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Advancing the price-performance frontier with GPT‑5.6

openai.com

101–110 of 424 posts

Re: Advancing the price-performance frontier with GPT‑5.6

#101
post #58
post #33

Earlier quoted context omitted.

Where are you going to apply to with that resume that’s a step up from your current job though?

lots of places, actually. not everyone wants to be attached to the Silicon Valley culture, and that line alone will guarantee practically any workplace. that person is going to find out what work-life balance is :)

Sure, but those places don’t need such lofty resumes to begin with.

Re: Advancing the price-performance frontier with GPT‑5.6

#102
post #33
post #7

Earlier quoted context omitted.

imagine writing that on your resume > reduced inference cost by 20 percent saving company x billion dollars per month

Where are you going to apply to with that resume that’s a step up from your current job though?

The other place, but for more money

Re: Advancing the price-performance frontier with GPT‑5.6

#103

Earlier quoted context omitted.

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.

We can guess based on the decisions of other inference providers who serve these models.

[dead]

Re: Advancing the price-performance frontier with GPT‑5.6

#104
post #14

> 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.

it's 80% less cost, not 80% in efficiency gains, could be that Luna was overpriced to begin with, we don't have much info on the models themselves. Assuming the efficiency gains are real, I feel like something has to give, maybe worse quality due to aggressive quantization/kv cache compression?

Something can be overpriced and still lose money.

Re: Advancing the price-performance frontier with GPT‑5.6

#105
post #14

> 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.

This is exactly the model that DeepSeek V4 Flash followed, and it's been insanely successful as a result, even though it's not frontier.

Re: Advancing the price-performance frontier with GPT‑5.6

#106
post #82

Earlier quoted context omitted.

Luna is comparable to Haiku, not Sonnet.

Totally untrue. Luna and Sonnet 5 are very comparable: https://artificialanalysis.ai/#intelligence Luna is an extremely strong model.

> Luna is an extremely strong model.

By benchmarks, which sadly is a poor measure. Yes Luna is a good model under certain circumstances. Whether it is great for general usage is another story. Sonnet is definitely better when prompts are more vague and it needs to decide things. Luna generally sticks to things very strictly and goes off in bad ways.

Re: Advancing the price-performance frontier with GPT‑5.6

#107
post #69

Earlier quoted context omitted.

It all comes back to electricity cost. China has cheaper electricity so as long as China keeps pace there is no way for American companies to undercut them. Each boolean operation in China is cheaper than the one in America. > China: Household rates average around $0.08 / kWh (¥0.53/kWh). vs > US: Household rates average around $0.16 / kWh, though regional variation is massive—ranging from ~$0.10/kWh in low-cost stat…

This doesn't seem correct. Estimated final electricity price for large industrial customers in energy-intensive industries: USA 50 USD/MWh China 68 USD/MWh https://www.iea.org/reports/electricity-2026/prices

[deleted]

Re: Advancing the price-performance frontier with GPT‑5.6

#108
post #47

I 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.

The official doc says, Luna = Previous Nano models, kind of. Is it really good at coding?

Smaller models are great if you are doing targeted changes in existing codebases. Don’t expect to use it for creating complex architecture from scratch or do major refactors. The larger the context, the greater the drop off will be.

Re: Advancing the price-performance frontier with GPT‑5.6

#109
post #47

I 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.

The official doc says, Luna = Previous Nano models, kind of. Is it really good at coding?

According to the link I mentioned above it's roughly as good as GPT-5.4. Haven't tried it in practice yet.

I bet it must be better in some contexts and worse in others.

Re: Advancing the price-performance frontier with GPT‑5.6

#110
post #35

This 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…

How do you run 'deep research'?
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