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.<p>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.<p>--
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.
> late last year/early this year<p>That's an eternity when it comes to coding models.<p>In my personal experience, we've had almost a step change every ~3 months this year, at least for bigger one-shot tasks. For example looking at Gemini Flash 3.0 vs 3.5 vs 3.8, it went 5% -> 30% -> 75% on DeepSWE, all since the start of the year.
In one sense you're right.<p>In another one, Opus 4.6 level already solved 90% of my work-day tasks, so while better models have been instrumental into handling a higher % that does not mean that defaulting on cheaper models can't be good.<p>I run DS 4.1 flash daily, and then cross check with gpt-6-astra and I've nuked 90% of my AI monthly bill while having higher limits and better performance/intelligence than I did just at the beginning of this summer.
tbf, I the happiest I've been working with claude is late last year/early this year (before March)...
That's because Opus 4.6 was the last good assistant model.<p>Everything after it might be more "intelligent" but is super tuned around end-to-end task (and related benchmarks), not to act as an assistant.<p>Now it's *you* being the assistant, reviewer, etc.
Same here. It’s the first AI provider I actually gave money to, since they offered the model for free with a Mimo code for the first month or so, and it was great.<p>These days, there are more intelligent models like DS4.1, but Mimo is very obedient, so I plan things with another model and give the implementation to Mimo.
I’ve been very pleased with DS 4.1 flash. Not so much the 4.0 models, but for coding (Rust) it’s been great so far (3 solid days of work).<p>I’ll give Mimo a try.
MiMo is my backup whenever DeepSeek is down, had the price bump, is slow, etc.<p>UltraSpeed was absolutely awesome. I miss it.<p>DS 4.1 Flash is amazing. Well worth the extra cost.
I am also using 2.5 and it is giving me solid results. Its available free on Openrouter
I've found that mimo v2.5 works for very basic things like a python script to do one thing, but it also is very 'dumb' compared to qwen 3.8-flash-next (I think the benchmark scores for terminal and coding specific benches back this up). And definitely not in the same class as like a GLM5.2 or 5.3. It's <i>fast</i> but makes basic mistakes that only get caught later.
May I ask why you ended up there instead of just using the heavy subsidized subscription. I’m actually curious.
Could you elaborate on how you check daily? Do you swap models for certain tasks?
2.5 Pro or the regular 2.5?<p>I always found that those Mimo models to be really good at tool calling and following instructions
How does it compare with DS 4.1 Flash in your experience, if you ignore the cost?
> <i>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.</i><p>API may be expensive, but I do 900m tokens (95% cached, ~0.4% output) on Z.ai's $18/mo coding plan with <i>GLM 5.3 Flash</i>.
I wouldn't call that inexpensive.<p>For comparison, I am currently at 6.6B tokens, 95% of monthly quota on a 10$ command code plan, mostly using DeepSeek flash 4.1, or some of the free models for easier tasks.
How fast is it compared with the other Chinese models?
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Yeah, this Brazilian dude who has been a contributor here on HN longer than your anonymous account is shilling for a Chinese model company. Makes sense.
Are you accusing them of astroturfing? Why is it strange for someone to say something topical?
Well, if open source AI is dangerous (for OpenAI/Anthropic IPOs?), this is like watching a time bomb.
the open burial started when zAI served their latest model on all Chinese chips.<p>now we r just noticing the grave getting dug deeper.
For my own usage, Luna is cheap enough that I don't care if other models are cheaper. I'm interested if another model is in some way better and not too expensive.
Luna is great but makes a lot of mistakes at high and lower in my experience (large rust codebase). I use Luna Max for asynchronous subagent reviews and am very happy with its work, but it’s slow af.
From my experience DS 4.1 flash is a much more capable model than luna.
What plan are you on?<p>Trying to understand why users are using Luna when Sol seems essentially unlimited on the pro plan. Unless you have jobs running 24/7.
Plus plan. For professional use, $100/month would be ok but it’s rather steep for hobbyist use.
Sol is useless atm on the Plus plan, 1-2 questions 5-10m to get through the 5h allowance. (used to be good, can change any day)
I think it might be closer to this:<p><a href="https://www.debtdefaultclock.us/" rel="nofollow">https://www.debtdefaultclock.us/</a>
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.<p>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.<p>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.
