This model is specifically trained on this task and significantly[1] underperforms opus.
Opus costs about 6x more.
Which seems... totally worth it based on the task at hand.
[1]: based on the total spread of tested models
That would also help to reduce our dependency on American Hyperscalers, which is much needed given how untrustworthy the US is right now. (And also hostile towards Europe as their new security strategy lays out)
The AI Act absolutely befuddled me. How could you release relatively strict regulation for a technology that isn't really being used yet and is in the early stages of development? How did they not foresee this kneecapping AI investment and development in Europe? If I were a tinfoil hat wearer I'd probably say that this was intentional sabotage, because this was such an obvious consequence.
Mistral is great, but they haven't kept up with Qwen (at least with Mistral Small 4). Leanstral seems interesting, so we'll have to see how it does.
Speaking as someone who's been doing stats and ML for a while now, the AI act is pretty good. The compliance burden falls mostly on the companies big enough to handle it.
The foundation model parts are stupid though.
It's not an excuse. Anybody with half a working brain should've been able to tell that this was going to happen. You can't regulate a field in its infancy and expect it to ever function.
>The compliance burden falls mostly on the companies big enough to handle it.
You mean it falls on anyone that tries to compete with a model. There's a random 10^25 FLOPS compute rule in there. The B300 does 2500-3750 TFLOPS at fp16. 200 of these can hit that compute number in 6 months, which means that in a few years time pretty much every model is going to hit that.
And if somebody figures out fp8 training then it would only take 10 of these GPUs to hit it in 6 months.
The copyright rule and having to disclose what was trained on also means that it will be impossible to have enough training data for an EU model. And this even applies to people that make the model free and open weights.
I don't see how it is possible for any European AI model to compete. Even if these restrictions were lifted it would still push away investors because of the increased risk of stupid regulation.
As I said, the core of the AI act was written about supervised ML, not generative ML, as generative ML wasn't as big a deal pre Chat GPT.
> You mean it falls on anyone that tries to compete with a model. There's a random 10^25 FLOPS compute rule in there. The B300 does 2500-3750 TFLOPS at fp16. 200 of these can hit that compute number in 6 months, which means that in a few years time pretty much every model is going to hit that.
As I also said, the foundation model stuff (including this flops thing) is incredibly stupid. I agree with you on this, but my point is that the core of the AI act was supposed to cover the ML systems built since approx 2010.
> The copyright rule and having to disclose what was trained on also means that it will be impossible to have enough training data for an EU model. And this even applies to people that make the model free and open weights.
Again, you're talking about generative stuff (makes sense given the absurdly misleading name now) whereas I'm talking about the original AI act, which I read well before ChatGPT happened.
The training data thing is a tradeoff, like copyright is far too invasive (IMO) and it's good to be able to use this information for other purposes. However, I personally would be super worried about an ML team that couldn't tell me what data went into their model. Like, the data is core to all ML/AI approaches so that lack of understanding would make me very sceptical of any performance claims.
Lets be real, the AI companies don't want to say what's in their models because of the rampant copyright infringement, not because of any technical incapability.
Most Copilot customers use Copilot because Microsoft has been able to pinky promise some level of control for their sensitive data. That's why many don't get to use Claude or Codex or Mistral directly at work and instead are forced through their lobotomised Copilot flavours.
Remember, as of yet, companies haven't been able to actually measure the value of LLMs ... so it's all in the hands of Legal to choose which models you can use based on marketing and big words.
Still, the more interesting comparison would be against something such as Codex.
I think it would still be fine for the legs and on battery for relatively short loads: https://www.notebookcheck.net/Apple-MacBook-Pro-M5-2025-revi...
But 40 degrees and 30W of heat is a bit more than comfortable if you run the agent continuously.
Most people I know that use agents for building software and tried to switch to local development, every single time they switch back to Claude/codex.
It's just not worth it. The models are that much better and continue to get released / improve.
And it's much cheaper unless you're doing like 24/7 stuff.
Even on the $200/m plan, that's cheaper than buying a $3k dgx or $5k m4 max with enough ram.
Not to mention you can no longer use your laptop as a laptop as the power draw drains it - you'd need to host separately and connect
I understand the value proposition of the frontier cloud models, but we're not as far off from self-hosting as you think, and it's becoming more viable for domain-specific models.