Plus, a lot of IP from a previous startup (that was acquired last year), Pi Labs, was used to help push the model to SOTA performance quickly. The startup was building scoring models long before Jev released

My biggest learning after some experiments - a BF16 (unquantized) Qwen beats a Q8 of double its size for decisions. I guess that’s the reason Kev switched to 4B BF16, from the original 8B version. Isn’t it interesting that quantization seems to mess with decision accuracy?

Surprised they aren't doing these sorts of one-off models with Microsoft Phi, which is intentionally smaller, but there's no reason Microsoft couldn't try to make a slightly larger Phi model with more capabilities...

> Great to see open weights (if not open source)

The insistence of naming it open weights as opposed to open source is getting ridiculous, and it's both irrelevant (i.e. no one cares in practice) and factually incorrect.

Weights are source in language models. Apache defines source as ""Source" form shall mean the preferred form for making modifications". That is precisely what's happening here. Everyone is using the preferred form for making modifications to these models (including the model creators themselves). A model is "created" at init time, and then "trained" by modifying the weights.

All these models are open source. What's not open sourced (with qwen et all) is the training code. So open source model, no training code. And that's ok. There are labs that release those as well. Apertus and Olmo series come with open source models, open source training and open datasets. Nemotron comes with open source models, open source training and some open datasets, while others are not published. And that's ok too.

The fact that you see all these models being modified (from AR completion models to "decision models") and re-released should be all the proof you need. That's what a license offers you. The right to inspect, run, modify and re-release a model. A license cannot (and never did) give you any other rights. OpEnWeIgHtS is silly.

I hope they can finally make my "Copilot+ PC" infer things locally that are actually useful. Phi Silica for Advanced Paste was a good start, if a bit late. If they got their act together, Microsoft-Decision-1 could have some local potential. Their track record leaves me with some reservations.

Those local APIs already exist. It is called Microsoft Foundry Local: https://learn.microsoft.com/en-us/azure/foundry-local/get-st...

Supports GPU, NPU and CPU.

That’s where Apple is moving to as well. The models doing the implementation work need not be better than opus 4.6. And locally available hardware to run this already exists and likely will be sub 2k of 2026 dollars in a few years time.

Yeah, because they are desperate to try and justify the investments into Copilot and the NPUs they pushed OEMs into integrating. I'm all for competition, but every one of MS's AI models have just been nothingburgers or relabels of other lab's models. Even their novel high cardinality models are just novelties.

Honestly, I'm glad that people later to the AI game are exploring niches other than state-of-the-art "smartest" models – I'd love AI applications that tackle the small hassles in life.

Actually, it is price competitive! As of now, $0.042/1M input tokens - the same as Jev as far as I'm aware. Checkout the blog. https://techcommunity.microsoft.com/blog/azure-ai-foundry-bl...

They say they are only benchmarking public models in the blog.

Also, section 2.3: https://typesafe.ai/legal/mca

There isn't much choice, I am afraid. It is Chinese models or Gemma or Llama? (I am skipping a few lesser known ones.)

Docs are up now here: https://learn.microsoft.com/en-us/azure/foundry/foundry-mode...