Quasar 438B: Europe's top-scoring AI model also outruns rivals in speed
Quasar 438B: Europe's Leading AI Model
Multiverse Computing has released Quasar 438B, its first large reasoning model and the highest-scoring European model on the Artificial Analysis Intelligence Index v4.1.1, with a score of 43. It outperforms Mistral Medium 3.5 (30) and NVIDIA Nemotron 3 Ultra (38), and delivers 500 tokens in 15.3 seconds, faster than most higher-scoring models. Quasar excels at long-context reasoning (AA-LCR 75.0) and agentic coding (Terminal-Bench 69.3), and is available via the CompactifAI API.
Only three models in the comparison are faster, and only one of those, Gemini 3.7 Flash, scores higher on the index.
- fantyoon
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...
- pu_pe
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.
- espadrine
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
- walrus01
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?
- Roark66
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.