I been using MiMo-V2.5 to do most of my work as software engineer, on a variety of projects I'm working on, and I been VERY happy with ROI. The model is very powerful! Not perfect – I've run in hallucination loops once or twice, but nothing a stop-then-continue wouldn't solve.

The cost is unbelievably low, and the quality of intelligence I get is equivalent to when I was working mostly with Anthropic models (late last year/early this year). I'm fully invested in MiMo and I'm very happy with it.

-- PS: I also check almost daily to see if other models are capable of doing such great work. And they do – DS4F is powerful and DS41 is impressive, GLM 5.3 Flash gets a job done well, etc. – but when I add cost of M-token in the ROI math, Jeez! MiMo is an order of magnitude better.

Well, if open source AI is dangerous (for OpenAI/Anthropic IPOs?), this is like watching a time bomb.

For reference, Mimo-v2.5-Pro scored 19% on DeepSWE 1.1. This is looking great.

Fable scores 70%, Kimi K3 69%, Astra 74% (all on max effort).

https://deepswe.datacurve.ai/blog/deepswe-v1-1

Neat! I've been trying out their next model for the last week, which I assume is a version of this, and it's been a good experience so far.

I had used 2.5-pro for a hefty chunk of development, and found it to work like a somewhat forgetful senior engineer who was new to my project. Very capable, would almost always choose a reasonable option, if not always the best one for the project, and not great at multi-tasking. Generally, made me comfortable not scrutinizing the code line-by-line, but still needed a bit of steering once projects got to a reasonable size.

The next model is a clear step up in the multi-tasking capability at least, with me very rarely having to steer the implementation of a well-defined issue. In terms of code, I found MiMo-V.2.5-pro to be extremely conservative, implementing minimal solutions. The next model seems a little bit more ambitious, in positive ways, making good guesses about gaps/next steps. It also seems to be a fair bit better at design, at least for the little bit I've done, it was good at translating my concepts to practical elements on screen, and cleaned things up nicely as I made suggestions.

This is pretty neat. What would be a good reason for the other Model providers to not do this?

When you run benchmarks while training, isn't that the definition of contamination? Asking because I am not sure if this is normal in big labs now.

2.6 Pro: >started 2026-09-15 10:32 UTC

For some reason I thought training took much, much longer than what the progress bar suggests.

This is really neat, I'm currently using mimo 2.5 pro, and it's decent (or great given the price). Hopefully their next one is multimodal.

Very cool to see the openness here, and likely more like this will come from smaller startups where they win users on transparency.

This is crazy, but sadly anthropic/openai will never do this, what has happened to this world, where chinese companies are more open than US or even EU companies

$5 per second if my eyes don’t fool me. That’s ~$432K per day. Enough to rent 3,000 B300 nodes on Modal.

I didn't know 2 thirds of the training data would be source code.

That's the kind of transparency we need! That DeepSWE benchmark puts it in frontier territory: https://artificialanalysis.ai/agents/coding-agents?coding-ag...

Total run cost is $1.2M until now, what resources are they using to train their model? Wish they shared more details on that and what the MFU metrics are.

I absolutely love that someone is doing this! Why isn’t IBM for Granite or Google for Gemini?

If you are going to develop a near frontier model, and you don’t think you have special sauce up your sleeve, why not making training runs and RL environment scores etc. visible to the world?

I’m genuinely learning quite a bit just from the dashboard

Mino 2.5 has been my workhorse for coder and tester agents (the ones planner agents delegate tasks to)

Wouldn't us observing this break down the model superposition and make it dumber? :)

Hah, it would be great to see more labs pick this up.

Wow, spending money on training an almost-frontier-model is much more time intensive than I thought it was.

Why are they doing this? To try head off accusations about distillation?

"Slow down this much openness in AI or we won't get our trillion dollars valuations!"

Google had this GPT long go and a wise man within Google noted:

"We don't have any maot neither does anyone else."

The AI bubble burst is guaranteed and is only delayed by IPOs.

Very curious that everyone here (so far) seems to assume this dashboard presents real data.

been using the 2.5 mimo for side projects, works amazing

Refreshing, now let's make this a default feature. I imagine a "Upcoming models" list with links to these kind of dashboards.

Can we call this open AI?

"Claude Distill Requests":'hidden'

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The Chinese labs are just making fun of the US labs at this point.

Where is the cool shit from the US labs?

This is so very clearly fake? See the message stating the flash 2.6 flash run was restarted and 0 graphs correlate that restart

Distillation in real-time? Very interesting!

You'd think they would make it less obvious that they are running their whole operation with Claude