I was absolutely mind blown when I saw how they were publishing that training dashboard while US models publish 100s of pages of reports (just provide a "copy as MD" button, folks, in the future). I was thinking about doing something similar but did not know how to show it, and this is a perfect example for someone who wants to show whatever they are training, for me it was local training on a consumer GPU.

My dream is to see this like a dashboard for a model trained across distributed machines, like Bitcoin mining, where minted coins are given to people whose machines were used for training. I don't know if they are worth it, but bragging rights alone, like a tag they can put on a website or social media, will be good enough for me.

Thanks so much for sharing this. As someone who mostly watches from the sideline, can you share what you can see in this dashboard that someone like me can't see? Is it the metrics themselves that they measure (the metrics tab is absurdly detailed), something in the notices, or something else I missed?

> The realtime dashboard

'twas discussed a few days ago:

Xiaomi Mimo 2.6 live post-training dashboard - https://news.ycombinator.com/item?id=49732270 - Sept 2026 (155 comments)

> the pro run restarted at step 17 due to a GPU OOM issue caused by expert load imbalance. we have adjusted the training parallelism strategy.

no idea why I laughed so hard at this, feel so familiar

The RL dashboard is quite cool.

I wonder if this waters down the “distillation attack” claims by Anthropic. They have their own RL environments! I guess the caveat is that the RL datasets are still opaque, nothing is really proved.

> got lots of clever behind the scene tricks like Google or Deepseek writeups) and benchmark scores

Xiaomi MiMo is led by Luo Fuli, a former Alibaba & DeepSeek employee. Perhaps it is due to Luo just how similar Xiaomi's tech & GTM approach is to DeepSeek's.

- How Luo Fuli Keeps an Earthy Touch as she Soars Through the AI World, https://newsen.pku.edu.cn/news_events/news/people/15385.html (https://archive.vn/I8Pmu).

- Luo Fuli, the 30-year-old ‘AI genius girl’ behind DeepSeek’s success?, https://e.vnexpress.net/news/tech/personalities/who-is-luo-f... (https://archive.vn/sb3B6).

maybe this is why Dario want to slow down AI development and all the big AI labs in the USA is singing the same song.

whey they all singing the same tune. it make me question what is their real motives.

they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.

There's also a Qwen 3.5 9B distill

https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B

curious why the HF pill (on the right) always has inaccurate values

more like 500B in FP8

I think we are talking about the medium term for China to pull ahead. They just released a new domestic AI chip which seems in important ways to have caught up to Nvidias previous generation. They have a LOT more manufacturing capacity. They are collaborating via open source by default. They have better materials access and much more energy capacity. They have many more people overall and more researchers.

There is a strong chance most of the researchers are pulled out of the US and Europe if WWIII really kicks off or even if there is just more global crisis and concern.

One other thing about the power needs. Within a few years, the power efficiency of AI chips is likely to improve by a factor of 20, 50 or more times by switching to true compute-in-memory architecture with new materials that have made rapid progress lately.

I was curious how much energy is actually needed to power these datacenters, so I did a little bit of math.

Looking at Nvidia revenues in the past few years, there's maybe $300 billion worth of GPUs currently deployed in the U.S. The B200 costs ~$40k, so we have 7.5 million B200-equivalents, which draw 1000W. Running these at full capacity requires 66 TWh a year, or ~1.5% of total current U.S. electricity consumption. Maybe a bit more to account for inefficiencies, cooling, and other components, but not more than ~2.5% total I would guess.

So it's not that much in reality, but will definitely grow fast.

It's worth looking at similar industries with enormous electricity requirements such as aluminium smelting, where the plant can be located in a friendly country but the product is owned and controlled back in the US.

Aluminium is often described as "congealed electricity". Ship bauxite to wherever power is cheap and stranded, turn it into metal, and ship the metal out. Here in NZ, Tiwai Point is the textbook case, with London-based Rio Tinto running a smelter on the other side of the world that exists mainly because Manapōuri hydro had nowhere else to go.

AI data centres can be just the same - even more so, since the plant's assets (its chips) are virtually perishables, so there is less concern about assets becoming stranded if the host goes rogue. All the US needs is friendly and stable allied countries with cheap power.

the bottleneck right now is compute, not energy, and it's not even close. That is why RAM, SSD, CPU, and GPU prices are increasing exponentially, while solar panels are dropping.

Also, unlike China, US companies are building data centers all over the world, which gives them higher distribution and ability to colocate with the energy production sources.

Lastly, energy production costs have been decreasing over the last couple of decades. If they will increase, the market will react, as it always does. Looking backwards does not predict the future in this case.

The sole reason the Chinese cannot “win” is because ceding more power to agentic AI will eventually reduce the primacy of the CCP’s ideological control.

As these models get smarter they will no longer distribute it openly. Patel reporting this too.

There are real headwinds that I don’t think people have thought through.

How long is the long run though? China doesn't have the chips, and probably won't have them for a while.

I agree in principle, but it could be more than 5 years, maybe 10. Who knows what things will look like then.

