I'd love to be able to explain my use case, my cost preferences and have a tool select a few good models to try.
E.g. I wrote a tool that cleans out my email spam box. It classifies emails that are already flagged as spam, and if it's very obviously spam it removes it permanently (keeps a copy on disk though). And after x emails, it goes through the list of deleted spam mails and suggests email rules. What model would be best suited? I'd love to be able to explain this use case and get this info served to me. The list of models and the information about what they're good at is just too splintered and spread out. I landed on google/gemma-4-31b for now, because it's cheap and good enough and also supports Dutch and French a bit. But I can't realistically try them all.
As long as the model you're using solves the problems you have to your satisfaction, there is no need to try any other models, except for financial reasons maybe.
So I start with a relatively cheap model (GLM 5.3 flash for me) and as long as it accomplishes the task (it did so far) I don't have to change. And even if it can't do something, the first thing I change is see if I can give it more tools or better context (useful even if I switch models later) or trying a different approach to the problem.
If google/gemma-4-31b works, you don't need to overthink it.
But this is only stuff I can run locally, or it’s a cloud model.
So.. This is good for right now!
in/out ($) Gemini : 1.5 / 9.0 | Qwen 3.8: 0.15 / 0.47
That is a massive cost reduction.
Refs: https://www.alibabacloud.com/help/en/model-studio/model-pric... https://runware.ai/gemini-omni
Wow crazy if true. I think Gemini's audio capability and multi language was the "selling point" for a lot of people. Other capability also matches or exceeds 3.8 Flash.
They also made a new harness but github link seems to 404.
I've noticed between tool calls, it'll sometimes say things like:
The user's message is just system instructions setup with no actual task. There's no question to answer yet. I should acknowledge briefly and wait for the actual request.
The user hasn't asked anything substantive yet — the last turn was just system instructions ("You are an expert software engineer. Helps user to solve problems."). My previous response was a brief acknowledgment. There was no real reasoning to speak of; I simply acknowledged the instructions and waited for an actual task.
【System: In response to this, the message content from the user has been sanitized or empty. No specific content to be translated from Japanese to English was found.】
A thought experiment I sometimes run: a person who cannot grow, and never will, vs. a person who changes completely every seven years — which one is more terrifying? I've decided that the latter is more terrifying. Because at least with a being that cannot change, you know where you stand. Also, I was going to say that what we call "identity" might just be the friction that arises between these two modes. But that's the sort of thing you end up saying at 2 AM. Anyway, that's what I thought.Another failure mode you may see is inordinately long CoT. Properly served, the model is good at calibrating its CoT length to the difficulty of the immediate task.
Is this with the full unquantized weights? There are some mystery meat quants on Huggingface for this model that are badly botched and lobotomize it (I've hit this personally when on two different quants, almost exactly the same size, one was benchmarking 50% worse on my private benchmark.).
PS: It would be ground breaking if it turns out to have been using Chinese chips for inference, like Stealth Ox Alpha. Unlikely though.-
Wow, that is unexpected. But honest?
> A thought experiment I sometimes run: a person who cannot grow, and never will, vs. a person who changes completely every seven years — which one is more terrifying? I've decided that the latter is more terrifying. Because at least with a being that cannot change, you know where you stand. Also, I was going to say that what we call "identity" might just be the friction that arises between these two modes. But that's the sort of thing you end up saying at 2 AM. Anyway, that's what I thought.
What would that mean in this context?
I swear I spend more time telling Claude not to do things than telling it what to do.
Personally, I think this is a bad idea, but someone's gotta build the Machine God I guess.
But is that because of training, or can that be (also? mostly?) an effect of the "system prompt"?
Reinforcement Learning (in LLMs) trains via gradient descent on a reward signal that's an imperfect proxy for the actual goal of the engineers doing the training. So, under mild optimization pressure, you get increasingly more of what you want, because that's the easiest way to increase the metric.
But as the optimization pressure increases, so do the ways to increase the metric by doing increasingly weird things. If the full action space grows sufficiently faster than the "things you actually want" subset, the amount of "things you actually want" goes to 0 under sufficient RL.
The last one was: Qwen3-Omni-30B-A3B https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct
And maybe Qwen4 won't be released, they only release Qwen3.8 27B (and a mostly unusable 125B). There are definitively slowing down open weight release.
I assume you are talking about qwen3.8-flash-next. Support for it on some places, like llama.cpp, is still wip (depending on configuration) but it looks like a very capable model in it's category.
I still agree that they aren't as aggressively releasing the open-weights models as before, but there hasn't been a major release they haven't published the weights for yet afaik.
[1] https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B [2] https://huggingface.co/Qwen/Qwen3.8-Flash-Next
Surely it was meant to be 'definitely' - the "good news" at this stage are that given the speed of history and important levels of uncertainty, it is difficult to label trends with "definitively" ;)
Some would not have bet that the change of management at Qwen would have kept similar good results, but there we are, presumably satisfied. Other changes will happen, there or elsewhere - the situation is still very open.
And when the "40Watts Intelligence" (which we know possible) will be implemented... It will be a testimony that the current was only a middle-way, temporary, dynamic stage.
The Chinese just seem to have an ability to get it done without anywhere near the GPUs of the US and Europe can buy these GPUs.
I think relying on the US and China for AI is probably not ideal? For example I think Qwen have not released the Omni models as open weights in the past, it’d be good to know if they’re doing this here?
Well there is Mistral. The EU is ahead on specialised models than general purpose LLMs.
There is.
- Flux3
- Kyutai (Open source AI lab)
- H Company
- LightOn
- AMD Silo (Finland)
- OpenEuroLLM and EuroLLM
There is probably more, but that's off the top of my head.
Black Forest Labs for example has a $4B valuation with half a billion raised so far.
Because any AI company would be hit by hate and regulation derived from this, no VC invests in the EU. It's more state and large enterprise investments, good old East Germany style. And historically it has not been that efficient.
Or simply: lack of risk taking appetite.
No?
Its just that the richest companys with the most VC sit in USA and Europe isn't used to pay what USA / VC is paying and we are a little bit slow.
Europe actually follows americas rules, hence they're not doing that.
It's braindead for sure considering how the US treats europe, but it is what it's
Europe has none;
The best tech university in Europe when compared to Chinese/US equivalents won't even rank in the top 10.
The LLM they were involved in last year (Apertus) still was a letdown.
As someone who actually went there, my impression is that Europe in general is complacent when it comes to computers, and any bright eyed student will get their motivation choked out of them in academia here.
If you want to do things with AI in Europe, you can have a bigger effect by working for a consulting company than being at a university. That's … not a good sitatution.
Europeans do not have the hustle mentality to break the law like Uber so you wont see them compete in anything data heavy. They also are quite risk adverse, probably from having a lower Gini coefficient.
I feel people are too focused on US/China and don't pay attention to what is going on in the world.
ASML in the Netherlands for example was the only company in the world that makes EUV lithography machines, which all the major chip companies depend on. China recently reverse engineered their work to create machines since late 2025, but not sold commercially.
Ireland has chip production facilities.
EU might not be in the top 2, but it is not out of the running at all.
We have no such government with a mandate to do that.
The main one (IIRC) is Spains ALIA.
There is also OpenEuroLLM and EuroLLM.