EDIT: this doesn't say anything about availability on either Azure or AWS. I'm assuming it will show up later, but it would be interesting if it didn't.
Design: https://image.non.io/78795662-8bfc-4e14-8d72-3738392aa6b3.we...
All 3 were given the same prompt to dynamically light these and to create the designs as a SPA with page transitions.
Astra: https://html.non.io/annui-astra
Sol: https://html.non.io/annui-sol
Luna: https://html.non.io/annui-luna
Luna gets the button wrong, and in the same way Grok/MiMo did. Looking into it more, it's because Luna actually searched my computer for similar builds, found the ones that I did for grok/mimo, and referenced their files. Astra is still the best by a significant margin in my eyes. Far more polish, better page transitions, effects that aren't overcooked and take into account the page. Better contrast.
When 5.6 dropped I had no weekly limits and I could just drive my work with Sol xhigh and things were great. Once limits were back (and maybe token prices changed iirc) Sol was no longer usable (on Pro or business) unless I was ok with 4 prompts every 5 hours, so I had to switch to Terra medium/high. I've used Luna for some really dumb tasks like moving files, renaming variables and whatever other old-school refactors I've needed.
Then Astra dropped and it just uses so many tokens I've only prompted with it once. Now with GTP-6 Sol/Luna I'm not sure what's being said here but most importantly I'm wondering whether Luna 6 is a good replacement for Terra.
Has any other Terra user tried and knows more or less than answer to this?
Now GPT 6 Luna is even cheaper, and more intelligent, there is no going back... to SOL 5.6 for intelligent layer.
I was hoping for a serious Luna upgrade. It was already cheap enough. This feels more like a price reduction than an upgrade.
That said, if the new Luna is able to handle ultra mode and subagents v2 in codex cli, then at least that’s a win.
I forgot which model degraded in quality as time went by, but let's try out Luna 6 for a few more days to confirm for upgradability.
For me it seems like benchmarks are mostly noise, and the rest is based on vibes. Some find newer models annoying, some are amazed.
[0]: https://aibenchy.com/compare/openai-gpt-5-6-terra-high/opena...
So essentially I was not able to get nowhere close to the accuracy of previous model and it was slower, and more expensive at the same time.
Now gpt-6-luna, has really competitive pricing and offers similar accuracy compared to gpt-4.1-mini fine tuned for my specific task. And fine tuned models are getting deprecated anyways, seems like a good time to move to gpt-6-luna.
P.S. This cindyllm seems to be stalking me whenever I comment against the grain, does anyone else experience this?
I thought it should have been long dead of all the downvotes it gets, but there we go.
All I want to know is how old is the model and how much does it cost. I can figure out which one I want to use based on that, assuming that newer models are always better.
Trying to convince us there is a difference between GPT-6-Sol and GPT-5.6-Terra or whatnot is ludicrous to the point of being insulting, especially when new models come out every week.
What's unclear to me is who OpenAI thinks they're marketing to with this form of branding. These different models don't really mean all that much to the vast majority of people using their products who aren't developers, and developers aren't helped at all by the way they've been naming said models. Are they merely scared that they'll become irrelevant because Anthropic decided to give their models quirky names like "Opus" and "Fable"?
If OpenAI really wants to give their models names, they should name the generation of model and then have the different sub-models named by purpose or capability level. After all, I wouldn't use Mini for a job that Nano could easily do, and I wouldn't use Nano for a job that the full version of GPT-* necessitates. Similarly, I've had to discover exactly how Luna, Terra, and Sol are appropriate for different complexities and task types. OpenAI could help me skip a lot of those steps and just tell me what each model distillation is good for without causing me to look through their pricing page and make educated guesses. After all, shouldn't they not want me to pay attention to how much they're charging me?
All of this makes the days of frontend framework churn seem quaint and actually preferable.
The issue that OpenAI had when they had mini and nano models is that ambiguous the differences between those and everyone just used the base model anyway. I have no idea what type of job mini can do that nano couldn't or vis-à-vis.
I do wonder if it would just be better if they were named 6-small, 6, and 6-big?
In my experience, Nano won't reliably handle complex open-ended tasks and is mostly suited for very explicit instruction that it can't screw up. It's no different from how there are some chores you can give to kids and there are other tasks you need at least a teenager for. If the decision tree of the task is very clear and conventional, Nano can be cheaper than giving the task to a relatively overpowered model, especially if it's something where the output is rigidly structured. This makes it well suited for skills that essentially run CLI commands and generate output, especially because it is usually faster. Mini is more like a discount version of the base model, and Nano is the dollar store version. Mini is more of a generalist and a fairly good deal if you have a moderately complex task that is conventional, but can be less conventional that what Nano can handle. I mostly used gpt-5.4-mini this year for my side projects because it's a pretty good generalist while significantly saving on costs. It is, however, somewhat dumber than the base model and more prone to ignore or forget rules you give it. I'd have just used a base model, but the low cost of Mini and Nano made them appealing to me. Maybe I'm a cheapskate, but I have hundreds or possibly thousands more in my pocket than many other users because of that.
This workflow I settled into with Mini and Nano didn't map cleanly on to the current generation of model tiers. With the price of Luna, you'd think it would be a replacement for Nano. In a sense it is, yet I didn't find that Terra became the new Mini. Terra is more powerful, better at explaining its own decisions, yet I've also found it to be relatively stupid while charging me more to use it. On the other hand, Luna with its reasoning set to "high" is what I consider to fill the role of Mini, and is good enough such that I no longer use Mini. Sol and Astra are great, but they're pricey. It could be my own brain and its bad perception, but so far I don't get the point of Terra. Luna succeeded at reverse engineering some abandonware with a very complicated licensing and virtualization scheme, and did so over SSH into a Windows VM with only PowerShell on the other end. Terra did such idiotic crap to my flashcards app that I stopped using it for anything after that.
This is why I find OpenAI's naming unhelpful and kind of pointless. I don't really care about the benchmarks that all these models are commonly run against. They're not that useful, IMO. OpenAI could easily give early access to these models, get a ton of feedback, and provide better insight to customers on how these things behave. Even calling Terra "gpt-5.6-overpriced-cheating-dumbass" would be better than wasting my time and money figuring it out myself. But that wouldn't make OpenAI as much money.
