Twitter/X

Kimi K3 is priced at $3/$15 vs Opus 5 ($5/$25) and Sol 5.6 ($5/$30), but is not…

2026-07-27 · 19:39 UTC ·@cryptopunk7213 ·1 min read

Brief

Alex Corrino (quoted by @cryptopunk7213) argues that Kimi K3, despite lower list pricing ($3/$15), is expensive to serve because it needs 16+ H200s per model—about twice GLM 5.2/Deepseek V4—since KDA/MOE design reduced HBM cache but bloated model weights; thus Kimi is not “much cheaper” or “effectively free.”

Why it matters

Kimi K3 is priced at $3/$15 vs Opus 5 ($5/$25) and Sol 5.6 ($5/$30), but is not cheaper to serve because it requires 16+ NVIDIA H200 GPUs to host a single copy—roughly double the ~8 H200s needed for GLM 5.2 and Deepseek V4.

Key details

  • Kimi applies KDA and a scarce MOE as described in its paper, but HBM savings were largely shifted into storing very large model weights; weights can serve many users per instance while cache is user-specific, and overall memory consumption is rising sharply (aligned with a recent Meta/Micron whitepaper).
Source evidence

"Purely architecturally it's correct to say that whatever HBM demand they saved was simply shuffled to hold the weights of this very large model."

I guess kimi delta attention didn't reduce memory requirements for frontier models.

probably a common pattern we'll see amongst many models going forward (and confirmed in the recent meta/micron whitepaper): memory consumption is skyrocketing

Alex Corrino (@AlexCorrino)

Kimi K3 is an impressive model. Interestingly, none of the 3rd party providers are offering it more cheaply than Moonshot themselves.

For pricing comparison:
Opus 5 is $5/$25
Sol 5.6 is $5/$30
Kimi K3 is $3/$15

Why isn't it cheaper? Well, put simply, it's not cheaper because it's expensive to serve.

Now that we have the technical paper, we can see that it is a massive model that requires 16+ H200s to serve a single copy (and significantly more at scale).

For context, GLM 5.2 and Deepseek V4 both required ~8, so this model is ROUGHLY double the cost to serve (which roughly fits with the pricing comparisons).

On the memory front, KDA is applied pretty much exactly as specified in the paper.

The interesting learning on memory is that even with their scarce MOE model the model weights are huge. Purely architecturally it's correct to say that whatever HBM demand they saved was simply shuffled to hold the weights of this very large model.

But it's important to note that while cache is user-specific, model weights can serve many users from one instance.

Memory architecture isn't simple, so it's hard to ferret out exactly how this affects memory demand going forward.

But I think it's safe to conclude that the idea that Kimi is "much cheaper" than the frontier models or "effectively free" is decisively false.

— https://nitter.net/AlexCorrino/status/2081779876160582042#m