MiMo v2.6

For (C) -- I think it could additional create jobs funded by government, philanthropic and other private institutions. For example, a government funded museum may already be participating in Wikimedia GLAM projects (e.g. uploading historical images to Wikimedia Commons with complete metadata). Perhaps this type of open source contribution may increase if organisations realise their mission can be better accomplished by contributing this same open data into LLMs, in addition to Wikimedia Commons. If the museum's mission is to educate the public on the history of life in ACMEville, having LLMs be able to provide historical information and images to a prompt of "What is the history of ACMEville?" may be a good pursuit.

I'm sceptical though whether use of LLMs would encourage creation of data that doesn't already exist. For example, if you ask an LLM "What are the top 100 most prevalent flora endemic to ACME National Park", this data may not currently exist _at all_, and to collect, would require paying botanists to do an extensive field survey. If no one has done this work yet--why? Is it relevant to the scientific community, to making government decisions, etc, or just an obscure academic curiosity. There are certainly some journal articles on _other_ national parks describing some of their common endemic flora, but perhaps there was a reason for this. Such as a scientist funded by a one-off government program trying to determine how to preserve or even create habitat for a specific endangered species.

Consider for the prompt of: "What are the top 100 most prevalent flora endemic to ACME National Park"

An LLM may reply: "I couldn't find any journal article or other prior work that may answer this question. Typically such survey field work may cost $X to complete, require expert botanists, and take 6-12 months to complete. Let me know if you want further information on how to find and select a company to conduct such a botanical field survey."

Would this type of LLM response grow the industry of botanical field surveys, or do nothing, perhaps because anyone likely to fund botanical field surveys is already doing so regardless of whatever is happening with AI.

I think all of these can be addressed by being able to understand which machines contributed what to the model. I know that's messy, but if I can state "this information has come from Alice and the consensus is that it's good and right as it aligns with other information from Bob and others, meanwhile this information came from Mallory and stands out as being incongruent with the rest of the information I have in the model", we can identify malicious violations. If we're then able to state either Alice or Bob is one of the copyright holders, and one of them gets paid a little more, the other a little less as the confirmation agent, well, the economics of all of this changes a little.

At the moment we have Annas Archive being paid by frontier labs and rare/second hand books being destroyed in order to support the training regime. If instead we could just pay the publishers and they could distribute royalties to authors...

i think you’re wrong - async rollouts very much standard and i would be shocked if benchmark evals were not done in parallel

Hold on, isn't that just standard practice for post-training LLMs for agentic use? Give task, generate n rollouts, grade rollouts (either at termination or after each tool call)? Or is the difference that the rollouts are generated ahead of time and then graded? (Of course, then it's not really on-policy.)

yes I'm interested. please consider writing this

I'd read the heck out of that blogpost. You have my interest.

Seconded.-

I would really enjoy that blog post.

Write and we shall read.

+1 waiting for the blog post!

Please do

definitely do!

Please do!

100% agreed

It'd be great to see a description of even just a subset of training datasets. It feels very much under-reported how much expense is worth investing in preparing and selecting training datasets versus just using masses of random quality unprepared training data. This dashboard appears to be good though in showing the limits quickly reached when throwing parameters and compute at the problem.

For example, if they were to train on Wikipedia dumps, do they consider every article to be the same quality across each language, or have they done more work beyond Wikipedia's own article quality ratings to make training decisions such as "Ignore cebwiki it's machine-generated spam" and "Treat dewiki articles with coordinates within Germany as being higher quality (weight it higher) than their equivalent enwiki articles".

And let's say one of the datasets is all the source code of packages in the Gentoo package repository. Not every software package is a good example of how to write code. You perhaps wouldn't want to train your LLM on 1990s era PHP web application source code as an example of how to write code in 2026. Instead, you'd possibly want to use such PHP web application source code as a negative training example of what _not_ to write. But when training an LLM to detect software bugs, maybe outdated PHP source code is good for training.

Similarly for translation, perhaps UN treaty documents translated into 4+ languages are good translation examples because of high accuracy needed, professional translators being used, and bigger budgets. However this training data would perhaps be a negative training example towards translating chat messages, movie subtitles, etc because it doesn't use everyday slang and could result in output of nonsense such as "Pending Your Excellency's response, please accept, Your Excellency, my sincere greetings." for a prompt asking to write a birthday card for a child.

