by Alifatisk
4 subcomments
- > Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and prioritizing compute for current members. Existing subscribed users are not affected.
Such a beautiful paragraph to read, a company that prioritizes their current customers and focus on keeping them satisfied instead of just focusing on fast growth.
by thevinter
6 subcomments
- 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.
by impossiblefork
3 subcomments
- 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.
by abalashov
1 subcomments
- 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.
- 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
- 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
- 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?
by himata4113
6 subcomments
- I wonder if anthropic and openai will remain relevant simply due to the fact that they're the only ones that are able to handle this much demand for the forseeable future? My bet would be that companies would probably not be too happy with employee time being wasted on outages and other related issues when it already costs so much.
by comandillos
3 subcomments
- Oh, so they’ve decided to suspend subscriptions because they know they might not be able to guarantee a minimum quality of service to their customers?
by noisy_boy
1 subcomments
- Damn some timing, I just signed up few hours ago and within 3-4 hours spent over 50 bucks on my personal project (didn't realize that the launcher script I copied from an example had set effort to max). This is API use that consumes from your recharge amount. It found tons of issues with the code generated by Sonnet 4.6 (yeah I know, older model). Also didn't seem to burn through context window that fast (1M). The generated code works but quality could be better. Works fine with Claude code cli. The standout aspect was strong analytical abilities and a much more sobering and cautious tone (which is a good thing).
Will have to watch cost carefully though. If only I could get it at DeepSeek adjacent prices.
by Curtis_Guan
0 subcomment
- Early in the morning on the day Kimi K3 was released, I tried using Multi-Agent to execute a relatively complex research report. The whole process took five hours, involved 12 roles, and ultimately produced a 54,000-word report. I read through it carefully, and my feeling is that it could directly serve as an outline for a book or a documentary. I also observed the writing process—especially the repeated verification of potentially inaccurate data sources, as well as the reminders and adjustment suggestions regarding certain platforms. In short, I am very satisfied with this report, and I will use it as a reference for the subsequent documentary.
- I tried it through siliconflow (I have no relationship to them other than as a normal customer) with the command line Pi agent and it's pretty good at adding features to a rust lang code repo. Compared to Deepseek V4 Pro there, M3 is about 3X the cost for non-cached input tokens and 5X the cost for output tokens. (both offering a 1M token context window). It certainly wasn't worh 5X the output of the Deepseek model for my uses, though.
I couldn't find any info on how they (siliconflow) quantized the model. I think some of the other ineference service aggregators might offer it as well, already.
- Kimi k3 is noticeably less censored than anthropic models
by Departed7405
0 subcomment
- Just open-source it already then ?
- Currently using their 100 USD subscription, considering going up to their 200 USD subscription, because the model is approximately as capable as Opus (maybe sometimes better, sometimes worse) but less annoying to use in practice, a bit less of slop phrasing: https://blog.kronis.dev/blog/ai-slop-is-a-self-inflicted-tra...
The token limits do feel a bit less than I get with Anthropic Max 5x, but maybe that's because I've mostly been running it on Max reasoning (oh and Anthropic is also temporarily boosting the limits, who knows, it's hard to keep track of all of this stuff exactly) and there's plenty of tasks where High is still close enough in performance. The token limits still feel a bit more generous proportionally to the price compared to what I got when trying out the 65 USD tier of GLM Coding Subscription with GLM 5.2, and that was with the ZCode usage discount as well, though I did enjoy that harness.
Plus, if I decide to go with Kimi's annual pricing, then it'd come out to only around 159 USD per month or 139 EUR per month, which is really good and pretty close to what I pay Anthropic anyways: https://www.kimi.com/help/membership/membership-pricing
- Shooting for the moon and then be surprised when you actually land there.
I hope they wire up new hardware quickly to handle the demand.
- another reason why it’s great to have open weights: inference providers and your own hardware can ensure that you get the tokens you need even if a lab itself hits roadblocks (and there have been many in quality and availability)
- They could've simply cut the usage limits, removed K3 from the plan, added a weekly cap, and called it "fair usage." Anthropic has been running that playbook for a while now, and they're a trillion-dollar company.
- I already have a small sub. I think I can still upgrade/extend it, which is great.
by charcircuit
1 subcomments
- The rate limits on the $20 plan go so fast that it's easy to use my weekly budget in only a couple days. I can't imagine also trying to use K3 on the plan.
Back in January with K2.5 it felt like I could use it a lot more.
by gyanchawdhary
0 subcomment
- [dead]
- [flagged]
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