ryan_greenblatt's Shortform

ryan_greenblatt

Kimi K3 was significantly but not massively above my expectations. I'd tentatively guess it's similar in overall usefulness/usability to Opus 4.8 and in overall capability somewhat above Opus 4.8 (while also being somewhat more benchmaxxed). As a pretrain, ~~it's probably somewhere between 4.8 and Mythos (around halfway between?)~~ it's around or a bit worse than Opus 4.5 ^[1] . Maybe this implies Kimi is like 8 or so months behind Anthropic in overall model strength/goodness (including usability) and like 6 or so months behind on overall capability (somewhat below Mythos Preview).

This gap is presumably reduced by distillation (and more generally using OpenAI/Anthropic models) and algorithm leakage/diffusion, so I think that hypothetically if the US completely stopped and recent algos didn't diffuse, it would maybe take Kimi like 10 months to fully catch up to the best internal (including in development) Anthropic model. (I think this notion might be a better measure of where Anthropic/OpenAI are relative to Kimi, even though this hypothetical won't happen.) And if the US completely stopped, it might take Kimi around 27 months to reach the level the US would otherwise have reached one year from now (as in, with a year of further progress).

My views here are pretty sensitive to how much benchmark performance is representative to overall usability.

I think I now expect an open-weight AI which is straightforwardly "Mythos-level at cyber" (including usability etc.) in like 5 months supposing Kimi and others don't change their open-weight model policy. (I don't have a strong view about how big of a deal this is for cyber, but it may cause significant political consequences. This could be a significant overestimate of the time required.)

I wonder what's driving Kimi being closer than I would have expected. Options include:

Experiment compute is significantly less important than labor (and labor at Kimi is competitive, which seems super plausible)
- Implies more of a speedup from AI automating AI R&D and a bigger software-only intelligence explosion.
- Or possibly Kimi is just doing much better than US companies and this is overcoming experiment compute disadvantages.
Algorithms are diffusing a lot / quickly (from e.g. OpenAI to Kimi).
Perf is overstated / benchmaxxed a lot.
Distillation / using OpenAI or Anthropic frontier AIs in AI development is very helpful for catching up. (But I'd guess Kimi K3 is a competitive pretrain which distillation doesn't help with?)
US companies aren't going as fast as they could for whatever reason.

(Cross posted from x/twitter.)

Based on some quick tests, see here for details. ↩︎

ryan_greenblatt4mo*75-2

Showing 3 of 13 replies (Click to show all)

Altaer4mo10

I see - I wonder how sensitive this measurement is to labs doing post-training steps which include lots of low/no reasoning token math problems. With K3 currently supporting only 'max' reasoning I'm also led to wonder if if Kimi deliberately put lower focus on low/no-reasoning tasks in their post-training and if you could in fact be seeing the results of that rather than of a substantially weak pre-training phase.

3Erich_Grunewald4mo

My understanding is that Chinese AI companies have a much shorter lag between finishing a model and releasing it than do American AI companies. E.g., they likely do far less safety testing and red-teaming. I think I remember reading someone at a Chinese AI company saying they try to release within days of a model finishing training, but I can’t find that source now. I would be surprised if Moonshot had a more powerful model than K3 internally today.

12Aaron_Scher4mo

Source for Z.ai / GLM: https://www.chinatalk.media/p/the-zai-playbook#:~:text=Zixuan%20Li%3A%20Get%20it%20out%20fast.%20We%20open%20source%20it%20within%20a%20few%20hours.

ryan_greenblatt's Shortform

19

Ω 5

ryan_greenblatt's Shortform

19

Ω 5