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

chris_money202

Microsoft is doing things differently with AI. It feels to me they are moving into local inference heavily and see a future where Windows has native AI APIs that run locally or optionally in the cloud/edge.

nejch

This is based on one of the smaller Qwen models, just like Cloudflare's Clef, Strands decider, and a plethora of others released in the last couple of weeks. 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.

simonw

> 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.

bflesch

While this looks like a contribution from a capable team trying to impress senior leadership, for me personally the Microsoft brand is so badly tarnished I don't even feel negative emotions any more - just pity.

wkcheng

I don't see any API documentation for this yet. How can someone actually try it? Did they rush this out for hype?

tencentshill

But what about when the government's AI skills amount to: "is this DEI, only answer yes or no"

prometheus1992

why wouldn't they benchmark the accuracy against jev too?

hollow-moe

what in the michaelsoft binbows? micro$oft actually naming a product clearly and concisely? Is the team office hidden in a far building wing that marketing hasn't found yet?

HarHarVeryFunny

Looks like yet another non-price-competitive Jev competitor. 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!

buredoranna

clippy! is that you!?

fredsmith219

The article talks about using the decision model in code, but could it be used to help indecisive people with everyday life, decision decisions? I know a few and they could really use help.

MisterMunchkin

State: “I shit my pants and now my pants have shit in them” Question: “Which team should handle this message?” Result: “Tech Support (85%)” Yep, sounds about right.

elzbardico

In related news TypeSafe AI just raised a ginourmous amount of money.

Topfi

In my minimal suite it was cheaper (by 0.72x) but higher latency (283ms vs 369ms p50) than Jev. Results were very comparable across all scenarios I measure, first of these that I have tested that actually justifies its existence as a commercial release. 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.

mrbonner

I just don’t understand the use of a decoder model to make a classifier. Jev model will give you probability scores for each of the classes/selections you ask for. The probability is actual statistical probability that a selection is right. 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?

chrisandchris

Will they use it to decide how to name Office, ehm Office 365, ehm Microsoft 365, ehm Microsoft 365 Copilot next?