For reference, Mimo-v2.5-Pro scored 19% on DeepSWE 1.1. This is looking great.<p>Fable scores 70%, Kimi K3 69%, Astra 74% (all on max effort).<p><a href="https://deepswe.datacurve.ai/blog/deepswe-v1-1">https://deepswe.datacurve.ai/blog/deepswe-v1-1</a>
Also worth taking a look at is the mimo harness. It's a fork of opencode with some new modes added for long horizon tasks. One of the better open harnesses out there at the moment.
2.6-pro just reached 63.7% by step 10, it's on step 11 right now.<p>Even flash reached 60.7% by step 12, and it's on step 16 now.<p>This is so exciting lmao.
gemini 3.8 flash is also 74% and google just started letting all their engineers use claude...go figure
> and google just started letting all their engineers use claude<p>That's misleading.<p>1. Having different models available is useful for A/B testing and helping improve Gemini itself.<p>2. They have an enterprise offering for Antigravity (their agentic coding platform), and they need to test that it works well with non-Gemini models too.
This is pretty neat. What would be a good reason for the other Model providers to not do this?
Speculating here, but I assume researchers can make a reasonable estimate of the size of closed models based on factors like training time, training speed, and the number of tokens processed.<p>Also, Anthropic and OpenAI probably want to keep each other on their toes so they don’t end up on the wrong side of another Opus 4.6 / GPT-5.3-Codex situation, where one lab releases a model only for the other to drop a better one hours later.
I don't really remember a situation, which of those models supposedly beat the other?<p>I still opus 4.6 though not for code
I think first of all it’s not an obvious idea, also the marketing surplus for other providers is not as big for openai/anthropic as for xiaomi and last but not least I’m pretty sure you can withdraw methodology from here.<p>I’m saying who has a million dollars for me, so I can make my own model?
this is remarkable transparency in an otherwise hyper competitive and secretive industry
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.
They run one step/iteration on an additional chunk of training data, then use the snapshot of the weights after that iteration in a separate validation benchmark while continuing to train on another chunk of data for the next iteration.<p>They result of the benchmark does not feed back into the training, it simply serves to provide a measurement of progression over time.
Kinda yes. The benchmarks become part of the validation set, which means the models get slightly overfit to them if they are used as criteria for stopping the training. But a lot less compared to using them in the training data.<p>I'd guess everybody uses at least some benchmarks as stopping criteria, which is kinda sensible, but it also does induce some benchmaxxing, and explains partly why the newest models always tend to eke out in benchmarks.<p><a href="https://en.wikipedia.org/wiki/Training,_validation,_and_test_data_sets" rel="nofollow">https://en.wikipedia.org/wiki/Training,_validation,_and_test...</a>
They are using it to evaluate checkpoints during the training, they are probably not using the benchmarks for training the models. It's a common practice for big reinforcement learning runs.
They exist to detect degradation. Datasets are not perfect and if a batch contains too much bad data it can ruin a run, also an opportunity to find bad data and improve the dataset filtering.
You gotta have something to aim at. And, presumably, the benchmark is not part of the training data, it is the test against which the model is tested at each stage; is behavior moving in the right direction?
Not if you don't train against them.
Very cool to see the openness here, and likely more like this will come from smaller startups where they win users on transparency.
2.6 Pro: >started 2026-09-15 10:32 UTC<p>For some reason I thought training took much, much longer than what the progress bar suggests.<p>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.
I didn't know 2 thirds of the training data would be source code.
that is the the "data used to improve the model" when signing up for the subscription plans
this is the rl run, not the pretraining run
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.
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?<p>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?<p>I’m genuinely learning quite a bit just from the dashboard
Wouldn't us observing this break down the model superposition and make it dumber? :)
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?
I don’t see how it would head off such accusations. This is post-training, and even it’s data could be pulled from other models or run against other models in realtime. Not saying that’s the case, just that the dashboard does not disprove.
Sometimes you're confident about what you're doing and show how you work to the world.<p>Keeping the garage door open, or at least making the door translucent. It's always cool.
