MiMo-v2.6-Pro: Intelligence, Performance and Price Analysis

Same- I pay $200/mo for Codex but whereas I used to get a week's work out of a weekly limit, now I get roughly 1~2 days.

I've stopped using Astra entirely and remain on Sol orchestrating Luna Xhigh, but it's still not nearly a week's usage for a week's allotment.

And even then, whenever a new model is about to come out, it feels like the model I'm using is being dumbed down substantially.

I have no evidence for this and can have no evidence for this, but I can vote with my wallet regardless.

Sol 5.6 xhigh had been a very reliable workhorse for coding for me via the 200 bucks sub.

But this week they seem to have tweaked the system to a point at which all models (Astra, Sol, Luna) hit rate limits all_the_time without me being anywhere close to the weekly limit.

Early results with MiMo 2.6pro are quite encouraging for anything that's non-UI work so likely switching spend for the time being

Welcome to the dark side. I've been on Kimi, with a little DeepSeek-V4-Pro, GLM 5.2/5.3, and MiMo thrown in, for probably about a year now. It's great here!

For DeepSeek, I recommend their Reasonix harness strongly, due to its alignment to DeepSeek's prefix cache. It means mostly (95%+) cache hit input tokens, so very cheap large-scale code analyses and things that require mega context windows (at the cost of some attentional drift, yes). Reasonix does require that you send data to China.

For most everyday stuff outside of where Reasonix + DeepSeek just makes overwhelming sense, I use OpenCode/Maki/Pi/whatever harness I feel like using today with Kimi K3, via OpenRouter. This does not require sending data to China.

I also use Kimi K3 in Zed via OpenRouter quite a bit, but sometimes like to mix it up with the other models.

For local hardware experiments on my MacBook (128 GB unified memory), Qwen3.6-35B-A3B (speed) and Qwen3.8-27B (intelligence, but slow). As has been widely noted, this amount of unified memory isn't as useful as it seems, due to memory bandwidth and decoding constraints, lack of tensor cores (on the M4 Max, anyway), etc. A giant bag of memory isn't fast, but it'll let you load some impressively big models. The future M5 Studio Macs will continue in this general vein, but will of course be somewhat faster, particularly due to the apparition of tensor cores in the M5 -- Neural whateverApplecallsthem.

The Chinese models are simply _excellent_, and cater to lots of use-cases and tastes.

I have done the same. I hesitated for way too long. I shouldn’t have.

I get way more usage for way less money without any quality or performance degradation. My $200 Codex Pro plan allowance is depleted in 2-3 days. Sometimes Tibo announces a usage reset. But GPT-5.6 models are really not good for coding. Sol has been making increasingly more mistakes in the past two weeks even in the reviewer and advisor roles. Astra is usable for coding but slow and very expensive. In the past two days I’ve used up over 70% on simple copy editing, with dedicated short specs and short sessions. Really little one can do to make it more efficient. Similar work took 20% at most just a month ago. I’m looking to use Astra for milestone reviews/advisory. Perhaps a $100 Pro downgrade will be enough. But my main work is now on open-weight models. And you don’t need to depend on someone to send you a reset. And it’s cheaper by the end of the month too.

With Claude the limits are not even fun anymore - my weekly $100 Max plan quota is gone in one day on merely review invocations, no coding. And my $200 Pro quota is gone in two with some coding. Sonnet 5 is not usable for coding. And Opus 5 tends to always make a couple avoidable mistakes on every task. Fable 5.1 is ok but tends to ignore skills and to work around explicit instructions. Completely canceled all my Claude subscriptions.

With Qwen 3.8, DeepSeek 4.1 Flash, GLM 5.3 I’ve been getting Opus 5-level performance, with less blah blah and no overengineered churn. Public benchmarks are really not telling the real story. The models are more dependable and more predictable. They have their own failure modes. Sometimes DeepSeek 4.1 Flash is quite stubborn but it fails in a good way. Bad for full autonomy - I need to intervene, but it sticks to the rails and instructions - other than Fable and Opus that try to outsmart you and your harness.

