> 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 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.
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.
Maybe, but Microsoft, through their partnership with OpenAI, is already involved in major copyright lawsuits. That is probably a driving force for this move, actually... I doubt they would want to tempt fate while those lawsuits are on-going.
"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.
It's good there is a new player on the market, I take benchmark tables with a grain of salt, however. Speaking about model presentation it's funny to see how clearly their website is inspired by other AI company blogs with extra innovation of hijacked scrollbar.
Yes it is, but I can imagine that they want to start out a bit smaller to see how well things scale, and/or did not yet have the time to work on optimizing for the large context windows.
The 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 model 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.
Looks like the OAI divergence is finally taking place. Seems like the comparisons are mainly with Opus 4.6 and GPT 5.4 though. Still, exciting to see a new frontier player.
> MAI-Thinking-1 is a 35B-active, ~1T-total parameters, sparse Mixture of Experts model, a smaller inference footprint than much larger models.
This seemingly nonsensical sentence (of course this will have a smaller inference footprint than larger models) suggests this model's competitors have larger inference footprints and total parameter sizes.
They've hijacked scrolling. They've hijacked the spacebar. It flickers like crazy when I try to move through the article. Trying to get through it is an exercise in madness.
"Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting."
> 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 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.
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.
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.
I doubt any lab would say otherwise, they all _claim_ to use licensed data
Maybe, but Microsoft, through their partnership with OpenAI, is already involved in major copyright lawsuits. That is probably a driving force for this move, actually... I doubt they would want to tempt fate while those lawsuits are on-going.
I'm interested how much "Clean Data" is synthetic data from "unclean" models...
So, laundered data?
> with AI-generated content excluded from pre-training.
> without distillation from third-party models
sounds like zero unless they are lying.
> with AI-generated content excluded from pre-training.
Though this is largely impossible these days, unless they pre-trained on pre-AI era data.
"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.
Adding "unless" to a statement makes it vacuous if the latter clause is weaker than the first clause
I think that's the point. "How do I say they're lying without outright saying they're lying?"
It's a common rhetorical trick.
“ We trained it from the ground up on enterprise grade, clean and commercially licensed data, without distillation from third-party models.”
aka all of GitHub OSS
Yeah this is exactly what I was thinking.
It's good there is a new player on the market, I take benchmark tables with a grain of salt, however. Speaking about model presentation it's funny to see how clearly their website is inspired by other AI company blogs with extra innovation of hijacked scrollbar.
> 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.
Yes it is, but I can imagine that they want to start out a bit smaller to see how well things scale, and/or did not yet have the time to work on optimizing for the large context windows.
I struggle to get quality results from the frontier models at contexts > 256k anyway.
The 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 model 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.
Looks like the OAI divergence is finally taking place. Seems like the comparisons are mainly with Opus 4.6 and GPT 5.4 though. Still, exciting to see a new frontier player.
Is it a frontier player though, or perhaps a new benchmaxxed model? People were saying similar things about Grok but it ultimately amounted to little.
"preferred by humans over Sonnet 4.6" makes it pretty clearly not benchmaxxed though.
At least when you define benchmaxxed as "good in benchmarks but not human preference".
Post 4.6 Anthropic models do not exactly have a stellar reputation, so that choice is smart.
> MAI-Thinking-1 is a 35B-active, ~1T-total parameters, sparse Mixture of Experts model, a smaller inference footprint than much larger models.
This seemingly nonsensical sentence (of course this will have a smaller inference footprint than larger models) suggests this model's competitors have larger inference footprints and total parameter sizes.
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.
Is that a pretext zoom effect when changing screen dimensions? Very cool.
We need to see DeepSWE scores. SWE Bench Pro is junk.
I like it so much when a website hijacks the way my scroll works. This is truly innovative.
At least there shouldn't be any complaints about benchmaxing this time.
Just because it is performing rather poorly by comparison, it doesn’t mean it isn’t benchmaxxed. It can still be worse than it appears.
It isn't benchmaxxed because they are using human preference as an evaluation.
Anyone believing those benchmark numbers from a 35B model?
It says right at the top, 35B active, 1T total.
They've hijacked scrolling. They've hijacked the spacebar. It flickers like crazy when I try to move through the article. Trying to get through it is an exercise in madness.
I do not understand how scroll hijacking is still a thing. Who thinks this is a better experience?
Designers.
Even without flicker it is very distracting. Why do people think this is a good idea?
I normally don't comment on matters of taste like this, but wow this is brutal. It's like someone threw the site in a vat of molasses.
there is also a gap between the header and the top of the page... they should ask the ai to make it better a few more times...
I gave up after the first scroll.
It really looks like they used Claude to design this webpage. I guess the color taupe it the marker of good AI today.
Inflection AI
7 modes launched. 5 models in the dropdown. Only 4 actually usable :(
About time Microsoft joined the fray. After the OpenAI divorce, it really looked like Microsoft was going to become another Uber.
They still own 27% of OpenAI, this IPO will feed them a lot of easy cash.
Absolutely disgusting scroll jacking, even when "Accessibility mode" is turned on
I'm sure most of us agree, but:
"Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting."
https://news.ycombinator.com/newsguidelines.html
Forgot about this, my bad!