I get a quota of GitHub Copilot for free.

From all the models available to me I'm most happy with Kimi K2.7 (given the cost/performance).

Traditional narrative is that you need tons of traces of actual execution to post-train and get models right. Nobody seems to use Kimi API from Moonshot, I bet everybody is using them on neoclouds/inference providers like Together, Nebius, Fireworks etc. where unlikely they will get traces (in fact, thats the whole promise of these inf providers). How are Kimi models improving so quickly? Is this just distillation (though Sol/Fable just came out so I find it hard to believe)

I hope this means they stop downgrading my fable requests

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

>Too many people are chatting with Kimi right now. Subscribe to enter a dedicated priority queue!

Does anyone have any heuristics on how scaling parameter count actually scales cost to serve? Also im assuming we dont really know the sparsity here?

Is them pricing at Sonnet level actually give us any information at all at how big Sonnet is or is there too much opacity around inference margins?

This is super exciting. I really need to buy better hardware to try this stuff.

But, it's not open models.

Is there a way to try it without using your Google account or giving them your phone number?

This is far too expensive. Why would I use this over a frontier model at these prices.