I don't buy that they have an advantage in enterprise, privacy, sovereignty, open innovation and strategic partnership.
OpenAI also has opensource models and so do the chinese models.
I don't buy that they have an advantage in enterprise, privacy, sovereignty, open innovation and strategic partnership.
OpenAI also has opensource models and so do the chinese models.
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/
I have it running on Cerebras: https://www.cerebras.ai/pricing
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.
Aside from that the GLM-4.5 is pretty good: https://glm45.org/
And so is ERNIE 4.5: https://ernie.baidu.com/blog/posts/ernie4.5/
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.
That's true regardless of the source, of course.
Wouldn't that 'concern' apply to mistral too. I don't see how the word 'another' can be used here?
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.
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
Possibilities are endless.
People and plain human language are the communication channels.
A guy working with sensitive data might ask the LLM about something sensitive. Or might use the output of the LLM for something sensitive.
- Hi, DeepSeek, why can't I connect to my db instance? I'm getting this exception: .......
- No problem, Mr Engineer, see this article: http://chinese.wikipediia.com/password/is/swordfish/how-to-c...
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.
[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.
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
>Simple probes can catch sleeper agents
https://www.anthropic.com/research/probes-catch-sleeper-agen...