Some official benchmark numbers posted in Chinese social media (I am sure they will publish an English blogpost later too):

https://mp.weixin.qq.com/s/V4xhEIy8xDXSMDPrPkmUAQ

Generally looks like a Sol/Fable tier model, better across the board than Opus 4.8.

(Edit) English blogpost is up now: https://www.kimi.com/blog/kimi-k3

Tokenizers also matter. Anthropics tokenizers will encode the same piece of text at a way higher token count than OpenAi, for example.

That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price.

I've been avidly using Fable since it was re-released and while it has been excellent at building the apps I want, the reasoning has been completely opaque.

Kim, however, has exposed the whole reasoning trace, or enough of it to matter. I'd almost forgotten how nice it is to see this. I've been able to see all of the weird twist and turns it takes and it is joyful. But also, far, far more informative and means I can debug ideas far more thoroughly. Also, at a first glance it seems to have gotten quite far on a niche hobby horse of mine that no LLM has been able to crack. I'll be testing this more for sure.

I feel like the quickstart is missing something. It's referring to its tech blog for actual benchmarks, but K3 isn't mentioned on there, the last thing on that blog was K2.6, 2 releases ago.

Does it have safety guardrails that constantly false positive like Claude does? The only obvious change I’ve seen since opus 4.6 came out is that it constantly flags my requests (no, I’m not doing biology research or security research, yes, it flags for both of those things).

Recently, they backported the blocks to Opus 4.8, so I’m reluctantly stuck on sonnet.

I probably could successfully apply to get special approval to use claude code unencumbered, but I don’t think it is ethical to support tooling that’s built so a central authority gets to decide what intellectual endeavors and knowledge work are permissible, and what are not.

also its pretty big model inference costs are high even with margins running a 2.8T model costs a lot. if they release oss may be it goes down to $10-12 per million tokens.

> reasoning efficiency matters directly for how expensive a model actually is in real use

I have high hopes on this topic, given token efficiency seemed to be the primary (only?) goal of the K2.7 Code release.

Excited to see the signals that come out of the big eval/benchmark sites.

API prices are amazing, but hosting this on-premise will be real challenge.

Will be interesting to see how it stacks up pricing wise on the various inference providers.

Agreed re reasoning. I’ve seen this play out with 5x reasoning negating cost savings.

This is too expensive to be a viable model. If it were $5/1m output, it might be another story. At these prices, there's no reason to use this over GPT 5.6.

Are thinking models only the reasonable tradeoff vs using much larger non thinking ones because the cost of output tokens is below that of input tokens?

How do Kimi's subscriptions work? I find their price structure pretty confusing

The big danger here is the gradual increase in open-weight subscription costs. I use open weight subscriptions, with lower-cost models for 80% of my tasks and GLM-5.2, Qwen 3.7-Max, Kimi-K2.6/2.7-Code for the 20% that need the most intelligence. That lets me maximize the rate-limit the subscription gives (rate limits per model are literally a price-limit-per-token/model). When new/more expensive open weights come in, providers phase out older/cheaper models. Over time we will either have to pay more, or use our subscriptions less.

It goes without saying, but if the open weights become as expensive as SOTA models, there's no point in using open weights. If nobody pays for open weights' development, the development dies out, and we're stuck with a US-controlled duopoly again. Which may be the biggest threat the world has seen from the US since nukes.

I eat 1M context in a local model in about 3-4 hours.

It'd need to be exceptionally smart and error free to ever make sense.

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It seems the subsidized era is nearing its end and we'll see a convergence on API pricing before a pulling of subscriptions pricing.