Preparing training data and deciding how to best use it for training I assume would be the largest expense (cost of labour -- mostly expert labour too) and also the greatest opportunity in the future for LLMs to improve. It seems to me somewhat irrelevant if the dashboard indicates a compute expense of $1m or $5m if good training datasets (prepared by experts in their fields) cost $10m/y to maintain. For example, hiring expert software developers to tag 1000's of open source software packages according to their quality, on different metrics, such as human readability, performance optimisation with choice of algorithms, reasonable trade-off between coherence and coupling in the software architecture, currency with state of the art programming trends/preferred dependencies/operating system APIs, etc. And keeping that metadata continually updated rather than a rapidly obsolete once off tagging project completed in 2005.

Theater is often more effective than truth

+1 at some point, you need to expect to train a much better base model using everything you've learnt. At the least, you probably want to bring on line the next 10 clever RL environments and ideas your team has been cooking up (which will pipeline into v2.7 etc.)

> But the problem is that the AI buildout has become a large percentage of GDP. So obviously the government wants to keep it going because these companies are pumping enormous amounts of money into the economy.

They are pumping enormous amounts of money into each other. Hardly any of that is making its way to people, it's all going to highly automated construction and to energy use.

Seriously, how many jobs did the $1t in venture capital fund?

I’m still not at all sure about the “billions” invested claim. How much of that is cloud running the models? How much is pre and post training (which may or may not be part of what we’d want to include in accounting). Etc. Does anyone have links to good reporting about this: not blind recitations of numbers, but analysis and thought mixes with investigation?

Well, if you are right, I just hope their protectionism will only affect the American market, and they leave us unAmericans free to get our models from wherever.

On the other hand (OTOH), China is desperate to keep up and keeps pushing open models (rightfully so), as they understand how far ahead from everyone the US is, and that whoever gets this right first basically is going to become an alien compared to others.

But even with all the open models the US is just insanely ahead in AI buildout and capital allocation (as usual).

Is it a bubble? Is it like the race for the-first-to-the-nuclear bomb? Both?

Unless something has shifted, “everyone catches up” is because these bleeding edge models are distilled. You don’t see this happening with other European and US labs and the problem isn’t something being ignored. I’m not convinced this pattern will continue indefinitely.

No chinese lab has caught up yet. They've tried to fake it by distilling and overfitting on benchmarks to make their models look better than they are, the 'best' models available from chinese labs right now (GLM 5.3 and Kimi K3) fall apart completely when you try to do real work with them. K3 is especially embarrassing because it is larger than Mythos yet performs worse than opus 5 and 5.6 sol in benchmarks they haven't been able to fake yet.

The general idea is that Anthropic/OpenAI is pushing this narrative as an attempt at "Regulatory Capture"[1] which would allow them to make it prohibitively expensive for anyone but them to enter the market thus stifling competition.

* 1: https://en.wikipedia.org/wiki/Regulatory_capture

> Please explain how putting an upper bound on how good the strongest models can be prevents cheaper less strong models from catching up, rather than enabling it. I do not understand this argument at all.

They are not proposing to regulate only the strongest models. They are proposing to regulate all models. If they are already on top, regulation may stop them from proceeding further, but it also stops the cheaper alternatives from catching up.

If they feel they have reached the asymptote of the curve, then regulation doesn't affect them, it affects those who have yet to reach the asymptote.

I don't think "putting an upper bound" was OPs phrasing?

That’s not the argument.

> and the availability of good enough open weight models for their purposes

it is childish to believe Chinese are going to give up profits to provide such open weight models forever. the whole idea of being "open" is not compatible with the Chinese culture.

The counterpoint to that, though, is that the Chinese companies have to figure out how to be competitive, regardless of their significant compute deficit. And, as far as I can tell, they're actually doing that. They're trailing the frontiers in model effectiveness, but not by years. It's single digit months.

If there is no upper bound how how these things scale with compute, and if China does really begin to catch up to Nvidia (and they're probably not going to feel encumbered by US patents for domestic AI hardware, given how important AI seems to be to the Chinese government), there will come a day when China leapfrogs the US on AI.

If you pay attention to how these US CEOs talk, it'll be "safety". If I were to guess, they'll try and require a lot of testing, validation, certification before a model is legally allowed to be used in the US or on US products.

It won't be a great moat, they'll probably try and get trade treaties setup to try and expand the moat. But ultimately it won't slow down chinese model development, just limit who can legally use them.

From the frontier labs, the only publicly stated one seemed to be to give them an exception from anti-trust laws to form a cartel and place - incidentally friendly - regulators in charge of monitoring everyone's work.

From politicians like Bernie Sanders, we've had proposals like 20 year imprisonment for anyone researching "ASI".

