Moonshot AI Pauses New Subscriptions as Kimi K3 Demand Surges

Personal anecdote: I exhausted my Claude usage yesterday so I decided to spend 20$ to try Kimi while I was at it. Logged in, paid, downloaded Kimi Code, set it to use K3 and prompted something along the lines of: "Check this repository and find all the settings that can be passed as input related to hardware, I/O, thread control or networking. Produce a report".

It thought for about 12 minutes and then told me I had exhausted my daily quota. (The next day Fable did the same task in 3m)

If you want to buy a plan for K3 do NOT buy the 20$ one.

I think the Kimi thing is super cool, especially that they have so many RNN/linear attention layers (3x more than they have full attention). I haven't yet tried it though. It seems like it would be extremely reasonable for long context tasks and I guess this fits the times.

I suspect that the reason it has so many parameters is the same reason that compute optimal xLSTMs have some many parameters, and the success of this model makes me a bit unhappy that we haven't gotten an xLSTM-style model of huge size developed in Europe.

Obviously these guys are very pragmatic, they're probably not committed to anything other than what works on their internal evaluations, so they still have ordinary attention layers in the model and so on, and one can't be guaranteed that the people who come up with a good model then do the engineering in an ideal way, but I still think the success of Kimi shows what could have been if we had enough big supercomputers for LLM training and made them available to the right people-- because this is basically Hochreiter's thing. It's RNNs, or well, mostly RNNs.

I've been using Kimi for coding tasks for close to six months now, and haven't looked back. I'll periodically try something on Claude to make sure I'm not missing anything, but I've been very happy.

I just do the OpenRouter thing. My use of LLMs is narrow enough that cost is a negligible consideration either way.

A breath of fresh air to pause new subscriptions rather than the Google approach of quietly nerfing the limits and hoping you don't notice you're getting less value for your monthly/annual subscription.

Limits may change without notice, including due to capacity constraints. When there’s a large increase in activity in Gemini Apps, we may change limits to maintain a high standard of quality. [1]

[1]: https://support.google.com/gemini/answer/16275805

In our multi-agent game coding evaluations, we usually see Chinese models struggle in one-shot reasoning but make up for it with tool use and iterating towards better solutions. Kimi K3 follows that pattern, ranking 19th in one-shot coding and 3rd in agentic coding (where the model gets a harness and tools and many calls to iterate toward better code). Only Sol and Fable have better average agentic coding submissions.

Agentic coding is what's most relevant to software engineers, but so is speed, which is a real usability issue right now. Third-party inference providers like Fireworks have bridged the gap for some previous releases.

It's exciting to see the open weights frontier becoming the norm. Competing on having the frontier model is going to be an increasingly difficult business.

Data at https://gertlabs.com/rankings?mode=agentic_coding

Ive been pleasantly surprised with the quality of this model. Its really good at code review and PR review. But right now due to them being so overloaded + this being a big model, its SO slow. It takes forever to do a relatively simple code review.

Curious if this kimi moment is net growth (jervons paradox - abundance = more consumption) or just people shifting to cheaper model

Is there a good source for total token consumption between the various labs / openrouter / etc?