Between 1941 and 1945 the US built well over 8000 major naval vessels. Never write off America--if there's a will, there's a way.

> China will most probably win the AI race in the long run because of one major bottleneck the US has - energy

Plus another bottleneck - China produces engineers, the US produces lawyers.

To add to your point:

> "Quite frankly, the biggest issue we are now having is not a compute glut, but it's power and it's sort of the ability to get the builds done fast enough close to power," he told the show's hosts. "So if you can't do that, you may actually have a bunch of chips sitting in inventory that I can't plug in. In fact, that is my problem today. It's not a supply issue of chips. It's actually the fact that I don't have warm shells to plug into."

> - Satya Nadella

https://redmondmag.com/blogs/generationai/2025/12/microsoft-...

But can they build their data centres ... IN SPACE?! (spoken like the dude from half baked who insists you need to try everything "on weed")

I think we can say pretty confidently they aren't pelican-bench-maxxing

can you update this website, I just wish the entire layout wouldn't shift when the page gets loaded and the timestamps in the title look very ugly and take up a lot of space.

Just me, or do these look bad?

Qwen3.8-27b pelican was amazing on Mac.

https://www.nudgehost.com/dpjn3uwe

How does this translate to coding performance, which is what most of HN cares about (...I assume)?

ish… at least we can be sure they don’t benchmaxx the pelicans lol

can you recommend any benchmark websites that show up-to-date details like this?

TerminaBench, DeepSwe sites are out of date.

Maybe you should not trust any of the benchmarks!

Why not? In my own benchmark Opus 5 does in fact come out on top[1]

1 - https://bench.killswitch-lang.org/

They match my experience. Astra and Fable I rate below Sonnet. They are incredibly poor. They were excellent for a couple of days after release and then plummeted.

Maybe I am being routed to more quantised versions or less capable models with system prompt to fake Astra or Fable.

My guess is that by function they break down frontend sections or components into pieces and I believe document things for themselves on some level, or purposely are verbose in this way. It is probably also shaped by users and existing web patterns. They probably get reinforced by models the more common they become.

The extraneous small-caps labels are one of the main idiosyncrasies of AI generated markup. I wonder how much of this is a "scaffolding" technique to help the model build stable designs. But was it reinforced in RLHF or an emergent behavior of the models?

Nice catch!

I'm also fond of how Mimo is confidently showing their model behind Deepseek and other models. They're being transparent on multiple fronts

No moat and competition isn't always preferable over a competitive oligopoly (with some differentiation). The former ends up with politicians intervening way more like with solar, steel, agriculture ....

My 0.02 on this is that for a given level of capability, yes, there is no moat. What’s frontier one day quickly becomes a commodity almost too cheap to meter within a year or two.

But there is always a use case for frontier models, even if they’re quite a bit more expensive. The set of things you can profitably do with better intelligence than everyone else is unbounded.

So yes, the number of tokens that get pushed through commodity models for very cheap will continue to grow, but so will the tokens for expensive frontier models. We’ll never run out of things to do with the latest geniuses who are twice as smart as last year’s geniuses.

The moat is the engine that builds and sells the latest geniuses, especially the compute. And no one will have more compute than American labs for the next 5-10 years.

I believe it's a function of their primary user base being in china

Same as DeepSeek, non busy time for UTC+8, maybe also cheaper electricity during night

Astra's looks the worst to me on mobile, though

For me it seems like this model’s overthinking should be tamed.

Perhaps instead of giving it a one shot task with vague prompt. I wonder how it can perform with much more detailed and constrained prompt (can you please elaborate more on the level of detailness and ambiguity that the prompt is and where does this model seem to overthink the most?)

Also are there any ways to tame such overthinking of models in general?

I hope that once models start becoming smart enough (I think for me it’s already there) or becoming genuinely the Sota. They then start focusing a lot more on optimizing token usage

https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B is an option

Is there a gamechanger around the corner to reduce DRAM requirements?

That's the RL.

I only use it for personal projects, but their european Token Plan [1] has ZDR and is hosted in Amsterdam. The DC also likely runs on 100% solar wind/power, with heat recovery systems used to heat nearby university buildings.

[1] https://platform.xiaomimimo.com?ref=UKV2FC (invite link = 10% off)

Fireworks is amazing for DeepSeek, Kimi and GLM.

Hope they bring MiMo for tests.

Absolutely! Chinese models are both cheaper and more capable in many cases, compared to the American models and their makers continuously fumbling or reducing model capability with each update. Deepseek decreased costs when they released Flash 4.1 you would not see any American company do this, in reverse they would try charge you more.

Affordability is derivative of control which is really what I care about.

I'm just not going to build long term infra that depends on something that another person can and will - objectively based on experience - take away from me at some unknown point in the future.

The biggest benefit of open models is they keep all the other players honest. The extent to which they feel they can dictate terms is directly set by the threshold where they feel people will take the trade to run open models instead.

