alt Hacker NewsQuasar 438B: Europe's Leading AI Model
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I wish for this company to have great models. I am glad to see such good scores.
I see they have a HuggingFace account[0] and they fine-tuned GPT-OSS, Nemotron, and Qwen, in the past under new names.
There are some things about it that make me worry though.
• They don't indicate the active parameter count, or indicate whether they pretrained the model. It could be a MiniMax M3 finetuning, as the parameter count almost matches (435B vs. 438B).
• They mention using quantum algorithms in other projects: https://multiversecomputing.com/singularity despite quantum algorithms not being typically useful currently.
It would not be the first company with a splashy release, like Brampton Intelligence[1], or SubQ[2]. Unlike those, they do seem to have experience fine-tuning models. Regardless of my worries, I am rooting for them to learn how to train models.
[0]: https://huggingface.co/MultiverseComputingCAI
I might be misreading this. But they seem to heavily imply that they trained this model. Or at least want to give off the impression that that is the case. Otherwise the "Europe's Leading Model" line doesn't make much sense.
Their bread and butter is to remove parameters from models [1] and I think this is GLM 5.2 with parameters removed. Its advertised in their changelog [2] as "capabilities are identical to GLM 5.2," it has the same two effort settings "high" and "max," and both are text only [3]. I might be wrong about this, but I would love to hear more about what they did before changing my mind.
Not the greatest fan of the marketing personally. Irrespective of what this model is.
[1]: https://multiversecomputing.com/compactifai/deployment
[2]: https://docs.compactif.ai/changelog/#added-3
[3]: https://docs.compactif.ai/features/multi-modality/#compatibi...
I do not want open weight models from China to be the only viable locally hosted things (deepseek v4 flash 0731 Q8, qwen 3.8-flash-next Q8, GLM-5.3-Flash) in the under 200GB RAM class.
I want to see things like Mistral and Laguna (non-CN) succeed. I have spent about a week using Laguna S 2.1 as a test and while I wasn't blown away by its capabilities, it's also totally acceptable for many purposes.
I do hope these Quasar people learn that if you announce a new model and it already performs worse than things people can go download from huggingface, and/or buy access to with very cheap token plans via openrouter or opencode.. If your new model is API only and people can't download/examine it, it will get very little uptake and real world use.
I can see it as a niche market for european sovereignty stuff if absolutely necessary, hosted and run in Europe, sure. Same as Mistral. That's a niche which exists, there's probably enough room for a couple of modestly sized companies doing it... I guess?
Didn't know the "Highest scoring EU model" has a low bar, lower than Qwen3.8-27B, but still congratulations on the milestone, hopefully next iterations will get better from here
When the weights are closed I don't believe any benchmark.
I just got Qwen3.8-27B to score extra 10% on SWE Pro by adding a proxy in front of it that has few simple "harness like features": - when the model gets stuck it tells it to "go on" - when it sends no output, malformed json, slips to wrong tool use format, etc it asks it to "try again better" - detects repetition and tells the model. - injects a prompt about "planning tool use" when it seems to be using same tools repeatedly. - injects a reminder it can use tools if there are no tool uses for over X messages.
10% - with just that.
I have more to test. My point is, open weights models get tested on naked model quality. "Frontier" models get tested as a model + whatever secret sauce they choose to put in front.
As a European: I don’t care where an open weight model comes from.
It is beyond me how they managed to acquire such funding. Sounds a lot like an earlier quantum computing effort which pivoted to "AI" as well: https://zapataquantum.com
What I want is a model that is trained with data that is openly available, where the data is curated by academia. I don't want corporate crap in my AI (unless it has been filtered properly).
As a European, or in general, this make me happy. Since the more diversity the better. Tho it’s hard not to not to think of this as a big fish in a small pond situation (when talking about best model in EU).
Multiverse Computing is one of the weirdest companies I've encountered here in Europe/Spain. Their product / job application descriptions are just technobabble.
Despite my doubts I applied to one of their positions a couple of years back only to receive a super late and generic "we are not moving forward" mail (I'd say I fit pretty damn well for the position, but it seems to be the new normal that most companies don't even want to chat with people 99% of the time).
This has to be fake... or similar.
Their CMO's bio lists "20,000 'qualified' quantum AI contacts on linkedin" as the SECOND line in his bio.
"Business Management by ESADE. Co-Founder and CMO of Multiverse Computing. President, “barcelonaqbit-bqb”, 20,000 “qualified” quantum AI contacts on LinkedIn. VP of the AMETIC Innovation..."
> Quasar is not only intelligent, it is also fast. It returns 500 tokens, thinking time included, in 15.3 seconds.
Seems to be worded a bit strange, is "thinking time" referring to prompt processing or something? Otherwise "reasoning/thinking" is typically part of the returned tokens, at least for most non-OpenAI/non-Anthropic platforms, so you can see the actual reasoning. But here it seems either they word this weirdly, or "thinking" is somehow separate from the actual chat completion request?
It's amazing to see Qwen3.8-27B occupying a respectable central place among the giants.
The leadership team of that company is outsized and bizarre.
Is this a compressed and retrained version of GLM-5.2?
Are the weights public? I'm not seeing them
I am still very surprised how absent India is from the LLM game.
Any hope of an integration to Openrouter ?
How can it be that a 438B model is worse than Qwen3.8-27B? Are these benchmarks totally gamed?
So "Europe's Leading AI Model" is just a modified Chinese model which does worse than the better Chinese models?
It seems Europe is many years behind in tech. Maybe for AI models they are just a decade behind, but as far as producing hardware capable to run SOA models they lag tens of years.
So, slower and worse than Gemini 3.7 (high)?
I don't get what the issue is. Chinese labs fully publish how they make great models. Architecture is known, training methods are often very open. Why Europe just can't copy what they do?
> Unlocking the Quantum AI Software Revolution
I'm sorry, what?
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The company sounds like a bunch of hot air to me. From their about page:
> At the heart of Multiverse's platform is CompactifAI, a compression technology that applies tensor networks, a mathematical framework from quantum physics, to the problem of AI model compression. This application was pioneered by co-founder and Chief Scientific Officer Dr. Román Orús and reduces the size of large language models by up to 80-95% with immaterial accuracy loss.
Ironic, considering they are releasing a 438B model that loses to a 27B one. From another part:
> Singularity Machine Learning is a cloud service that uses quantum machine learning for solving supervised learning problems.
I wouldn't be surprised if these guys just finetuned an open Chinese model and called it a day.