We as many other’s were curious to try and benchmark it.

On that note, as a small gesture of support, we’ve hosted and made Kolibri-1 free for anyone to try for the next few days.

No GPU. No setup. Just try it. tesseracted.com/kolibri-1-chat/

https://x.com/konarkmodi/status/2106373678589960260?s=46

We trained Kolibri with abstention data and with our Merlin-Arthur protocol. As a result, it is trained to say "I don't know" when the answer isn't in the context.

https://aleph-alpha.com/en/blog/bounding-hallucinations-merl...

disclaimer: I‘m part of the training team, happy to answer any questions

And that is a good thing - no need to hide it. Given the growing cost of keeping up, these few non-US, non-Chinese companies really need to do more sharing of efforts and costs.

Canada too is very much in need of sovereign AI options, but funding that on its own would be pretty much a waste of money. Would love to see this new German Canadian company cooperate with Mistral too, or maybe one of the Korean AI companies.

Also, once the Cohere takeover is complete will they still be able to use this "sovereign" claim despite being 90% owned and 100% operated out of Toronto?

Right now one could run an open model for most government applications and it would be good enough, you just cannot trust any of these.

So having a sovereign controlled model audit the first one would basically act like a “trust adapter”.

If the second model is cheap and fast enough, there is a business model.

You don’t even need to audit all the intermediate steps, just tool calls and end results.

intellectual-property safety

My suspicion is that you simply can't build an even slightly competitive model without liberally stealing your training data, in 2026, as much as I'd like it to be otherwise. You can get to the point that I suspect most of the frontier labs are at, where you've laundered the initially stolen data through the creation of huge amounts of derivative synthetic data, but still. Anyone who isn't comfortable stealing their training data is bringing a knife to a gun fight, and is going to die a noble but inevitable death.

I get that doesn't invalidate the real "point" of the model, but...

Its speed is pretty good on the other hand with only 3B active parameters I am getting around 170 tkn/s on fp8

Aleph Alpha Kolibri: How the sovereign German LLM works - https://smackernews.com/item/49943034 HN - Oct 2026 (162 comments)

Reiterating what I said yesterday, I think what most people did miss here is that coding is probably not the main goal of this model. It can also code, but the real target is the business processes that aren't code.

Think for example a support department, where new cases (in german!) should be automatically processed (or automatically draft processed) based on existing cases, without pumping all that data through a cloud api.

Or being a general sparring thing for non-IT workers.

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If you want to code, you already operate in english, so there's the existing and better coding models for you.

But if you're some regular worker somewhere, you might just want to ask the thing to retrieve data on this specific project done three years ago and go through the shared email folder relating to it.

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You can see that that might be the target, given that they've explicitly focused on getting the model to say "I don't know" instead of just making stuff up. That is exactly the capability you need for these kinds of use-cases.

You can also see that in how it is optimized for speed and a fairly small KV footprint. The idea likely being that a business can pay like a few hundred euros once per worker and then have this kind of local capability for a team of 30.

I wonder how far we are from this. How far are we from LLM's Debian moment?

Good to see Europe adding toe what Mistral is doing. +100

the right balance between [...] environmental constraints and energy requirements

trained it on infrastructure in Germany

Made me chuckle. About the second worst place in Europe to train a model if you really care about environmental constraints and energy requirements.

https://app.electricitymaps.com/

Good initiative on the sovereignty front though.

More specifically, I asked the model about what I should put on my contact page, which can cost you in the order of 500 € in Germany if you don't write the right magic words.

Kolibri incorrectly referenced the "Telemediengesetz" ("telecommunication act"), which has been superseded by the "Digitale-Dienste-Gesetz" (DDG, "digital services act") since 2024. The model knows about the DDG, but does not reference it unless specifically instructed to do so.

If anyone of the developers reads this, you can fix this by introducing a recency bias during training. You can even control it by conditioning the model on a date provided with the system prompt or first prompt, so you can travel in time.

If they can follow Qwen3.8-Flash-Next, the could draft off the huge reduction in VRAM requirements.

I’ll be curious to see which it becomes in the next year - nba “very narrow record”, or a sign you can hang with the big boys.

This thing is worse than a Qwen3.8 27B.

The second was to rephrase German documents we already had. An LLM rewrites an organic German document in the style of an encyclopedia entry, a Q&A dialogue or a text passage, preserving its content.

"an LLM" -- does that mean they are effectively learning from that LLM the German encyclopedic style? makes me wonder which LLM and how that is really sovereign.

3B active

built for sovereign mission-critical work in regulated areas including public administration, industrials and aerospace

Cool to be doing more independent model lineages, but I sure hope no one actually uses this as part of any aerospace engineering...

A bigger dense model beats it. Qwen3.8 27B ...

How is a 27B dense model bigger than a 78B MoE?

Model Training as Code - https://smackernews.com/item/48673450 HN - June 2026 (24 comments)

Congrats to the team.

But anyway. I think the main oversight when dismissing this is that not every use-case is coding a SV-style startup app. That market is quite saturated, so it would make sense to create something locally for the use-cases currently underserved by LLMs.

We will probably learn more about what this can really do once quants become available that can be run by people without an SV salary (and the biases that come with that).

It knows less from memory, Multi-turn tool calling is weaker, It’s not the best coding agent

Then what does it good at? Sending faxes?

4. It thinks in German

This means that it’s always on time, it uses acronyms for everything and when there’s a decision to be made, it sets up a committee.