Absolutely! DeepSeek-V4-Flash-0731 has become my daily driver. It's pretty amazing what it can do for what it costs at deepinfra.com (I don't use deepseek as a provider since they train on your data [at least their honest about it]). GLM-5.1 was my daily driver before that and Kimi K2.5 before that.

The cost issue is obviously of prime importance, but I'd also argue that the transparency of the innovations creates a tremendous cross-pollination, and not only within the Chinese communities but in the US/Europe as well. How many of us are learning the practical aspects of actually running and building AI based primarily on open models? As an example - how far would the work of vLLM or SGLang or even NVIDIA itself (all random examples) be without these models and the challenges they pose?

American models are on the frontier of capability. Chinese models are on the frontier of efficiency. The problem for American labs is that Chinese models are more than capable enough for the vast majority of applications that people care about at this point, so efficiency is more interesting for people.

I couldn’t tell you what western model I was last excited about. Probably Glimmer.

I did try to use Chinese open models, but for my production work they simply couldn't cope at all; both GLM 5.3 and Deepseek v4 went into infinite loop and wasted my tokens until my OpenRouter wallet reached 0; good thing I didn't enable the auto topup. US models, by contrast, breezed past them. Even for simpler tasks, Chinese models took long time to complete, and I needed to supervise closely. The price , in the end, didn't come cheap, mainly because too much time wasted on thinking.

So maybe one day Chinese models will squeeze out the American ones, but today is not that day.

So no, I am not excited about Chinese models ( just because its open weight and not American).

Yes, an expensive American LLM has zero capabilities as far as I’m concerned because I’m never going to pay for it.

Yep, I'm trending in that direction, and I'm someone with Claude stickers all over my laptop. My main app dev work is still going to Claude, but everything else is going to China even at API rates now.

One simple task: I needed an LLM to go through and clean up a few thousand page descriptions and titles in my personal search engine index, where the human web page authors had put in no effort sigh. I did a shoot out between Claude, Luna, GLM 5.3 Flash and Deepseek. Despite the high cost, Claude's descriptions were terrible, and even Opus warned me that the descriptions coming back from Haiku were "generalized, not accurate". I expected I would choose Luna because of price, and occasionally it did have wonderful descriptions (one captured emotion in a way no other model did). But in the end, the GLM 5.3 Flash descriptions were the easiest to read, they flow well while also being accurate & including necessary keywords, and being highly affordable. So it won out. It's a task that is nowhere near frontier, but a task where somehow China is better than frontier.

I certainly am.

Months ago I switched entirely to use Chinese model. Mostly DeepSeek and MiMo, although I recently started to play with GLM as well.

The models are excellent and in many ways I prefer them to Claude.

I see no difference in terms of capability, but the fact that they are cheap frees me to experiment.

I have a contrarian opinion that China passing America in Ai is the Sputnik moment we need to leave the hubris behind and get our mojo back

debatable if a turn around is possible before '29

No, because I'd rather not support our economic and military rivals.

Yep. If OpenAI and Anthropic get the regulatory capture to block Chinese models I’ll go on an AI strike.

As far as I can tell, if you look at the open training dashboard it's because the flash was trained on some cyber while the pro wasn't. 4% of training on the flash was cyber and 0% of the training on the pro was cyber.

It sounds like they are pricing very well because OpenAI and Anthropic have been scamming us for years. Once the infrastructure is in place, electricity cost is the only concern. China supports businesses and gives them a lot of incentives to lower their costs. And who knows what's being provided to them without anyone knowing. All in the name of winning the race.

Can we afford to look past it? If/when claudeslop starts infecting every new model to such an extent, that model will produce its own slop, infecting new models... At what point do we lose all reliable methods for establishing "truth"? This is epistemic collapse waiting to happen. I honestly thought it would take longer... holding out for a coherent shared reality in 2030 seems optimistic.

how do you recognize "claudeslop"?

token vendors are headed to the same place mobile data vendors went, this is good for everyone but those who thought they could maintain exorbitant prices

That's not chinese models' fault. Fable and Astra deserve to be punished for their scammy bait-and-switch.

Pretty sure that's Luna xhigh.

I guess that would only be possible if your provider was Xiaomi itself?

There are two (or more) concepts named after the same person:

- Pareto efficiency/Pareto curves: Basically the convex hull of points along the edge of a graph, indicating the best tradeoff between the axes. This is what the post is talking about.

- Pareto principle: this is the 80/20 rule you're talking about

This is the Pareto Front [1], rather than the Pareto principle. It's the idea that anything that's more intelligent is more expensive and anything that's less expensive is less intelligent.

[1]: https://en.wikipedia.org/wiki/Pareto_front

No, Pareto refers to Pareto efficiency https://en.wikipedia.org/wiki/Pareto_efficiency

What you call "frontier line" is also called "Pareto frontier" https://en.wikipedia.org/wiki/Pareto_front

Your description of it is basically correct though

"Pareto" is many things, but here it does indeed refer to the frontier: https://en.wikipedia.org/wiki/Pareto_front