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>> I pretty sure OpenAI and Anthropic are doing the same or worse. So in your opinion, they are training on your data even if you toggle the "don't train on my data" checkbox off? That's a bold assertion.
Just in case you were thinking of signing up directly with Moonshot to use the service, they appear to train even on API use: > We may use Content to provide, maintain, develop, support, and improve the Services, comply with applicable law, enforce our terms and policies, and keep the Services safe and secure. Customer who requires restrictions on the use of Customer Content for training or improving Moonshot AI mode…
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These benchmark numbers are insane. The days when China was 6 months behind are over? How are they doing this with so much less resources than the US??? I have so much respect for the researchers there
Mythos/Fable-class models have been around for at least 4 months internally in the US, and Kimi still isn't quite there, so I'd say the 6-months is still about right.
Some official benchmark numbers posted in Chinese social media (I am sure they will publish an English blogpost later too): https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ Generally looks like a Sol/Fable tier model, better across the board than Opus 4.8. (Edit) English blogpost is up now: https://www.kimi.com/blog/kimi-k3
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Kimi doesn't do well on my "ask a trivia question that other AIs get wrong" test. The question it came up with, "which U.S. state is closest to Africa?" is a pretty standard trivia question without any reason to believe other AIs would get confused. https://pellmell.ai/s/dccdeca69f929f79bc89317035610049 Even GPT-OSS-120b gets this right: https://pellmell.ai/s/1a43dfc7a3baa214aa0fa1b95d2c536a
IMHO an Ai is the llm plus it's harness.
A good harness would allow the llm to investigate on a map.
Just like the llm can use a python script to figure out how many r's there are in strawberry.
These tests are simply not that predictable of performance of the llm.
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> I pretty sure OpenAI and Anthropic are doing the same or worse. No they're not. It would end both companies if they were ever found to be doing that. Their terms are clear - if you use the coding plans they can[0] train in return. Enterprise and API, absolutely not. The argument here is that with the Chinese labs you have zero legal recourse. [0] opt-in, thanks
Their terms are not worth shit considering they are reselling you stolen copyrighted data. Even in they terms they started clearly say they retain your data for "safety reasons" for however long they want. Perhaps you didn't watch the space with Anthropic going back and forth with ToS updates(we retain your data for 30 days...stike that and add 30 days or more or no or ..whatever) like my own alpha website.
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I would also assume the same for non-Chinese as well
I trust them to act in their own interest if nothing else.
Kimi doesn't do well on my "ask a trivia question that other AIs get wrong" test. The question it came up with, "which U.S. state is closest to Africa?" is a pretty standard trivia question without any reason to believe other AIs would get confused. https://pellmell.ai/s/dccdeca69f929f79bc89317035610049 Even GPT-OSS-120b gets this right: https://pellmell.ai/s/1a43dfc7a3baa214aa0fa1b95d2c536a
These types of tests are kind of moot as agentic harnesses are taking over. IMHO an Ai is the llm plus it's harness. A good harness would allow the llm to investigate on a map. Just like the llm can use a python script to figure out how many r's there are in strawberry. These tests are simply not that predictable of performance of the llm.
> Chip Design > As an early proof of concept, Kimi K3 designed a chip to serve a nano model built on its own architecture. In a single 48-hour autonomous run, K3 built, optimized, and verified the chip using open-source EDA tools on the Nangate 45nm library. Within 4 mm², the chip closes timing at 100 MHz and sustains over 8,700 tokens/s decode throughput in simulation, packing 1.46M standard cells, 0.277 MB of SRAM,…