OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)

Though I think they gave a banked reset this time.

There are no open source models, at least not useful ones (yet) [0]. Open weight is not the same as open source. The current "open weight" models are just opaque binary blobs you can run on your own computer instead of through a web API.

[0] https://allenai.org/

Training data and code.

While the training code and data are the true source. Since if you want to robustly modify the LLM that's actually what you need.

But since "compilation" (training) is extremely compute intensive this isn't something accessible to anyone without an entire datacenter.

Anyway semantics aside having the binary is still infinitely better than dealing with an api as far as privacy and control go.

I don't know LLM theory well enough to say if there's some secret sauce they can hold back that makes training ineffective. Less effective I'm sure, we don't have access to their smart training schemes, but post-training should always be possible IIUC.

post-training is like writing a wrapper around the binary. It is closer to building on top of than truly modifying, in that you can tailor things to your needs slightly but cannot make fundamental changes to the underlying thing.

For a stretched analogy, I think it is more like LEGO sets. Someone hands you a 10,000 piece masterpiece, and a box of unused LEGO parts. Hackers on HN object that the LEGO part manufacturing process is not included, you can't make your own parts, etc. But it's LEGO. You can pull apart the model, see how it is constructed, add your own refinements and features, or even redo it from the ground up. In a practical sense having knowledge about the factory making the parts doesn't really matter here.

https://en.wikipedia.org/wiki/Ablation_(artificial_intellige...

Put in the work. This is akin to asking how to remove Rust from a Rust project; just because something is legally available to you doesn't mean you wont need to apply dome elbow grease, depending how deep the changes you want are, ablation, fine-tuning, or distillation are tools you can use to remove "censorship"

Open source means you reveal how you created this binary.

you need to "literally" go read the definition of open source software or even ask an LLM to define it for you. Weights + inference code are not the source code they're more like the compiled binary. Making modifications to the behavior of a model with additional training is like writing a mod for minecraft. Sure, you can change things but it doesn't make it open source.

Calling these models "open source" is an old trap that software companies use to use. Free to download but then, once you're fully comitted, the trap snaps shut and you must pay up to continue.

The actual problem is that we know nothing about the training set of any open-weights model. They could be intentionally biased to influence users, from political censorship to brand advertising, or general shaping of cultural norms. You run the model on own hardware not knowing if it is designed to act against you. Having whole chain open source would allow audit and reproducing the results.

Is it just the supplementary data/code for how they were trained, not just the final product?

I don't mind open-weight models, but they are not open source. It's like bringing home a dog from the rescue and just hoping that it doesn't have a history of biting kids in the face. You just can't know, because you don't know the full history. You can try to add new training (fine tune) to tell it not to bite kids, but that's it.

That said, I've noticed the training procedures and corpus size of more useful open/available weights models are settling down more than I expected. Wonder if crowd sourcing good training data, even if it's just expensive model coding session transcripts, has potential to level the landscape some.

they discovered a great way to destroy their own stickyness and make ppl build generic ai solutions.

    Model       Input  Cached input  Cache writes  Output
    gpt-5.6-sol $4.00  $0.40         $5.00         $20.00
    gpt-5.6-terra
                $2.00  $0.20         $2.50         $12.00
    gpt-5.6-luna
                $0.20  $0.02         $0.25         $1.20

Why is large better than medium to the average end user of ChatGPT though?

I don’t think there’s a way to name these things that will satisfy everyone.

My brain's initial conception of the concepts was earth-relative, so I mapped it as:

Sol = big, it's the sun Luna = medium, in-between sun and earth, space Terra = small, terrestrial

But alas

The problem becomes when you add in the adjustable reasoning efforts and you end up with {model, reasoning_effort} combinations that end up completely obviating particular model classes altogether for at least some percentage of queries; e.g. with GPT 5.6 the price/performance Pareto frontier is dominated by permutations of either Luna and Sol, with Terra nowhere to be seen (but then if you need "large model smells" that aren't captured by your benchmark you can't even rely on this, as a model like Luna simply isn't capable of encoding sufficient world knowledge in its weights to perform certain tasks at any reasoning level but you might be able to get away with Terra on low reasoning, but no one seems to be covering this for some reason).

Calling something "small" might make it sound inferior to competitors. And S/M/L gets awkward as soon as you have more than three sizes.

This naming system can get near-infinitely bigger or smaller.

The people who give them the money are greedy, and hopefully in for a rude awakening. Starting from Nvidia's vendor financing which has a very direct benefit to them, through to every company and oligarch investing into data centres in the hopes of being one of the ones left capitalizing on capturing the livelihoods of the majority of what remains of the "middle class".

It's either hopium or a truly horrific dystopia. Something's going to have to give.

aside from the obvious IP theft problem, it's probably most dangerous for Chinese users outside China to use the Chinese models.

10 or 15 years ago if one had asked me to envision a future where a private company invents artificial intelligence, I'd have thought for sure they'd have a massive moat, be very difficult to catch, and it would create an almost instant monopoly.

Rather, it seems that selling intelligence might end up as a race to the bottom.

Who woulda thought that just having access to enough textual inputs and outputs and a vaugely similar transformer architecture would be enough to copy-cat rather useful intelligence.

Having a dude doing translations has always been useful even if they didn't understand the subject matter. Happened all the time pre LLMs

The economy he and his ilk want to build is infinitely worse.

If investors start fleeing from senseless businesses in the AI sector, that does not mean that sensible businesses will be spared. These things follow herd mentality, and the primary drivers of the herd are greed and fear, not fundamentals or business logic.

A major correction would be a bummer but we were never entitled to these abnormal gains in the first place.

China or SpaceX seem like the 2 likely candidates in 5 years, but who knows.

If (a) demand for AI continues to increase, and (b) SpaceX can get to ~$100/kg to orbit, then they will have a ridiculously deep moat. Probably more like 10 years, though.

But as you said, who knows.

If it doesn't work out, I think China's exponential terrestrial energy deployment will eventually give them the lead, IF they can get enough chips. Another big if.

You can put AI chips in datacenters in the desert for far less than $100/kg. With lots of solar power available, the option to easily access your hardware and far less radiation issues.

The datacenter in space story really only exists to make it possible for Musk to sell X to SpaceX and make more money from the IPO. That's all. There is no engineering reason.

Cooling is probably the easiest problem to solve, easier than power. And in both cases, the problem is solved by mass to orbit. All you need for cooling is a big f-ing radiator. Solar panels are chips, and not trivial to manufacture. But a radiator is just a hunk of metal with some pipes.

That's why the cost of mass to orbit is the most important thing. You can solve almost any space problem by just throwing more mass at it.