doesn't seem so worth it from investor, even end user, training perspective.

Maybe this is the reason why we have gotten almost an exponential growth in coding capabilities as that became the most (if not only) capability that truly really matters.

To not wish for discussion to go to a tangent, nonetheless this could also explain why our work as a software engineer is going through a slight crisis of meaning as these AI models have gotten impressive at coding.

As an (aspiring) developer (currently in 1st year college), I don't wish for AI to replace coding even more for me or to push it even more or to suck the joy out of it even further. So I am more than happy for these models to stay as they are and rather make advancements elseplace. Ironic because it is us developers who use AI as well and we want cheaper workflows and so we would also want models to be cheaper and better at coding. There are some fine lines of contradictions and irony in all of it. I'd much rather also have open-weight models of such terra-like models but I imagine that because of the reasons that I mentioned above, we are most likely gonna see it the most focused on coding but still open-weights is pretty close.

The more choices the better (though choice-fatigue is a real thing), I hope I was able to explain some of the nuance I feel about this particular launch.

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.

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

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.)

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

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.

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.

> 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.

> 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 :)

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.

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.

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.

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.

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! :)

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.

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 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

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.

"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.

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.

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.

> 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.

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"

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.

Disappointing absence of Galactica.

> 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.

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”?

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.

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 ?

> 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.

Hey! I think it's confusing because there is a mix of three components:

  - Model name
  - Version number
  - Effort level

Given the operations of the model are opaque, can you see how someone (most people?) might be confused?

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.

> 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 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.

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.

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.”

Huh? I have not observed Sol 6.1 and Astra 6 to be the same.

> leaving Anthropic providing about 5x the value on their plans

5x is calculated in terms of API costs, nobody pays those extreme Anthropic prices outside enterprise users.

> like they didn't just double the cost for most users

Most users aren't on their $200 plan. Most users (including on the $200 plan) continue being subsidized.

Cost per task, time per task, quality (good luck measuring objectively between models) are the metric that matter. I don't care how much a token costs, I'm not buying tokens, I'm buying solutions.

It's neat that we can now compare the Terra without reasoning pelican (cost 0.64 cents) with the equivalently priced Luna with reasoning pelicans (Luna xhigh is 0.40 cents, Luna max is 0.91 cents). I think the Terra low one compares favorably to those, though I'd suggest also comparing them on tasks more representative of real work than generating pelican SVGs!

Terra without reasoning is almost certainly going to be faster than Luna on High (which is roughly equivalent in intelligence according to AA).

a bigger model still has more built-in knowledge to draw from, even without reasoning.

I should do but no, it's just a bad HN habit.

Apparently Google made it a point to train their models to output SVG, among other things, so that helps

pelican is also nitpicked (and probably the most useless)

> the model names are a bit confusing so let's just not bother.

I said neither of these two statements or claims. The fact is that OpenAI's model family and release is not standardized. This means that if I have applications that are using the high, mid, and low tier models, e.g. at v5.6, it's not straightforward for me to update to a new version, e.g. v6 or v6.1 of these models because there isn't immediately a 1:1 mapping at the time of release of the version. Competitors to OpenAI don't have this problem, so I will bother with them.

The implication is that 6.1 Terra should've been released together with 6.1 Sol and Luna on the same day, and that these tiers should stick across versions.

Yes but you are missing the point. It's the case where Luna is not good enough, and Sol is too much. The problem is that on benchmarks Luna xhigh is typically comparable to Sol low in terms of capability. So where does Terra fit in?

I guess the question is whether you could have done the same using Haiku High/XHigh or Opus Low/Medium at roughly the same speed and cost.

Except Sonnet is slower, more expensive, and worse at reasoning levels above ~medium. Opus on low is faster, cheaper, and better at doing things.

This is the only reason that makes sense to me. Thank you

Maybe it's my tasks, but in the cases you're describing, I'm usually better off going down to Luna. I specifically don't see the gap where "It's too complex for Luna-high" and "It's not complex enough for Sol-low" exists.

New version of "it's not that deep" dropped.

I feel like OpenAI hasn’t been as (publicly) hypocritical and petty about their distillation spats as Anthropic has been?

My understanding is API prices are more expensive then monthly plan, is this true?

My plan for OpenAI recently expired and now I’m thinking to test Anthropic ecosystem. My primary use case is programming only.

Should I go with API use or subscription? For coding harness, I jump between codex, OpenRouter, pi, whatever coding harness is trending on that day

Yes, V4.1 Flash 1017 is my daily driver cloud model. Copes fine with what I'd consider difficult brownfield development, like fixing bugs in vLLM prompt cache and coming up with small/sane overlay patches for them. Maybe it's disappointing for people who want to one-shot a game or website from a single prompt; I haven't tried that.

It's nice that their peak-rate hours end quite early in the day UK time.

I use it at work. It's dirt cheap, blazing fast, and is more than enough for most coding work.

If you live in a country with a weak currency, DeepSeek models are definitely a godsend.

You can do real world development with weaker models than Deepseek Flash.

I still use SOTA for the main model though, but also I'm perfectly aware it's mostly out of FOMO.

Deepseek is more than enough for default subagents though. Any well defined and verifiable task and there's absolutely zero difference between SOTA and good-enough models. Used to be just speed, but Deepseek is fast.

There’s also a chance they surrender the dev market altogether to Ant and just focus on the “normie” segment with dots, computer use, excel etc. In which case they won’t have anything competitive to opus-5.5 anytime soon for SWE.

Which is why I don't take the benchmarks at face value...

Taking the benchmarks (as reported by OpenAI) at face value, why wouldn't they show Opus 5.5 on the OS world benchmark, and show no Anthropic models at all on the "factual error rate" benchmark

...no it's not? At max effort, it scores 50% on terminal bench vs. Opus 5.5's 70%.

That's wild to say when comparing to the OpenAI family of models. I've never seen so many tests that validate tests that validate tests.

Medium or High? Depending on the task, you might want to switch between them.

You’re holding it wrong.

2025 called and it wants it's take on AI detection back.

https://www.pangram.com/blog/third-party-pangram-evals

Note that while that page is from Pangram, the studies on it are not. If they are excluding studies that show Pangram performs poorly in realistic conditions, please share those studies!

If Pangram was snake oil (it's not but could be more mindfully marketed), it's definitely not now that every major lab has added watermarking.

You wouldn't even need to be able to read the information the watermark encodes to recognize it exists in a piece of text

Everyone defaults to checking pangram when AI is susepcted, so it's still an important test.

True!