> What's different today is that now all the big LLM firms

not all, just a few American ones (~PayPal Mafia + Google), there are other big American LLM developers (notables include Nvidia, Meta, and Palantir) that do not agree

Moving 1000W of heat generation takes way less than 1000W.

Hyperscaler PUE (which includes cooling, power conversion, etc) is typically 1.10-1.15, so multiply 1.5% by 1.1.

The person you replied to already mentioned cooling and multiplied by 1.67 to cover ancillary uses.

How long will it take to convince them that it is worth it to get into long-running relationships with the US. How long before another populist is elected who will rip up agreements for weird reasons and you have to renegotiate them?

I think you underestimate what a massive country China is and the scale of their military. Russia is already quite an adversary, but China is at an even higher level. By some metrics, they have already surpassed the US military.

So it is very unlikely that a neighbor will have the audacity to attack them, and even if this happens, i'd expect the war to be over rapidly. Just like if Canada or Mexico tried to attack the US.

> China has recently awarded 1.3 to 1.4 million engineering bachelor's degrees a year, and about 292,000 postgraduate engineering degrees in 2022. [0]

> For the US, ASEE and NCES put engineering bachelor's degrees at a stable 130,000–145,000 a year, rising above 200,000 if computer science is included. Because China counts CS as engineering, the fair comparison is roughly 1.35M against 230–250k US engineering plus CS graduates. That is about 5–6x in absolute terms. Adjusted for population (1.41B vs 335M), China produces about 950 per million people and the US about 700 (1.3–1.4x more).

Even Claude highlights that the numbers aren't apples to apples and in China the definition of "engineer" is more loose. I do believe that in relative numbers, more engineers graduate in China

On lawyers:

> In the US there were 1,322,649 active lawyers as of January 2024. For China, there were around 650,000 lawyers in 2022. Which in relative terms is 8x in the US than China.

From a pure training perspective, yeah they could try to align it with CCP values. But the promise of AI is that it'll unlock an explosion of growth and prosperity. What happens when the CCP no longer becomes seen as the the primary enabler of growth? What happens when people are exposed to greater levels of agency? CCP played with fire in the COVID lockdowns and almost got burnt.

Like Terry Tao recently said, there are nonlinear effects at play. Things are going to get chaotic and I do not have confidence (like the parent comment) of anyone "winning".

Well, US did not really have that to start with (Kaiser &co learned fast). So it’s not about having something and losing it as much as bootstrapping and discarding.

For processes that are scalable, well known and industrial, profit motive actually does provide results.

The question is more of are there bottlenecks of skill and maybe secret sauce (eg something like ASML).

And the will

For now

It is a 9B agentic model developed by Xiaomi MiMo through supervised fine-tuning of Qwen3.5-9B on MiMo-generated data

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I prefer GLM 5.3 (Flash or not) over Deepseek 4.1 Flash because the GLM models are significantly less verbose.

Any lower (or broken) quantization could do that, not what I'm talking about though. They work fine for their size, as far as I can tell. Just surprised the Flash would reason for longer than the Pro.

Tried both https://token-plan-ams.xiaomimimo.com/v1 and https://api.xiaomimimo.com/v1, hoping they don't run any quantized variants there, but who knows.

I would think there is sufficient information in the various config files to work this out, based what I've seen in my own quant artifacts.

I suspect they are calculating something in the weights or config, I see it pretty consistently with quants

Yeah, for better or worse, writing style is practically uncorrelated with agentic performance, which is all the rage right now and the thing that most popular benchmarks currently prioritize.

I got a 3D pelican out of Blender and Astra a few weeks ago: https://simonwillison.net/2026/Sep/5/blender-coding-agents-m...

I've not committed to that as a benchmark yet because you really need a full coding agent configured to run Blender, and I like benchmarks I can run as a single prompt/response through the appropriate API.

I have been intending to get more of a coding agent benchmark going though, so maybe this should be part of it.

Not sure if it is the same thing they're referring, but the transition from loading the prompt from the gist to the result is indeed jarring: https://files.catbox.moe/c56eyl.mp4

I don't believe it is that important, but I guess you might've just hit some people's pet peeve with that. If anything, it goes to show as to why skeletons are popular on modern sites

How dare people say neural networks generalize when it's all clearly in the training data? /ß

Making SVGs is coding (of a very specific markup language). So it's not actually absurd. We've also seen that more capable models are generally better at everything minus benchmark contamination (benchmaxxing) anomalities.

Yeah, I thought the "N" was for Nest

Honestly, seems like a pelican WOULD ride side saddle if at all.

