Basically lunar at extra level can cover all use cases scenarios other than those requiring opus up. Goodbye sonnet and haiku
Advancing the price-performance frontier with GPT‑5.6
41–50 of 424 posts
Re: Advancing the price-performance frontier with GPT‑5.6
#42Looks like I might have a reason to use something other than Deepseek V4 Flash.
Re: Advancing the price-performance frontier with GPT‑5.6
#43> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.
To be fair we don't really know in terms of prices what's real and what's just investor subsidised attempts at market capture at this point. It could well be OpenAI's attempt to drown Anthropic while they've got the halo product if they feel they've got deeper pockets.
Enterprises implemented spending caps and inference providers are lowering prices. Seems they are jockeying for market share.
Re: Advancing the price-performance frontier with GPT‑5.6
#44"Half the money I spend on advertising is wasted; the trouble is I don't know which half." -John Wanamaker
This applies even more strongly to model choosing. I know for a fact that majority of my work doesn't require a very strong model, but separating the trivial and non-trivial tasks is a famously hard problem (if at all decidable).
Re: Advancing the price-performance frontier with GPT‑5.6
#45> Starting today, GPT‑5.6 Luna, our fastest and most affordable model, will cost 80% less, I don't have the words. I genuinely thought we were in a stage where we were plateauing and going in for 5-10% improvements over months. Seeing spikes like this makes me question about where the floor really is.
Hard to believe numbers. I don't mean that as a critique, but literally I am so impressed. Even if the model is a few percent lower for performance but is 80+% cheaper than competitors and is a US company hosted on US based hyperscaler clouds this is kind of a no brainer. Hard for most businesses to justify otherwise.
Re: Advancing the price-performance frontier with GPT‑5.6
#46Earlier quoted context omitted.
imagine writing that on your resume > reduced inference cost by 20 percent saving company x billion dollars per month
In this case, and I don't mean this critically, I guess it would technically be, "Instructed model to find efficiencies... reducing inference cost by 20% saving company x billion dollars per month." I have no doubt that further work was required to enable this, but it's still very cool to be possible to say that.
I think they meant that GPT-5.6-Sol can write that on its resume.
Re: Advancing the price-performance frontier with GPT‑5.6
#47I generally just check the Price/Performance graph on Openrouter: https://openrouter.ai/rankings#performance#benchmarks. Activate the "Show Pareto" toggle on the right.
I was still using GLM-5.2 in my personal projects, but this just made Luna a very easy choice.
Re: Advancing the price-performance frontier with GPT‑5.6
#48Making Luna, which was already very cheap and extremely capable, 5x cheaper is crazy. I use Sol at work but Luna at home, and while there's definitely a difference, it doesn't feel like night-and-day. After a year of ever-increasing prices it suddenly feels (between this, Kimi K3, GLM 5.2) that prices are falling again.
is kimi that cheap? it's a very expensive model
Re: Advancing the price-performance frontier with GPT‑5.6
#49What are your use case for these? I’m manly interested in coding where more capability is better - give me a 10x model at 10x the price and I’ll take it. A worse model at very low cost has no appeal to me. At-least not for coding. Translation maybe? OCR?
Re: Advancing the price-performance frontier with GPT‑5.6
#50This feels like the dialup->broadband transition to me. I was already a huge proponent of Luna for things like deep research. Being able to run 5x more for the same cost is simply bananas. We are already running 10 parallel agents for hypothesis generation. I cannot imagine 50. The statistics become much more interesting & powerful when you can run so many samples of the exact same prompt+model without breaking the b…
Do you have a sense of which tasks benefit from more agents and which don't?