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Kimi K3: Open Frontier Intelligence

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Re: Kimi K3: Open Frontier Intelligence

#151

Post by cmrdporcupine »

dghlsakjg wrote: post #23
Tiberium wrote: post #2

More details: - https://platform.kimi.ai/docs/guide/kimi-k3-quickstart - https://platform.kimi.ai/docs/pricing/chat-k3 1M context, pricing is $3/$15 for 1M tokens (cache $0.3), which is extremely high for a Chinese open-weight model, but if it's truly competitive with most of the current frontier and is only behind Fable/Sol, the pricing is justified. This is 1:1 pricing of Anthropic's Sonnet series (except Sonnet 5…

Tokenizers also matter. Anthropics tokenizers will encode the same piece of text at a way higher token count than OpenAi, for example. That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price.

GLM is actually quite expensive in actual practice because it's not very token efficient. I've yet to find a way to run it on a monthly sub reliably for cheaper than Codex.

Neuralwatt was cheap (but slow) but they cranked their price.

Ollama monthly sub is speedy but doesn't offer a lot of quota.

Right now unless you're paying by the token, there's no cost based reason to use the open weight models for daily coding work because the monthly coding plans from Anthropic and OpenAI are a better deal.

Re: Kimi K3: Open Frontier Intelligence

#152

Post by nerfbatplz »

lompad wrote: post #78
sixtyj wrote: post #35

Earlier quoted context omitted.

[flagged]

The thing is - as a European, I can choose between plague and cholera. One has mostly been reliable, stayed peaceful towards us and is primarily concerned with their internal matters and the countries right next to it. They have long-term strategy and understanding of win-win situations. The other one keeps threatening to invade/steal Greenland. Keeps waging an economic war against the entire bloc. Positions their pr…

The last time China bombed a foreign country was nearly 50 years ago.

A very inconvenient truth for the China hawks.

Re: Kimi K3: Open Frontier Intelligence

#154

Post by [deleted] »

leecommamichael wrote: post #47
dghlsakjg wrote: post #23

Earlier quoted context omitted.

Tokenizers also matter. Anthropics tokenizers will encode the same piece of text at a way higher token count than OpenAi, for example. That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price.

Tokenizers define the alphabet on which the language model is trained. I don't want people to get the impression it's a module which can be swapped out or modified on its own. Alphabet size is a design consideration related to correctly encoding the training data.

[deleted]

Re: Kimi K3: Open Frontier Intelligence

#155

Post by icedrift »

wolttam wrote: post #104

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.

Reuters has been reporting that Chinese government is undergoing similar investigation to the US; blocking the export of domestic frontier models. They boil down to "anonymous sources" but it does seem inevitable as the tech gets stronger and stronger.

Re: Kimi K3: Open Frontier Intelligence

#157

Post by verdverm »

npn wrote: post #85

Not worth it. I have just tried a single prompt in the web interface and it is still not finish reasoning. It thinks too much and often repeats the same stuff over and over. Combine with the price it will surely more costly than gpt 5.6.

Its bad to judge these things on immediate release, there is a spike of excited users and that distorts performance. Also bad to judge from on a single interaction, you'll get bad requests with every provider, super busy times raise the probability

Re: Kimi K3: Open Frontier Intelligence

#158

Post by leecommamichael »

smallerize wrote: post #127
leecommamichael wrote: post #47

Earlier quoted context omitted.

Tokenizers define the alphabet on which the language model is trained. I don't want people to get the impression it's a module which can be swapped out or modified on its own. Alphabet size is a design consideration related to correctly encoding the training data.

That's true, but it makes it difficult to compare pricing when it's based on tokens. Maybe we need a benchmark for price per a specific input, like enwiki8.

Yes, almost all work people share which seeks to measure the capabilities and differences of models needs to get more precise. We are clamoring to say something meaningful about these things.
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