This "no, Virginia, they aren't pelicanmaxxing" article is what broodbucket is probably thinking of: https://dylancastillo.co/posts/pelicanmaxxing.html

In any case, pelicanmaxxing comments without any further proof or evidence probably fall under the "Please don't post shallow dismissals" guideline.

Apologies for not taking the time to find it, but there was a post that tried to determine if the pelican was benchmaxxed across a bunch of models by comparing it to other SVGs, and found that it wasn't at all.

yeah nobody is happy using windows either, but still they all do anyways.

we would be better off using linux all together, but here we are. Lets see maybe every company will have their own little instance of a local LLM in their basement in the future, when the AI-bubble pops and server-racks become cheap

Contracts, provisioning, legal, data retention, those are the hard part in enterprise.

copilot is doing the same. i just joined a corporate and they only allow copilot. i am getting brain cancer using it, especially being used to claude max from before.

but cost is a thing, if i use the LLM gateway for a chatGPT-5.4 i am quickly (3 days of very light use) on 50$ in token. and everyone and their intern are using copilot. As azure has the guarantee that company data will not leak they are taking the bait.

The fear of secrets landing in the training data is just too big, and they are all microsoft 365 customers already. And lastly Management seeing that copilot can autonomously edit an excel file for them already counts as mindblowing... 0_0

I've noticed the context loss in subsequent turns, it's so strange. For me it happens whenever I stop gemini mid prompt. Sometimes its response annoys me, so I'll hit stop frequently. And then subsequent msgs seem to go through like a new chat where it forgets what we were talking about.

They’ve been saying that for the last 18 months at least. They’re always just a few months behind…

I picked double out of a hat, obviously there’s some intelligence gap that’s too small for a price premium beyond a certain point, but it seems like OpenAI and Anthropic’s revenue keeps climbing, so I don’t think we’ve hit it.

If the set is unbounded, then obviously yes.

there are many tasks (increasingly more each day) where small models are more than enough

It’s called “HBF”, high bandwidth flash, and it’s on its way!

That nearly certainly boots you to secs-per-tok land (as opposed to tok/s). Plausible if you are willing to wait hours to days for responses for simple testing, but not (debatably) "usable".

this is to offload VRAM to DRAM (for GP comment), and makes no difference for URAM

Hmm I may be mistaken on that now, I remember it being in the user agreement. There are third party routers that explicitly offer ZDR, though API costs will be slightly more expensive.

I didn't expect there to be a time limit and I do know benchmarks that don't have a time limit. They just penalize slower models which I think is fair.

In my experience models just don't take forever to mark tasks as done.

For my coding usage I don't set time limit so benchmarks that do so are providing me less interesting use cases.

With that said, I can understand having budget limits for expensive LLMs for those of us that don't have infinite VC money.

As for no internet access, the issue is that for most problems we do want Llms to be able to search docs, API specs, GitHub issues, etc. So not allowing that usually just favours larger LLMs that were able to memorize more data, not necessarily smarter ones when both have internet access.

OpenAI had to cut costs because of Anthropic. I also do not trust the benchmarks when it comes to models anymore. I have tried both Claude and OpenAI models and while it is true that the 5.6 series is smarter than Deepseek (at the time i tested it against 4.0) at that price it is still not worth it and sometimes randomly refuses to do tasks or stops midway etc.

Do also remember China is this far in the AI race despite all chip restrictions from America. If they were in equal standards I truly think Chinese models would have long surpassed American ones. Also would like to remind how Anthropic CEO is being hostile and blaming Chinese models with distilling meanwhile their own models claimed to be Qwen¹ and their stance against open models is negative² and they still keep blaming China for it.

1- https://news.ycombinator.com/item?id=48671252

2-https://www.anthropic.com/news/position-open-weights-models

> I did not personally test the open weight models beyond the old Qwen 3.6 27B, which produced unusably bad results for me.

So you don't have much perspective on things, it seems. Let me introduce you to the GLM 5.2 and then 5.3/5.3 flash series of... "oh, wow, I should have bought some RTX PRO 6000's while they were 'cheap'" stage of progression.

As someone carrying multiple max subscriptions to both claude and codex - primary workhorse is glm 5.3 flash running on rented GPUs for less than a latte/hr.

I also found qwen 3.6 27B nearly useless for my own needs. DS4 flash 0731 and then 4.1 have been nearly as eye opening as glm 5.3 flash, but have their own warts.

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> OpenAI reduced prices and Anthropic increased weekly usage limits.

As a Max x20 and Pro x20 subscriber, can tell you that it doesn't matter since they continually move the baseline of token use So in practice you feel that you're continually getting less from your subscription.