That China's official policy is now to prefer open models and open model development may be a part of it.
BRICS just had a New Delhi meeting where Xi pushed a 5-point plan on AI cooperation that centered on open source models
With that policy in place, labs might be incentivized to be creative in their openness. This being fun/free PR
Bottom of the page says "Open is what we value."
Hah, it would be great to see more labs pick this up.
Mino 2.5 has been my workhorse for coder and tester agents (the ones planner agents delegate tasks to)
Refreshing, now let's make this a default feature. I imagine a "Upcoming models" list with links to these kind of dashboards.
"Slow down this much openness in AI or we won't get our trillion dollars valuations!"<p>Google had this GPT long go and a wise man within Google noted:<p>"We don't have any maot neither does anyone else."<p>The AI bubble burst is guaranteed and is only delayed by IPOs.
Nothing is guaranteed.<p>Open models have not yet caught up with February's Mythos checkpoint.<p>Meanwhile OpenAI is solving millennium problems, and their compute is still fully utilized.
I wonder what we will do with the discarded data centers and its hardware..
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
That's the kind of transparency we need! That DeepSWE benchmark puts it in frontier territory: <a href="https://artificialanalysis.ai/agents/coding-agents?coding-agents-performance-chart=deep-swe-v1.1" rel="nofollow">https://artificialanalysis.ai/agents/coding-agents?coding-ag...</a>
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.<p>Where is the cool shit from the US labs?
With other software, devs convince their managers of the importance of using open source stuff in their stack. With AI, it's usually managers choosing what models to use for the devs. The US labs don't need to give a damn how much devs like open source
I was dev, and now I am Senior level manager.
Open Weight models are current main focus for many companies with full alignment with top management for very simple reasons:
- stable and predictable performance (no pre-launch models degradation)
- ability to tune them for specific business cases (though still rare tbh)
- better (at least 60% Opus vs Kimi (real,3rd party)) and more competitive pricing
- flat pricing if tokenusage is big enough to justify renting GPU
- decent quality
- much higher guarantees that data will not be sent somewhere (assuming 3rd party inference providers)
- and cherry on top: flat and minimal pricing with absolute confidentiality using Alibaba Apsara stack of recently released AMD Instinct Coder box[1]<p>[1] <a href="https://www.amd.com/en/ecosystem/oem/supermicro/amd-instinct-coder.html" rel="nofollow">https://www.amd.com/en/ecosystem/oem/supermicro/amd-instinct...</a>
This isn't about liking open source. This is about the labs just being cool and doing cool shit instead of the opposite which is Anthropic where all they talking about is killing everyone and taking everyone's job.
These labs are still (for the time being) made of people, who reflect their lives onto the work.<p>The US population is much more pessimistic and doomsday driven these days, whereas the Chinese are more optimistic and future driven.
> The US labs don't need to give a damn how much devs like open source<p>In the short term, true.<p>In the long term, unknown but typically when you hold progress that way while other countries don't you at best end up becoming siloed while the rest of the world continues on without you.
You mean all of the frontier models that the Chinese distillation clones are copying? Yeah kinda cool imo. If a dashboard showing training for a model that doesn't even come close to anything us labs have released in 6 months is "cool", then you're a loser
Hahaha. Is that Sam or Dario with throwaway account. This sounds like calling social security, a free handout. Who distills the distillaters? Get it?
Why the fuck would you or I care about that?<p>Anthropic and OpenAI literally stole from every human in history and youre out here complaining that the Chinese are distilling models and releasing them to the public?<p>Why do you care?
No crying in the copyright casino.
Distillation in real-time? Very interesting!
This is so very clearly fake? See the message stating the flash 2.6 flash run was restarted and 0 graphs correlate that restart
You'd think they would make it less obvious that they are running their whole operation with Claude
If you're thinking of the UI style, definitely not Claude. It is incapable of writing a clear sentence like "what each step's samples are made of", would have used all-caps for everything, more padding and gradients.
They'd be running in the red then cause they charge way less than Claude. Sorry but it just doesn't make logical sense. They have open source, papers, and self hosting too
It's not obvious to me. What's the tell?
Did you come here to astroturf or are you a big fan of Claude
Do tell cause I can't