Notable: they open-sourced the weights under Apache 2.0, unlike OpenAI and DeepMind whose IMO gold models are still proprietary.
Previous discussion: https://news.ycombinator.com/item?id=46072786 218 points 3 days ago, 48 comments
Ah, missed that one. Thanks for the link.
It's impressive to see how fast open-weights models are catching up in specialized domains like math and reasoning. I'm curious if anyone has tested this model for complex logic tasks in coding? Sometimes strong math performance correlates well with debugging or algorithm generation.
It makes complete sense to me: highly-specific models don't have much commercial value, and at-scale llm training favours generalism.
kimi-k2 is pretty decent at coding but it’s nowhere near the SOTA models of Anthropic/OpenAI/Google.
Are you referring to the new reasoning version of Kimi K2?
Shouldn’t there be a lot of skepticism here?
All the problems they claim to have solved are on are the Internet and they explicitly say they crawled them. They do not mention doing any benchmark decontamination or excluding 2024/2025 competition problems from training.
IIRC correctly OpenAI/Google did not have access to the 2025 problems before testing their experimental math models.
Why isn’t OpenAI’s gold medal-winning model available to the public yet?
'coz it was for advertisement. They'll roll their lessons into the next general purpose model.
Does anyone know if this will become available on OpenRouter?
A bit important that this model is not general purpose whereas the ones Google and OpenAI used were general purpose.
Both OpenAI and Google used models made specifically for the task, not their general-purpose products.
OpenAI: https://xcancel.com/alexwei_/status/1946477756738629827#m "we are releasing GPT-5 soon, and we’re excited for you to try it. But just to be clear: the IMO gold LLM is an experimental research model. We don’t plan to release anything with this level of math capability for several months."
DeepMind: https://deepmind.google/blog/advanced-version-of-gemini-with... "we additionally trained this version of Gemini on novel reinforcement learning techniques that can leverage more multi-step reasoning, problem-solving and theorem-proving data. We also provided Gemini with access to a curated corpus of high-quality solutions to mathematics problems, and added some general hints and tips on how to approach IMO problems to its instructions."
https://x.com/sama/status/1946569252296929727
>we achieved gold medal level performance on the 2025 IMO competition with a general-purpose reasoning system! to emphasize, this is an LLM doing math and not a specific formal math system; it is part of our main push towards general intelligence.
asterisks mine
DeepSeekMath-V2 is also an LLM doing math and not a specific formal math system. What interpretation of "general purpose" were you using where one of them is "general purpose" and the other isn't?
How do you know how well OpenAI's unreleased experimental model does on biology or history questions?
Not true
Do note that that is a different model. The one we are talking about here, DeepSeekMath-V2, is indeed overcooked with math RL. It's so eager to solve math problems, that it even comes up with random ones if you prompt it with "Hello".
That's a different model: https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale
Oh you may be correct. Are these models general purpose or fine tuned for mathematics?
How do you run this kind of a model at home? On a CPU on a machine that has about 1TB of RAM?
Wow, it's 690GB of downloaded data, so yeah, 1TB sounds about right. Not even my two Strix Halo machines paired can do this, damn.
You can do it slowly with ik_llama.cpp, lots of RAM, and one good GPU. Also regular llama.cpp, but the ik fork has some enhancements that make this sort of thing more tolerable.
Two 512GB Mac Studios connected with thunderbolt 5.
How is OpenAI going to be able to serve ads in chatgpt without everyone immediately jumping ship to another model?
I suppose the hope is that they don’t, and we wind up with commodity frontier models from multiple providers at market rates.
I don't care about OpenAI even if they don't serve ads.
I can't trust any of their output until they become honest enough to change their name to CloseAI.
ChatGPT is a website. There's nothing unusual about ads on a website.
People use Instagram too.
The same way people stayed on Google despite DuckDuckGo existing.
by having datacenters with GPUs and API everyone uses.
So they are either earning money directly or on the API calls.
Now, competition can come and compete on that, but they will probably still be the first choice for foreseeable future
Google served ads for decades and no one ever jumped ship to another search engine.
Because Google gave the best results for a long time.
and now, when they are not, everyone else's results are also pretty terrible...
They pay $30bn (more than OpenAIs lifetime revenue) each year to make sure noone does.
What are you referring to?
That got outlawed in Europe due to anti-trust lawsuits. I don't feel like it changed Google's market share here.
Don't they distill directly off OpenAI/Google outputs?
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I think we should treat copyright for the weights the same way the AI companies treat source material ;)
We don't even have to do that: weights being entirely machine generated without human intervention, they are likely not copyrightable in the first place.