While it never happened to me in the past, i reached my weekly limit within 3 days using Opus 5. And the Open AI weekly limit essentially is a Claude Max x20 5-hour limit. Not even talking about the baseline in intelligence : on release day Astra was so good that it lead me to move to Pro x20. Now it's dumb af and token use is insane.

Deepseek 4.1 Flash has been a lifeboat for me, finally able to work without being constrained/distracted by limits and with what is in my view even better intelligence than Opus 5 for a fraction of the costs. DS is not messing up my brain with load-bearing pseudo jargon in every sentence. It respects coding guidelines, and completes even the most complex tasks most of the time in one shot.

DS 4.1 had been able to add complex features to my repo without breaking a sweat (330k lines of F# + 4M circa lines of an Angular frontend). Writes very idiomatic F# and respects our guidelines and style perfectly. Just completed an extensive UI/UX research and implementation work.

I am ditching both x20 subs and will only keep a Pro x5 because wife does a lot of design work and needs solid image generation capabilities.

My primary use is AI coding agent. Its vastly cheaper than Kimi K3 and I haven't found a scenario where I really need Kimi K3 versus smaller models. GLM-5.3 Flash is good but there is series of bugs in the vllm middleware that prevent GLM models from getting all of their reasoning content returned to them that impairs inference quality. A lot of inference providers use vllm which makes it hard to find a good provider for GLM. I've been using friendli.ai but using GLM-5.3 Flash from them is more expensive then using DS V4 Flash from deepinfra.com simply because deepinfra.com is so cheap. The DS V4 Flash cost at together.ai is similar to the GLM-5.3 Flash from friendli.ai or at least that's what I found in my benchmarks a week ago: https://www.linkedin.com/posts/joshheitzman_i-ran-a-fuller-r...

I've used Kimi K3 for a few months as my main model and DeepSeek 4.1 is as fast and about 10x cheaper.

I just had like four big sessions going today, paid about $8 in tokens. I see no reason to pay more, this is more than I need for intelligence.

4.1 flash is very fast and capable. Token efficiency is not great so it fill up context window much faster compared to similarly capable models.

glm 5.3 flash is a tad slower but a bit more capable and way more token efficient.

Source: self hosted tested on rented GB200 node at 8bit.

I haven't tried 4.1 flash as I'm assuming its a preview. I did not get good results from the preview version of 4.0 flash (i.e. the one that did not include the month and date of release in its name).

In my very limited first test with MiMo 2.6 Flash it was much cheaper than DeepSeek 4.1 flash for a given broad task. It's at least worth a try.

I'm looking forward, towards the future, when I use "passing ... we need", need being key here as it implies something we don't yet have

I expect this to happen within 12-18 months, the differentiation has shrunk, many models are now sufficiently capable for most tasks

As much as the US has been easy to hate lately, I don't hesitate to say Xi Jinping as the most powerful man on Earth would be much, much worse.

Also, frankly, as a fellow Canadian it's pretty clear that the biggest "rival" the US has right now is itself. Just passed out in the corner puking on itself shouting about all the foreigners who won't talk to it.

I'm from Europe and I hate America way more than China now. Used to be about equal but then Trump started extorting Ukraine, threatening their own allies and sending billions to Israel to help with a genocide. I think that exposed America for what it really is.

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Canadians warming up to China makes me think of Germany becoming increasingly reliant on Russia in the 2010s.

Neither the US nor the Chinese companies are on your side then. They both censor, just different topics.

But at least I can run Chinese models locally, and strip a lot of that censorship/refusal.

As long as that speech doesn't come from CNN or criticise Charlie Kirk, Israel or Trump? I'm sceptical about how much the US really values free speech

Nvidia's top AI chip, Rubin, sells in 72-GPU racks for about $3.5–7.8M. A rack running Xiaomi's MiMo V2.6 Pro could generate roughly 150–300B tokens a day, worth about $130–260k at Xiaomi's API price. That's a payback of a few weeks in theory.

So yes, we are getting scammed by American SOTA.

that's belt and suspenders too

You're right to push back. That's on me. There is one thing I must flag which will move the needle. My honest take: It's all about the shape of your priors. That's the lever, and that's not nothing. It's worth your attention before you land your next remark. Here's why that matters: It re-contextualizes everything.

when mobile data first arrived, it was expensive and people bragged about their bills (token counts today)

with time, it became commoditized, people now have unlimited plans, and the money is made by the applications that sit on top (token generation is increasingly undifferentiated low-level infra)

This is not to say there has not been significant innovation in the time since, but it's a low margin business (tokens look to be headed this way)