This is napkin math since I'm mostly just extrapolating from glm 5.2 by assuming it's twice as heavy to serve in every single measurement, but I believe you can easily achieve 2500tok/s aggregate compared to 4500tok/s and up to 8000tok/s for glm5.2.
with nvidia r100 you are likely going to be able to push that number even higher while the cost of hardware appears to be relatively the same, so far I am seeing 21% premium from supermicro which is twice as fast and has nearly twice the vram.
The model weights are supposed to release tomorrow.
Over the next several weeks, I would expect competition among open weight providers to drive down the cost, as I've seen happen with other open weight model releases.
I don't mean to imply that Kimi is not at all cheaper than U.S frontier models. I more wrote this because I believe - since Chinese LLMs entered the public consciousness via DeepSeek R1, which was genuinely ~20x cheaper than o1 - there's a bit of a halo effect around Chinese models which causes people to overestimate the scale of the discount. And relative to that price anchor, Kimi is less extraordinarily cheap.
At the moment Kimi is ~10% cheaper than GPT-5.6 on the AA benchmark, and as you say that could go down to 20-30% cheaper (although I don't know how inference provider discounts play out on real world usage once you account for quantisation etc...). I'm not trying to suggest that that's nothing, but I do think some of the people driving the Chinese AI discourse would have a harder time pitching their conclusions if they were saying "this new Chinese model is 10% cheaper on some tasks, and it might get another 20% cheaper in the future".
If you switch the view to "coding tasks" on this website:
Kimi K3: $3.18 per task
GLM 5.2: $6.51 per task
GPT 5.6 Sol: $7.02 per task
Opus 5: 8.23 per task
Fable: 11.70 per task
Additionally, I fully expect the frontier labs to continue increasing prices to meet the profit margins they need to to continue existing.
ronsor•45m ago