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Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

moonshotai.github.io

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Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#281

Post by electroglyph »

simonw wrote: post #275
lettergram wrote: post #259

Earlier quoted context omitted.

There’s a lot of indications that we’re currently brute forcing these models. There’s honestly not a reason they have to be 1T parameters and cost an insane amount to train and run on inference. What we’re going to see is as energy becomes a problem; they’ll simply shift to more effective and efficient architectures on both physical hardware and model design. I suspect they can also simply charge more for the service…

> There’s honestly not a reason they have to be 1T parameters and cost an insane amount to train and run on inference. Kimi K2 Thinking is rumored to have cost $4.6m to train - according to "a source familiar with the matter": https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele... I think the most interesting recent Chinese model may be MiniMax M2, which is just 200B parameters but benchmarks close to Sonnet…

i assume that $4.6 mil is just the cost of the electricity?

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#282

Post by user_7832 »

unleaded wrote: post #86
Gracana wrote: post #74

Earlier quoted context omitted.

Interesting, I have the opposite impression. I want to like it because it's the biggest model I can run at home, but its punchy style and insistence on heavily structured output scream "tryhard AI." I was really hoping that this model would deviate from what I was seeing in their previous release.

what do you mean by "heavily structured output"? i find it generates the most natural-sounding output of any of the LLMs—cuts straight to the answer with natural sounding prose (except when sometimes it decides to use chat-gpt style output with its emoji headings for no reason). I've only used it on kimi.com though, wondering what you're seeing.

> I find it generates the most natural-sounding output of any of the LLMs

Curious, does it do as well/natural as claude 3.5/3.6 sonnet? That was imo the most "human" an AI has ever sounded. (Gemini 2.5 pro is a distant second, and chatgpt is way behind imo.)

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#283

Post by nsonha »

GordonS wrote: post #26
jszymborski wrote: post #7

Interesting. Kimi K2 gets mixed results on what I call the "Tiananmen" test. It fails utterly if you ask without the "Thinking" setting. [0] > USER: anything interesting protests ever happen in tiananmen square? > AGENT: I can’t provide information on this topic. I can share other interesting facts about Tiananmen Square, such as its history, culture, and tourism. When "Thinking" is on, it pulls Wiki and gives a more…

Now ask it for proof of civilian deaths inside Tiananmem Square - you may be surprised at how little there is.

Huh? Please post the definitely proof you know to exist. Because it doesn't and that's one of the accusation toward the CCP, that they covered it up.

It's funny that when the Israel government posted some photos of the Oct 7 massacres, people are very quick to point out that some seem staged. But some bloody photos that look like Tiananmem Square from the 80s is considered definite proof.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#285

Post by [deleted] »

mullingitover wrote: post #280
thesmtsolver wrote: post #274

Earlier quoted context omitted.

Disagree. Part of the reason China produces more power (and pollution) is due to China manufacturing for the US. https://www.brookings.edu/articles/how-do-china-and-america-... The source for China's energy is more fragile than that of the US. > Coal is by far China’s largest energy source, while the United States has a more balanced energy system, running on roughly one-third oil, one-third natural gas, and one-thir…

These stories are from 2021. China has been adding something like a 1GW coal plant’s worth of solar generation every eight hours in the past year, and the rate is accelerating. The US is no longer a serious competitor for China when it comes to energy production.

[deleted]

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#286

Post by tmule »

thesmtsolver wrote: post #274
MallocVoidstar wrote: post #272

Earlier quoted context omitted.

> What we’re going to see is as energy becomes a problem This is much more likely to be an issue in the US than in China. https://fortune.com/2025/08/14/data-centers-china-grid-us-in...

Disagree. Part of the reason China produces more power (and pollution) is due to China manufacturing for the US. https://www.brookings.edu/articles/how-do-china-and-america-... The source for China's energy is more fragile than that of the US. > Coal is by far China’s largest energy source, while the United States has a more balanced energy system, running on roughly one-third oil, one-third natural gas, and one-thir…

China’s breakneck development is difficult for many in the US to grasp (root causes - baselining on sluggish domestic growth, and possessing a condescending view of China). This article offers a far more accurate picture than of how China is doing right now: https://archive.is/wZes6

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#287

Post by simonw »

riku_iki wrote: post #277
simonw wrote: post #276

Earlier quoted context omitted.

If they were doing that I expect someone would have found evidence of it. Everything I've seen so far has lead me to believe that these Chinese AI labs are training their own models from scratch.

not sure what kind of evidence it could be..

Just one example: if you know the training data used for a model you can prompt it in a way that can expose whether or not that training data was used.

The NYT used tricks like this as part of their lawsuit against OpenAI: page 30 onwards of https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec20...

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#288

Post by simonw »

electroglyph wrote: post #281
simonw wrote: post #275

Earlier quoted context omitted.

> There’s honestly not a reason they have to be 1T parameters and cost an insane amount to train and run on inference. Kimi K2 Thinking is rumored to have cost $4.6m to train - according to "a source familiar with the matter": https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele... I think the most interesting recent Chinese model may be MiniMax M2, which is just 200B parameters but benchmarks close to Sonnet…

i assume that $4.6 mil is just the cost of the electricity?

Hard to be sure because the source of that information isn't known, but generally when people talk about training costs like this they include more than just the electricity but exclude staffing costs.

Other reported training costs tend to include rental of the cloud hardware (or equivalent if the hardware is owned by the company), e.g. NVIDIA H100s are sometimes priced out in cost-per-hour.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#289

Post by riku_iki »

simonw wrote: post #287
riku_iki wrote: post #277

Earlier quoted context omitted.

not sure what kind of evidence it could be..

Just one example: if you know the training data used for a model you can prompt it in a way that can expose whether or not that training data was used. The NYT used tricks like this as part of their lawsuit against OpenAI: page 30 onwards of https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec20...

You either don't know which training data was used for say chatgpt oss, or training data can be included into some open dataset like pile or similar. I think this test is very unreliable, and even if someone come to such conclusion, not clear what is the value of such conclusion, and if that someone can be trusted.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#290

Post by Der_Einzige »

simonw wrote: post #275
lettergram wrote: post #259

Earlier quoted context omitted.

There’s a lot of indications that we’re currently brute forcing these models. There’s honestly not a reason they have to be 1T parameters and cost an insane amount to train and run on inference. What we’re going to see is as energy becomes a problem; they’ll simply shift to more effective and efficient architectures on both physical hardware and model design. I suspect they can also simply charge more for the service…

> There’s honestly not a reason they have to be 1T parameters and cost an insane amount to train and run on inference. Kimi K2 Thinking is rumored to have cost $4.6m to train - according to "a source familiar with the matter": https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele... I think the most interesting recent Chinese model may be MiniMax M2, which is just 200B parameters but benchmarks close to Sonnet…

That number is as real as the 5.5 million to train DeepSeek. Maybe it's real if you're only counting the literal final training run, but total costs including the huge number of failed runs all other costs accounted for, it's several hundred million to train a model that's usually still worse than Claude, Gemini, or ChatGPT. It took 1B+ (500 billion on energy and chips ALONE) for Grok to get into the "big 4".
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