Grok is interesting but has been a bit underwhelming on Grok plans - my SuperGrok allowance is depleted in a single session overnight. SuperGrok+ gives more but it’s still about the same as with OpenAI, Claude is way less now.

Since the allowance volume has been shrinking with the major model providers, to me, open-weight alternatives are really necessary now to at least maintain the momentum and budget.

But at work it’s really an uphill challenge - it’s become impossible to convince the tech leadership once they got hooked on Anthropic. They No facts will help. Some people underestimate how expensive Claude really is after getting used to the subscription plans with allowance resets. OpenAI models are expensive too.

> and intelligence appears to be markedly declining

Serious question: does anyone have evidence of this?

It’s something that’s constantly asserted, and has been since 2023. Every time someone posts a site that tries to track this though, I look at it and it’s just a flat line.

They seem to have tweaked things this morning maybe (in response to complaints?) and there's noises from Tibo about a reset coming today.

The open model launches and competition generally definitely seem to light a fire under their asses.

Why does Luna have a score of 0? I will say that in my limited experience, I use Luna and DSv4 Flash (haven't tried 4.1 yet) and Luna is wayyyy faster. They both output at the same speed but DS has an endless thought process

Speed seems to vary a lot with demand. Last night it was reaching 80+ tok/s

That looks like you aren’t using the ultra speed endpoint.

Interesting - have you done MiMo-v2.6-flash?

The main AA benchmark keeps changing, and had to be radically changed when Astra came out and showed zero improvement over GPT 5.6 Sol in their benchmark. Opus 5 is still 1 point ahead of Fable 5.0 on the index, if you manually add Fable 5.0 back into the list, so it hasn't actually been "corrected". It's only Fable 5.1 that is shown as ahead of Opus 5.

The AA benchmark is a weighted average of other benchmarks and some internal ones. I think the difficult part is finding benchmarks that reflect your own use of the models.

> It feels suspicious that MiMo-V2.6 Pro gets 46 in de index while DeepSeek-V4.1 gets 39.

Why?

All of the chinese labs have been overfitting on benchmark data to game the results for a while now - MiMo and Deepseek are not anywhere near frontier and mostly compete with models like Luna - which they are still worse than.

There isn't much compelling reason to use these unless you are just averse to giving money to openai/altman. A $20 codex sub gives you ~$150 of luna use per weekly limit, while there isn't any good subsidized options for chinese models at all (and the few who were subsidizing, like opencode, rugpulled by reducing monthly limit to $60 to $15 with no notice to users).

DS 4.1 is good but it’s clearly not as “smart” as non-flash models- it just doesn’t have the training data. Without a solid plan, it goes off the rails pretty regularly.

Hover the #1/114 and it shows only open weights, that's probably why

Was going to say the same thing. The announcement blog[1] has the flash benchmarks. AFAICT Artificial Analysis has not added it yet though.

Interested to see if it also beats DeepSeek V4.1 Flash.

[1]: https://mimo.xiaomi.com/mimo-v2-6

I have seen them say "and reasonably priced when comparing to other models of similar price" on several models. I always get a kick out of it.

Nowadays these error can be a good thing :)

Human error means this wasn't just stopped together by some bot.

That is the RL training cost only. Their announcement blog mentions this: https://mimo.xiaomi.com/mimo-v2-6#scaling-rl-fully-open-sour...

My understanding of tech salaries in China is that they are pretty decent, but not as high as in SF; closer to typical European salaries.

Mostly due to lower cost of living; Shenzhen is way cheaper than SV

I sorta got the impression that the $3.47 million only covered post-training , given that few of the graphs start at zero. Is a barely-trained model going to score 48 on DeepSWE v1.1 ?

https://mimo.xiaomi.com/rl/

the graph has a dropdown for selecting models