Why we won’t see another DeepSeek moment anytime soon

In January 2025 the world saw the stock market collapsing with the ‘DeepSeek moment’. What happened: DeepSeek released an open weight model making a big leap in capabilities compared to predecessors. The result: NVIDIA lost about $600B in value on a single day. The logic from the market was: if great models can be made cheap and open, who needs that enormous amount of compute? Now, eighteen months later, recently released open models like GLM-5.2 and Kimi K3 are giving a similar shockwave to the AI market, but my prediction is that this time the stock market won’t collapse, more like the opposite.

Kimi K3 hit #8 on the OpenRouter leaderboard within a few days after release, doing ~155B tokens per day. And also on the benchmarks, the model performs exceptionally well, with only Claude Fable 5 and GPT-5.6 having a higher intelligence score on Artificial Analysis. We are seeing a similar leap in capabilities at Kilo.

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So all of that is interesting, but aside from intelligence there’s a lot more to take into consideration. And these two charts tell an interesting story:

To understand the effectiveness of a model, you need to combine intelligence with cost and performance. And when you do that, the picture starts to look very different for Kimi K3: the model looks stellar on raw intelligence, but isn’t even to be found on the speed chart. That’s why, for KiloBench, we’re looking at cost vs performance (in the Kilo harness) and popularity combined.

This week, Moonshot AI posted this:

The takeaway: even though the model is high on intelligence, performance metrics fell off a cliff. Throughput went from 30 tokens per second to 13. Time to first token increased to 20+ seconds. Moonshot paused new subscriptions to protect existing users and started splitting its plans to divide capacity between chat and coding.

So a frontier-class open model launched, and instead of relieving pressure on compute, it…