It does devolve into gibberish at long context (~120k+ tokens by my estimation but I haven't properly measured), but this is still by far the best bang-for-buck value model I have used for coding.
It's a fine model
I'm curious how the bang for buck ratio works in comparison. My initial tests for coding tasks have been positive and I can run it at home. Bigger models I assume are still better on harder tasks.
as kimi did a huge amount of claude distilation it seems to be somewhat based in data
https://www.anthropic.com/news/detecting-and-preventing-dist...