In fact, we should collectively refuse to abide to these fantasy license before weight copyrightability gets created out of thin air because it's been commonplace for long enough.
There's an argument by which machine-learned neural network weights are a lossy compression of (as well as a smooth interpolator over) the training set.
An mp3 file is also a machine-generated lossy compression of a cd-quality .wav file, but it's clearly copyrightable.
To that extent, the main difference between a neural network and an .mp3 is that the mp3 compression cannot be used to interpolate between two copyrighted works to output something in the middle. This is, on the other hand, perhaps the most common use case for genAI, and it's actually tricky to get it to not output something "in the middle" (but also not impossible).
I think the copyright argument could really go either way here.
Right, the .mp3 is machine generated but on a creatively -generated input. The analogy I'm making is that an LLM's weights (or let's say, a diffusion image model) are also machine-generated (by the training process) from the works in its training set, many of which are creative works, and the neural network encodes those creative works much like mp3 file does.
In this analogy, distributing the weights would be akin to distributing an mp3, and offering a genAI service, like charGPT inference or a stable diffusion API, would be akin to broadcasting.
Of course we should! And everyone who says otherwise must be delusional or sort of a gaslighter, as this whole "innovation" (or remix (or comopression)) is enabled by the creative value of the source product. Given AI companies never ever respected this copyright, we should give them similar treatment.
If they open source just weights and not the training code and data, then it’s still proprietary.
It's just open weights, the source has no place in this expression
Yeah but you can distill
You can distill closed weights models as well. (Just not logit-distillation)
Though it violates their terms of service
Is that the equivalent of decompile?
No, that is the equivalent of lossy compression.
Isn't that a bit like saying that if I open source a tool, but not a full compendium of all the code that I had read, which led me to develop it, then it's not really open source?
No its like releasing a binary. I can hook into it and its API and make it do other things. But I can't rebuild it from scratch.
If you distribute a binary to someone, with gpl2, you should also, if asked provide the source code used to _build_ that binary. Other licenses will differ. MIT for example lets you do pretty much anything, so long as you keep the MIT license and attribution public.
But when people are talking about open source, they generally mean "oh I can see the source code and build it my self." rather than freeware which is "I can run the binary and not have to pay"
ok but just the model isn't even close to anything open, it's literally a compiled binary, without even the source data
"open source" as a verb is doing too much work here. are you proposing to release the human readable code or the object/machine code?
if it's the latter, it's not the source. it's free as in beer. not freedom.
Yes, I 100% agree. Open Source is a lot more about not paying than about liberty.
This is exactly the tradeoff that we had made in the industry a couple of decades ago. We could have pushed all-in on Stallman's vision and the FSF's definition of Free Software, but we (collectively) decided that it's more important to get the practical benefits of having all these repos up there on GitHub and us not suing each other over copyright infringement. It's absolutely legitimate to say that we made the wrong choice, and I might agree, but a choice was made, and Open Source != Free Software.
https://www.gnu.org/philosophy/open-source-misses-the-point....
No. In that case, you're providing two things, a binary version of your tool, and the tool's source. That tool's source is available to inspect and build their own copy. However, given just the weights, we don't have the source, and can't inspect what alignment went into it. In the case of DeepSeek, we know they had to purposefully cause their model to consider Tiananmen Square something it shouldn't discuss. But without the source used to create the model, we don't know what else is lurking around inside the model.
> In LLMs, the weights are the preferred form of making modifications.
No they aren't. We happen to be able to do things to modify the weights, sure, but why would any lab ever train something from scratch if editing weights was preferred?
No, it's like saying that if you release under Apache license, it's not open source even though it's under an open source license
For something to be open source it needs to have sources released. Sources are the things in the preferred format to be edited. So the code used for training is obviously source (people can edit the training code to change something about the released weights). Also the training data, under the same rationale: people can select which data is used for training to change the weights
Not just semantics, the concept of open source fundamentally depend on what the preferred form of modification is
https://opensource.org/ai/open-source-ai-definition
The difference is that you can customize/debug it or not. You might say that a .EXE can be modified too. But I don't think that's the conventional definition of open source.
I understand that these days, businesses and hobbyists just want to use free LLMs without paying subscriptions for economic motives, that is, either saving money or making money. They don't really care whether the source is truly available or not. They are just end users of a product, not open-source developers by any means.
Is this a troll? They don't want to reproduce your open source code, they want to reproduce the weights.
For obvious reasons, there is no world in which you can "build" this kind of so-called open source project without the data sets. Play around with words all you want.
True. But the headline says open weights.
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you are absolutely right. I'd rather use true closed models, not fake open source ones from China.