The GPT-OSS-120B release was pretty decent and you could run it on vLLM, Ollama and a bunch of other stuff on day one, despite MXFP4, are you not entertained? I mean, it's even close to GPT-5 mini in some benchmarks: https://llm-stats.com/
As for the Chinese models, yes, there are quite a few good ones.
For programming and development, my current daily driver is the Qwen3 Coder 480B model: https://qwen3lm.com/
Personally I think Claude still has the best results, but Qwen3 is loosely in the same ballpark and Cerebras inference is measured in thousands of tokens per second, in addition to giving me 24M tokens per day for 50 bucks a month in total. That was enough to get me to switch over.
Either way, happy to see what the future holds for Mistral, it's cool to have EU options too! Either way, more competition prevents complacency and stagnation, and should be a good thing for everyone.
What's "serious" exactly? Codex is open source, is software, can be run with open/downloadable models/weights.
In my testing using Gemini, Claude Code, Codex, Qwen Code and AMP side-by-side for every prompt for the last two weeks, Codex seems the best of all of them so far.
Yeah, I initially thought so too, but since they used "models" later, I assumed they knew the difference and really meant "software".
> recent GPT-OSS is not competitive with other open weights models
Yeah, heard that a lot from people who haven't run GPT-OSS themselves too, but as someone who been playing with it since launch, and compared it to the alternatives since then, saying it isn't even competitive is a serious signal they don't know what they're talking about.
There are concerns besides spying if you really don't trust the source of an open model. One is that the training incorporates a bias (added data or data omission) that might not be immediately apparent but can affect you in a critical situation. Another is vendor lock-in, if you end up depending on specifics of the model that make it harder to swap later.
It goes for all models though if you are looking at the values argument that original commenter made -- western values are probably more aligned than authoritarian governments - even if you do have your concerns about western companies. At least thats my read on the situation.
yeah, but try to convince a board or legal about it for a company that is not software first, for that they have to understand how it works. we have "chinese" AI blocked at work, even through i use self hosted models for myself at home hacking on my own stuff.
Good luck convincing others of this. I know it's true, you know it's true, but I've met plenty of otherwise reasonable people who just wouldn't listen to any arguments, they already knew better.
It's theoretically possible that your model will work OK except for code generation for security-relevant applications it will introduce subtle pre-designed bugs. Or if used for screening CVs it will prioritize PRC agents through some keyword in hobbies. Or it could promise a bribe to an office worker when asked about some critical infastructure :)
Sending data back could be as simple as responding with embedded image urls that reference external server.
You are totally right EU commissioner, Http://chinese.imgdb.com/password/to/eu/grid/is/swordfish/funnycat.png
Of course theoretically lots of things are possible with probabilistic systems. There is no difference with open source, openweight, chinese, french or american llms. You dont give unfettered web access to any models (locally served or otherwise) that can consume critical company data. The risk is unacceptable, even if the models are from trusted providers. If you use markdown to see formatted text that may contain critical data and your reader connects to the web, you have a serious security hole, unrelated to the risks of the LLM.
Of course, you want to limit that with training and proper procedures. But one of the obvious precautions is to use a service designed and controlled by a trusted partner.
Having the local LLM process sensitive data is a desirable usecase and more trustworthy than using a “trusted partner” [0]. As long as your LLM tooling does not exit your own premises, you can be technically safe. But yes, dont then click at random links. Maybe it is generally safer to not trust the origin of the local LLM, because it reduces the chance of mistakes of this type ;-)
[0] Trust is a complicated concept and I took poetic license to be brief. It is hard to verify the full tooling pipeline, and it would be great if indeed there existed mathematically verifiable “trusted partners”. A large company with enough paranoia can bring the expertise in house. A startup will rely on common public tooling and their own security reviews. I dont think it is wise to share the deepest darkest secrets with ourside entities, because the potential liability could destroy a company, whereas a local system, disconnected from the web, is technically within the circle of trust. Think of a finance company with a long term strategy that hasnt unfolded yet, a hardware company designing new chips, a pharma company and their lead molecules prior to patent submission, any company that has found the secret sauce to succeed where others failed—-none of these should be using trusted partners in favor of local LLM from untrusted origins IMHO. Perhaps the best of both worlds is to locally deploy models from trusted origins and have the ability to finetune their weights, but the practical processing gap between current chinese and non-chinese models is notable.
Maybe it can not spy on you but models can be totally (e.g. politically) biased depending on the country of origin. Try to ask european-, us- or china-trained models about "Tiananmen Massacre" and compare the answers. Or consider Trump's recent decisions to get rid of "woke" AI models.
Classic problem: "Who do you love more: mum or dad?" ;) Surely it's naive thinking but as the EU citizen I feel like I've got a little more influence on "European censorship" than on any other. I suppose that ASML feels the same way
