Finally a lab that doesn't cheat on the charts

Wow, the chinese labs are getting good at advertising model releases. The moat is thin.

Some features of the release I like:

- Demonstration of diverse tasks, such as using a DAW

- Graphs from various benchmarks and price ranges

- Real world use of the model in scientific environments

This is a big week. Probably getting next OpenAI and Anthro models, Grok 4.7, Mimo, etc. These open source model releases are why I can't take the "slow down" crowd seriously. I pitted older Mimo, qwen, step, gpt-oss, and other models against each other playing games like Werewolf and Sketch.io-like games where I let them talk shit while they played against each other. Mimo was by far pareto frontier of game-playing for the models that were <$0.15/m input tokens on OpenRouter. Qwen was pareto frontier in the shit talking game though. Qwen's hilarious. https://www.tiktok.com/@clankerfights/video/7642862917582425...

This looks great in terms of cost and capabilities, truly pushing the frontier forward in terms of open weight light weight models.

yes. I use opencode and opencode uses Xiaomi as a provider.

Nice catch!

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?

the existence, who else has a live dashboard for the RL late-training?

The best thing they did is being open about all the setbacks they had to deal with. They logged every restart with a reason, talked about dropping a cyber dataset after it degraded coding benchmarks. Also published real time training loss, benchmark scores after every checkpoint and running cost estimates.

Really the only thing missing was dataset descriptions, the dashboard only had random IDs like "dataset-zrso". I guess it's their lawyers fault.

I might turn this into a blogpost if folks are interested, but my god there is so much clever info in that dashboard.

Here is one really neat bit:

A cutting edge training idea (for agents, it's been used elsewhere for ages) is on-policy RL, basically, it's not enough to say "here is an end to end agentic sequence (including tool calls etc.) that is perfect" you want to say "here is a sequence you might actually have generated that turns out to be correct".

Basically, it's more training efficient for models to improve with small tweaks to what they already do than from some perfect oracular answer

(if you've ever tried to teach humans new skills, you’ve probably noticed this too!)

When you do that, you care about how far the model you are updating (improving) has deviated from the one being used to generate rollouts (agentic rollouts for hard problems can take hours with lots of tool calls, so you can't keep redeploying every slight improvement).

Lo and behold, the dashboard literally has:

partial/avg_staleness (likely the measure of how many micro iterations the "generate answers" model is behind the "improving based on the occasional right answer" model)

train_infer_diff/new_infer/kl (a more direct KL divergence based way of measuring how differently the two models generate tokens)

How cool is that?!

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.

[delayed]

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

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

more like 500B in FP8

unified memory, not sure if anyone uses URAM, I human hallucinated it

interesting, peer comment seems to indicate this is a possibility as well, will have to take a deeper look

What kind of throughput do you see on what models?

n-grams can be kept on SSD, no need to hold them in any kind of RAM (at least w/o batching)

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".

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

They mixed up DeepSeek 4.1 Flash with something else on this page, possibly DeepSeek 4.1 Flash means Gemini 3.8 Flash.

Pretty sure that's Luna xhigh.

As for the stats that everyone wants:

MiMo-V2.6-Flash-310B-A15B roughly GPT-5.6 Luna / Claude 4.9 according to benchmarks MiMo-V2.6-Pro-1.02T-A42B roughly GPT-5.6 Sol / Opus 5 according to benchmarks

Perhaps with IQ2 flash will run on 128G M5?

It's an honest, load-bearing, simple thing.-

Thank you guys. I learned something new.

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

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

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

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.

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.

Mind you, Canada was a reliable defense and trade partner before the US' arbitrary trade war and annexation threats.

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.

Also would like to remind how Anthropic CEO is being hostile and blaming Chinese models with distilling

Why wouldn't he? If there really was 25,000 accounts breaking ToS any CEO would at minimum be upset. Evidence of Claude distilling qwen would be damning but that a) makes no sense b) doesn't exist afaik.

Not sure about that.

Given the difference in compute, it seems plausible.

However, the researchers at the US labs are surely no less talented, and they have better access to hire talent globally.

They too have to serve their models efficiently at a large scale, and with current capacity constraints this must be a top priority.

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.

OpenAI reduced prices and Anthropic increased weekly usage limits.

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

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.

Maybe you should not trust any of the benchmarks!

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.

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

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

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.

It means they're good at writing SVGs, in particular SVGs of animals riding modes of transport!

Yeah, I thought the "N" was for Nest

Night 0.8x Usage, 00:00-08:00 -UTC+8

It's because offpeak electricity is cheaper?

Funnily it's perfect if you are in the Pacific Time Zone because you can use it daytime 9am to 5pm

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

Leaning into what it cost to train is hilarious and an obvious shot at US frontier labs spending tens to hundreds of millions or more to train their models.

These benchmark are useless as they don't say whether they were done before or after Fable and Astra got nerfed.

I've got a working recipe to run this model on Dual DGX Spark: https://github.com/volfco/spark-vllm-docker/blob/main/recipe...

Averages ~25-35tok/s which isn't bad for a first attempt.

Funny that all but one video has audio, the house 3D model one, where you can hear (what I assume are) Xiaomi's engineers talking about who knows what.