Based on the first table, why would I pick this over GLM?
Because your employer might make you exclusively use enterprise copilot.
As long as my employer is footing the bill, fine.
For personal stuff this release is not noteworthy.
MAI-Thinking-1
51–60 of 90 posts
Re: MAI-Thinking-1
#52Earlier quoted context omitted.
I would really like to see what "appropriately licensed data" means. Cannot imagine they didn't copy all open repo's on GitHub, and can't imagine they asked for permission, or are reproducing license texts from these repo's now. It sounds hand wavy. P.S. A fairly basic website otherwise, but it unfortunately seems to be hacking scroll for no good reason.
I assume they took the actual repos’ licenses info account. I don’t understand why they should ask for permission when the license would already allow for it.
Which licenses allow usage for training? MIT, BSD, etc likely do. But I would expect it gets weird for all the various copyleft licences.
Re: MAI-Thinking-1
#53Anyone believing those benchmark numbers from a 35B model?
Re: MAI-Thinking-1
#54> MAI-Thinking-1 is built with enterprise readiness in mind. It supports long context with a 256k token window Isn’t 1M becoming the norm?
1M it's only marketing, in my experience above 150k quality noticeable drops.
Claude code will suggest you to start a new session or compact if you go above 100k.
Re: MAI-Thinking-1
#55> Second, clean data. MAI-Thinking-1 was trained on clean and appropriately licensed data, with AI-generated content excluded from pre-training. This matters for quality, provenance, and control. If we cannot account for what shaped a model, we cannot fully understand its behavior or credibly improve it. Shots fired? It would be interesting to see how far "clean data" can go on the scaling laws.
It's interesting because their last model series (Phi) was based around the thesis that high-quality synthetic data is better than a large pre-training corpus.
Re: MAI-Thinking-1
#56Anyone believing those benchmark numbers from a 35B model?
It says right at the top, 35B active, 1T total.
Re: MAI-Thinking-1
#57Earlier quoted context omitted.
> with AI-generated content excluded from pre-training. > without distillation from third-party models sounds like zero unless they are lying.
"how many of those shapes are rectangles?" "sounds like zero unless they are squares" Adding "unless" to a statement makes it vacuous if the latter clause is weaker than the first clause. I find it hard to believe that a company willing to violate licenses would have scruples about lying about it.
Not vacuous, but tautological.
Which is different, because tautologies can actually be quite directly informative. Whereas vacuous truths tend to be oblique.
Also, “Microsoft is lying” is not a logically stronger statement, because they might be lying about something other than whether they distilled or trained on AI output.
Re: MAI-Thinking-1
#58The benchmarks are a bit of a disaster? It's at about DeepSeek V3.2 level, but with about 50% more parameters. Loses handily to the also smaller GLM-5.1, and even worse to the similarly sized Kimi K2.6.
Re: MAI-Thinking-1
#59> Second, clean data. MAI-Thinking-1 was trained on clean and appropriately licensed data, with AI-generated content excluded from pre-training. This matters for quality, provenance, and control. If we cannot account for what shaped a model, we cannot fully understand its behavior or credibly improve it. Shots fired? It would be interesting to see how far "clean data" can go on the scaling laws.
I'd assume it's not up to par with Qwen-3.5 then, which has been distilling Claude, and the quality of the model is probably a direct result of that.
Re: MAI-Thinking-1
#60The benchmarks are a bit of a disaster? It's at about DeepSeek V3.2 level, but with about 50% more parameters. Loses handily to the also smaller GLM-5.1, and even worse to the similarly sized Kimi K2.6.
Yes and no.
Yes from a user PoV, I don't really see a great reason to use this other than for enterprises that care about using a model not trained on copyrighted data (not sure what the market really is for this anymore, feels like this concern has been forgotten by most customers).
From a strategic PoV for MS, all the models you cited are distilling GPT/Claude/Gemini and wouldn't be anywhere as good as they are without this distillation, which in turn means you are dependent on OAI/Anthropic/G first shipping a good model to generate data for your training. This MAI model is trained from scratch with no synthetic data or distillation. So in term of benchmark its obviously much harder to get strong score and thus not a disaster if they can keep on improving.