alt Hacker NewsI really wish there was just an easy guide on when to use Sol vs Terra vs Luna, and it just moves further into confusing territory when it comes to naming.
The naming convention is especially difficult to decipher depending on what your native language is. Of course a latin language speaker might be able to easily determine oh yeah each one is slightly bigger than the other but I still think it borderlines too confusing.
That aside all the numbers look amazing, and I'll be happy to probably main this alongside grok-4.5 for a while comparing the two on price and efficiency.
I vastly prefer the direction that OpenAI seems to be going with token efficiency and performance compared to Anthropic who seems to be moving towards a world where you just token-max as much as possible ignoring any and all costs.
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My guess is that it's the same for Haiku/Sonnet/Opus: Biggest model for architecture and high level planning and technically challenging problems, medium model for simple implementation tasks, small model is for nothing
It's just how LLMs work - these are three completely separate models trained in parallel, with different numbers of parameters and using different amounts of compute.
They could hide this behind a harness that picks the correct model for you, but devs don't seem to like that.
There's also the 'effort' slider, which I guess how many experts in the MoE are evaluated and how long reasoning chains are allowed to go on, which is the 'smooth' scaling you are thinking of.
> I really wish there was just an easy guide on when to use Sol vs Terra vs Luna
Their dev guide has the following:
> Use gpt-5.6-sol for frontier capability, gpt-5.6-terra for a balance of intelligence and cost, or gpt-5.6-luna for efficient, high-volume workloads. The gpt-5.6 alias routes requests to gpt-5.6-sol
https://developers.openai.com/api/docs/guides/latest-model#u...
I love how all the replies to this comment recommend completely different strategies for deciding which model to use.
it's simple: unless trivial TOIL, always use the highest at ultra max settings.
In my tests, in almost all cases, using Sol on (low) reasoning is the best option intelligence/price-wise.
Luna is good too, for classification tasks or any pre-processing task that is not critical
the size comparison didnt occur to me. I assumed the names were just random nice sounding focus-groupped marketing names.
isnt native english speaker
"i really wish this thing in my non native language was easier to decipher"
huh? if you dont know the words then read them in your native language. Sol/Terra/Luna are immediately unambiguous to an english speaker with any sense.
Use Luna. It's more performant than 5.5 and it's cheap. Hopefully it's cheap because it's more environmentally friendly than the bigger models. So you're doing a good thing. If it's a smaller model it may even be faster, but I haven't looked into it yet.
Why would you need a guide for that now? We long had to pick different models (and thinking levels) by task and feel.
> I really wish there was just an easy guide on when to use Sol vs Terra vs Luna
Terra when you need to get shit done here on Earth, Luna for moonshots, and SOl for when you want to launch something into the sun..
..right?
You don’t know what sol means? You don’t understand the difference in sizes between Terra and sol? I’m genuinely asking.
I use the strongest model (5.5 now 5.6 sol) on the highest reasoning effort with /fast for everything. With a $200 pro sub I can't even use my weekly limit. And it's faster than using a weaker model that makes more mistakes which I have to waste time fixing.