OpenAI usage limits have been severely cut, and intelligence appears to be markedly declining, so I'm going to start trying these Chinese models seriously now. I don't mind if it takes longer. I just need the intelligence to predictably work the same way from day to day.
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.<p>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.<p>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.<p>I have no evidence for this and can have no evidence for this, but I can vote with my wallet regardless.
see that's interesting, because I'm prompting all day and I usually end up with 15-25% by the end of the week. I'm using Astra xhigh exclusively
I strongly agree. Check out the Codex subreddit. Many empirical examples of Astra silently downgrading the models. One found Astra was silently using Luna Max (but still billing for Astra).<p>Even when I try to stick with Sol X/High, my limits are at best half of what they were before Astra launched, and the intelligence has declined markedly.<p>I cancelled my $100 plan. This is absolutely absurd and frankly unusable now.
Sol 5.6 xhigh had been a very reliable workhorse for coding for me via the 200 bucks sub.<p>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.<p>Early results with MiMo 2.6pro are quite encouraging for anything that's non-UI work so likely switching spend for the time being
> and intelligence appears to be markedly declining<p>Serious question: does anyone have evidence of this?<p>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.
Why is it labelled as #1/114 for artificial intelligence (at the top of the page) but if you scroll down and look at the graphs it's obviously not?
It feels suspicious that MiMo-V2.6
Pro gets 46 in de index while DeepSeek-V4.1 (<a href="https://artificialanalysis.ai/models/deepseek-v4-1-flash" rel="nofollow">https://artificialanalysis.ai/models/deepseek-v4-1-flash</a>) gets 39. According to the appendix at the bottom of <a href="https://mimo.xiaomi.com/mimo-v2-6" rel="nofollow">https://mimo.xiaomi.com/mimo-v2-6</a> the deepseek model sometimes surpasses mimo and it's not so far behind in capabilities. A week ago opus 5 appeared 1 points ahead of fable 5 despite fable being a much smarter model (this has been corrected already)
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.<p>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.
The way Artificial Analysis keeps changing their weights feels kind of like deciding who the winner should be and making the weights reflect that. They’ve been changing their weights to add more weight to improved long-running agentic capabilities, but doing so means they’re reducing the relative importance of world knowledge and of writing ability.<p>I’ll grant that maybe world knowledge isn’t that important for these models. But writing ability is important for human understanding, and I think the weird turns of phrase and word choices reflect the labs’ underweighting of the importance of human understanding.
[dead]
> It feels suspicious that MiMo-V2.6 Pro gets 46 in de index while DeepSeek-V4.1 gets 39.<p>Why?
Mimo2.5 is really good, but tended to loop too much for my taste. Locally, Pro2.5 wasn't much better. I would reach for it for one shots, hopefully they sorted it out with v2.6, it's a model that's slept on by many. I found that most people that used it did so because it was free. It's a top model worth exploring if you have never given it a go.
It is an impressive model. Agreed on most that is written on this page, with the exception of it being fast. I ran it on my own LLM benchmark suite[1] and it is faster than DeepSeek but still much slower than leading models. But it's pricing is where it really shines.<p>KillSwitch-Bench 1.0<p><pre><code> Claude Opus 5 66.9
GPT-6 Astra 57.9
Claude Fable 5.1 46.7
MiMo-V2.6-Pro 38.8
Muse Spark 1.3 36.5
</code></pre>
1 - <a href="https://bench.killswitch-lang.org/" rel="nofollow">https://bench.killswitch-lang.org/</a>
where's the flash model? it's out already isn't it
"When evaluating the Intelligence Index, it generated 140M tokens, which is somewhat verbose in comparison to the median of 140M."
Per Xiaomi, MiMo v2.6 training run cost $3.47m. A far cry from the estimated costs ($100m+) for the Big 5 (MSL, xAI, GDM, OAI, Ant). I wouldn't be surprised if salaries and R&D costs have similar drastic disparities.<p>For a model that matches <i>Muse Spark 1.3</i> in benchmarks, <i>MiMo v2.6 Pro</i> is incredibly cheap, given its cache rates will remain $0.0036 per million.
That is the RL training cost only. Their announcement blog mentions this:
<a href="https://mimo.xiaomi.com/mimo-v2-6#scaling-rl-fully-open-sourced" rel="nofollow">https://mimo.xiaomi.com/mimo-v2-6#scaling-rl-fully-open-sour...</a>
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.<p>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 ?<p><a href="https://mimo.xiaomi.com/rl/" rel="nofollow">https://mimo.xiaomi.com/rl/</a>
Why sol is not in the comparison?