5 Thoughts on Kimi K2 Thinking

First, congrats to the Moonshot AI team, one of the 6 “AI Tigers” in China, on the awesome release of Kimi K2 Thinking. One of the overlooked and inspiring things for me these days is just how many people are learning very quickly to train excellent AI models. The ability to train leading AI models and distribute them internationally is going to be pervasive globally. As people use AI more, those who can access supply for inference (and maybe the absolute frontier in scale of training, even if costly) is going to be the gating function.
K2 Thinking sounds like a joy to use because of early reports that the distinctive style and writing quality from their original Kimi K2 Instruct model have been preserved through extended thinking RL training. They released many evaluation scores, for a highlight they’re beating leading closed models on some benchmarks such as Humanity’s Last Exam or BrowseComp. There are still plenty of evals where GPT 5 or Claude Sonnet 4.5 tops them. Rumors are Gemini 3 is coming soon (just like the constantly pending DeepSeek V4), so expectations are high on the industry right now.
TLDR: Kimi K2 Thinking as a reasoning MoE model with 1T total, 32B active parameters, 256K context length, interleaved thinking in agentic tool-use, strong benchmark scores and vibe tests.
The core reaction of this release is people saying this is the closest open models have been to the closed frontier of performance ever, similar to DeepSeek R1‘s fast follow to o1. This is pretty true, but we’re heading into murky territory because comparing models is harder. This is all advantaging the open models, to be clear. I’ve heard that Kimi’s servers are already totally overwhelmed, more on this soon.
What is on my mind for this release:
1. Open models release faster.
There’s still a time lag from the best closed to open models in a few ways, but what’s available to users is much trickier and presents a big challenge to closed labs. Labs in China definitely release their models way faster. When the pace of progress is high, being able to get a model out sooner makes it look better. That’s a simple fact, but I’d guess Anthropic takes the longest to get models out (months sometimes) and OpenAI somewhere in the middle. This is a big advantage, especially in comms, to the fast mover.
I’d put the gap at the order of months in raw performance — I’d say 4-6+ months if you put a gun to my head and made me choose specifically — but the problem is these models aren’t publicly available, so do they matter?
2. Key benchmarks first, user behaviors later.
Labs in China are closing in and very strong on key benchmarks. These models also can have very good taste (DeepSeek, Kimi), but there is a long-tail of internal benchmarks that labs have for common user behaviors that Chinese labs don’t have feedback cycles on. Chinese companies will start getting these, but intangible’s are important to user retention.
Over the last year+ we’ve been seeing Qwen go through this transition. Their models were originally known for benchmaxing, but now they’re legitimately fantastic models (that happen to have insane benchmark scores).
Along these lines, the K2 Thinking model was post-trained natively with a 4bit precision to make it far more ready for real serving tasks (they likely did this to make scaling RL more efficient in post-training on long sequences too):
To overcome this challenge, we adopt Quantization-Aware Training (QAT) during the post-training phase, applying INT4 weight-only quantization to the MoE components. It allows K2 Thinking to support native INT4 inference with a roughly 2x generation speed improvement while achieving state-of-the-art performance. All benchmark results are reported under INT4 precision.
It’s awesome that their benchmark comparisons are in the way it’ll be served. That’s the fair way.
3. China’s rise.
At the start of the year, most people loosely following AI probably knew of 0 Chinese labs. Now, and towards wrapping up 20...