Kind of funny how much hype they can all get out of this, but Qwen really is the little engine that could. Great to see open weights (if not open source) driving the whole ecosystem like this though.

Qwen tunes are nice and all but either price or performance makes each example I have tested not viable unless you are able to cheaply self-host and fine-tune further.

Microsoft only compares the price of theirs to GPT Sol(!), not GPT Terra, or GPT Luna (which is what OpenAI's Jev wannabe is based on), and certainly not Jev (4/10 the cost of Luna).

I can't remember when a new product created So many competitors so quickly. What is very clear is that everyone is saying "Doh!", slapping themselves on the forehead, and scrambling to get a slice of this obvious-in-retrospect massive pie.

What no-one appears to have done yet is to come close to Jev on pricing!

Using a decoder model for this comes down to picking the most probable class based on the probability what the next token assigned to a class is. They are all probabilities but semantically mean totally different things. Am I right?

To build Microsoft-Decision-1, we post trained Qwen3.5-9B for fast, single-pass decision scoring and will soon rebase it on other models, including Microsoft AI (MAI) and OpenAI.

I guess the fear of Chinese models is finally subsiding.

I'm behind the times. Outside of Jev which are worth looking at?

Question: “Which team should handle this message?”

Result: “Tech Support (85%)”

Yep, sounds about right.

Microsoft-Decision-1, our model for fast decision-making

I'm still waiting for the Microsoft Vacuum Cleaner. /s