241–250 of 1001 posts
I'm a bit nervous this one isn't going to be open-weights. Any mention of "open" has been struck from the literature for this model (it was present an hour ago). We don't even know active params? At this pricing, I'll be surprised if it's open.
Imagine you're a mid sized company and you can host this model locally. Suddenly there are zero reasons to pay a single red cent to the bloodsucking American AI cartel.
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
> In our evaluations, Kimi K3 delivers frontier-level performance. Among the models tested, its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol. For the complete benchmark results, see our tech blog. The full model weights of Kimi K3 will be released in the coming days. More details on the architecture, training, and evaluation will be published together with the Kimi K3 technical report. > K…
> its overall intelligence ranks second only to Claude Fable 5 and GPT-5.6 Sol Pretty sure ranking “second” to two others means ranking third.
Imagine you're a mid sized company and you can host this model locally. Suddenly there are zero reasons to pay a single red cent to the bloodsucking American AI cartel.
I'm a bit nervous this one isn't going to be open-weights. Any mention of "open" has been struck from the literature for this model (it was present an hour ago). We don't even know active params? At this pricing, I'll be surprised if it's open.
Source: their release blog on WeChat. https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ
The big danger here is the gradual increase in open-weight subscription costs. I use open weight subscriptions, with lower-cost models for 80% of my tasks and GLM-5.2, Qwen 3.7-Max, Kimi-K2.6/2.7-Code for the 20% that need the most intelligence. That lets me maximize the rate-limit the subscription gives (rate limits per model are literally a price-limit-per-token/model). When new/more expensive open weights come in,…
Personally, I like that there is an option to not send data to companies that have strong financial incentives to steal it.
Also, open weight foundation models can be distilled, so they’re providing a service that the US duopoly is actively blocking. Given that app specific distillation can get > 10x improvements on inference cost (with slight improvement of quality), it’s clear that it’ll win out over time.
Curious why the thinking mention chatgpt for a moment https://ibb.co/JFdhMN95
It's incredibly funny, but I don't know whether it's related to distillation; it's probably quite rare for a distilled trace to mention which model it came from. (I'm not saying distillation doesn't happen, just that it's possibly unrelated.)
For your specific example, the internet is full of "As a large language model developed by OpenAI, I can't..." due to people pasting chatbot output without reading it. Seems reasonable for that to surface as part of the CoT for your question about model capabilities.
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
It's like reading Anthropic's obituary.
(I mantain a client with llama.cpp and 101 models across 14 companies by http)
Earlier quoted context omitted.
I wouldn't be surprised if models were optimizing for rendering SVG pelicans at this point
every ai release thread seems to have this same sequence of comments