Xiaomi MiMo v2.6

They invested billions into training the models but there is no competitive advantage, we see that within a couple of months everyone catches up. There is no way to profitability unless they get some policies to shields them against competitors that can't comply with the regulatory requirements.

That is also why there are things like Claude, Codex and Cursor. They are trying hard to build a customer relationship with a higher switching cost that hopefully sticks.

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.

What's different today is that now all the big LLM firms want to slow down AI development. When men like Musk and Altman (both known for habitually shooting their mouths off and saying whatever they need to whoever needs to hear it regardless of truth) suddenly agree with Amodei, that's when things start to smell off.

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 to improve models with small tweaks to do more of the right thing they are already doing sometimes than from some perfect oracular "this is the way" 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?!

And don't get me started on the clever ideas hiding behind dynsam/avg@n ...

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

And later they further cut Sol and Terra pricing by 20% (maybe only in the API) and Luna by 80%.

In fact Luna still outperformed DeepSeek Flash 4.1 in cost per task on Artificial Analysis when I last checked.

However, Luna is slightly less intelligent. I have a feeling that it's pretty dumb and prone to hallucination unless running at xhigh or max effort, where it somehow manages to work quite well.

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

The competition is great, and I hope Chinese models will continue to force leading US labs to offer models at a low price point.

That said, I don't think the Chinese labs have anything over OpenAI and Anthropic when it comes to capability or efficiency - I have no reason not to believe the US labs have even lower cost to serve the models.

OpenAI reduced prices and Anthropic increased weekly usage limits.

[1] https://sebastianraschka.com/llm-architecture-gallery/per-la...

[2]: See DS 4.1-Flash and Qwen-3.8-Next.