Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Advancing the price-performance frontier with GPT‑5.6

openai.com

Post reply on HN
Print view View original

381–390 of 424 posts

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

#381

Post by killix »

gck1 wrote: post #286
apitman wrote: post #281

Earlier quoted context omitted.

That's surprising. Does it have no sub agent support at all or does it just use the same agent as the parent?

They do have subagents, released v2 of that feature with the launch of 5.6 model series in fact. It's just... very poorly executed, is a significant regression from subagents v1 and thousands of miles behind subagents of Claude code. - Models that can be launched as subagents are hardcoded (can only be another Sol or Terra, but not Luna). Most of the time it'll just launch same model as parent anyway. - They encrypt…

[flagged]

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

#382

Post by welcomezhangjun »

firasd wrote: post #34

This is one of the things OpenAI has been focused on for an year or so that led to the doomed autoswitcher in ChatGPT .com (switching models based on estimated task complexity) that was quickly reverted Whereas Google with Gemini 3.x, Anthropic with Fable etc are happy to just go for 'big model with dense params' It's hard to guess from the outside of course but just this kind of talking points focus on GPU efficacy…

[flagged]

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

#383

Post by theshrike79 »

ch4s3 wrote: post #336
Cort3z wrote: post #302

Earlier quoted context omitted.

The magic is in the fact that they essentially have an ASIC llm device. There is no other trickery. The problem they will face is that it is actually locked in silicon, so upgrading models will be difficult, and likely require new hardware each time.

At some point I imagine you’d add a software layer on top that holds more current training and can be called as needed trading off for slower responses. There’s already work out there splitting models across networks. You could have the base on silicon, some stuff in memory on the machine, and another frontier tool in the cloud.

There are already studies proving that a "stupid" model with a good harness + tool calling will outperform a "smart" model.

Things like this give me hope for a system that can be fully local and private, but also with the ability to be almost infinitely extendable with tools.

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

#384

Post by Yopolo »

wronex wrote: post #187
Yopolo wrote: post #181

Earlier quoted context omitted.

Agentic layer. Your support bot. Your research long running bot. Your SEO Optimizer bot. Your incident analyser bot. Your personal assistent bot.

Game play bots (monsters, commanders maybe) would be really cool. But it needs long term support and probably local AI instead.

Yeah great point. 2 years ago i wouldn't have thought that this is even feasable but today?

Lets start of with 1000 ai credits which should be enough for one playthrough, have it included into the game.

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

#385

Post by dannyw »

sipjca wrote: post #312
dannyw wrote: post #83

Earlier quoted context omitted.

OpenAI's APIs are extremely reliable for sure. I don't even remember when the last incident or downtime was.

all day yesterday, Taiwan time Sol was having significant issues. “Overloaded”, maybe half of requests or more wouldn’t go through

Was that Codex/subsidised-usage or API? I do get overloaded in Codex-account from time to time, but API is rock solid.

They obviously load shed a bit of Codex-sub during peak times, and for the amount of tokens you get for a sub, I don't mind. I just mean the API where you pay-per-token is rock stable.

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

#386

Post by dannyw »

paxys wrote: post #108
hattimaTim wrote: post #91

Earlier quoted context omitted.

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.

Smaller models are also excellent executors/operators. When given a solid plan, including e.g. what files/functions to change and how, what to test, even a recent 27-30B open weight model will usually do an excellent job.

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

#387

Post by weiliddat »

nerdsniper wrote: post #191
jcul wrote: post #159

Earlier quoted context omitted.

It's crazy. Are they doing any precomputing as you type, I wonder if you paste a block of text is it the same speed.

I pasted and instantly hit enter on this prompt: "I generated a filter set using REW v5.31.3 using real-world sweep tone measurements from the room I'm listening in . How can I use it as my MacOS output equalizer so that my spotify music is adjusted for this room and speakers" and it gave a very reasonable answer in non-perceptible time.

idk if you're still actually looking for a solution (I got nerdsniped heh), but I found a seamless way to have it locked into the speakers + room, instead of a specific source/software, is using an iLoud subwoofer that routes to any speaker setup and has room correction + EQ that lives on the sub.

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

#388

Post by dannyw »

wronex wrote: post #49

What 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?

General light/defined tasks, e.g. go fetch a list of rows from and then run script against each.

Yes, I can just do it myself, but even at API prices, I'd rather have the LLM do it.

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

#389

Post by adrianvi »

5555watch wrote: post #321
jrflo wrote: post #115

Earlier quoted context omitted.

Burning the weights into silicon would be many orders of magnitude increase, not just 10x. It's kind of crazy that this hockey stick the AI hype bros talk about seems more and more every day like it might be real

I'm curious, how hard/expensive it is to burn a really large model into silicon, and why aren't we doing this already? Or, when we will start doing this, who's going to be able to do that in scale? I'm seeing the TAALAS example, but it's only an 8B model, suggesting some real limitations parameter wise. And for 2.5kW?

I can't speak for all cases, but the AI space is seeing improvements month by month, so it is beneficial to wait until it settles (a model becomes the standard in intelligence/price) before designing and mass producing an "LLM ASIC" of said model.

The big AI labs won't do that unless they are forced to, as they want you to spend more money on the big, expensive, frontier models (so they can live up to their valuation), so it's more likely that you will see this on smaller open weights models.

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

#390

Post by biorach »

theshrike79 wrote: post #379
taf2 wrote: post #338

Earlier quoted context omitted.

I remember bugs always end up being more expensive then the first time I implemented the feature… so for me always sol always max… I don’t want to pay for a bug later

Using Sol or Fable for implementing is like having your Principal Staff Engineer with 30 year tenure routinely write CRUD functions for a REST API. You use the big models to plan. Not just the overall plan, but which files need to be edited etc. Then they give that to the lower end model. So Luna or Sonnet, which are perfectly capable of following instructions and still creative enough to not get stuck.

> Using Sol or Fable for implementing is like having your Principal Staff Engineer with 30 year tenure routinely write CRUD functions for a REST API.

A Principal Staff Engineer who costs $2400 a year and never feels any work is beneath them? Hell yeah.

OK OK, usage limits

Post reply on HN