Comment by Tiberium
7 hours ago
More details:
- https://platform.kimi.ai/docs/guide/kimi-k3-quickstart
- https://platform.kimi.ai/docs/pricing/chat-k3
1M context, pricing is $3/$15 for 1M tokens (cache $0.3), which is extremely high for a Chinese open-weight model, but if it's truly competitive with most of the current frontier and is only behind Fable/Sol, the pricing is justified.
This is 1:1 pricing of Anthropic's Sonnet series (except Sonnet 5 which is currently on discount), and very close to 5.6 Terra pricing (Terra's input is $2.5).
One thing to consider, though: reasoning efficiency matters directly for how expensive a model actually is in real use. GPT's models are extremely reasoning efficient, and some Claude models like Fable at lower effort are as well. So if Sol spends 10K reasoning tokens to do something (at $30/1M) vs Kimi K3 that spends 50K reasoning tokens, Sol would win on cost effectiveness.
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.
It also depends on how many tokens it needs to burn through to accomplish something.
At this point, I always look at things like Artificial Analysis' total cost to run their tests. It'll take into consideration the cost of tokens, how many tokens it burns through, and how effectively it uses caching (and the price of that caching).
If a model "costs the same" but its reasoning ends up going through a ton more tokens, it doesn't really cost the same in real world usage.
> That said, Kimi is competing against GLM in my mind, and GLM 5.2 is less than 1/3 the price.
Having used GLM 5.2 extensively and K3 for a few hours now, these models are nowhere near each other. 5.2 is a great model, and I use it for a lot of things, but it's noticeably below Opus 4.8 or GPT-5.5 in real-world usage.
K3 is in the same ballpark as Fable or Sol.
Tokenizers define the alphabet on which the language model is trained. I don't want people to get the impression it's a module which can be swapped out or modified on its own. Alphabet size is a design consideration related to correctly encoding the training data.
That's true, but it makes it difficult to compare pricing when it's based on tokens. Maybe we need a benchmark for price per a specific input, like enwiki8.
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I’ve been struggling to understand the reason for the newer apparently less efficient Anthropic token encoding. If all inputs are less efficient in this encoding, why does it exist? Has Anthropic released any information that would convincingly show it was anything other than a stealth price hike? Please don’t respond if you are speculating.
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With that kind of pricing, I don't think they're competing with GLM with this new launch.
I believe Kimi is spending more on marketing than GLM (a lot of ads lately) so I guess that's part of what the higher price supposed to cover.
GLM is actually quite expensive in actual practice because it's not very token efficient. I've yet to find a way to run it on a monthly sub reliably for cheaper than Codex.
Neuralwatt was cheap (but slow) but they cranked their price.
Ollama monthly sub is speedy but doesn't offer a lot of quota.
Right now unless you're paying by the token, there's no cost based reason to use the open weight models for daily coding work because the monthly coding plans from Anthropic and OpenAI are a better deal.
> I've yet to find a way to run it on a monthly sub reliably for cheaper than Codex.
Matches my experience, I got their Pro subscription and while I enjoyed the model itself a lot and while their ZCode harness is also pretty nice, it gave me less tokens for similar amounts of money that Anthropic would give me on a subscription: https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
I'm yet to try out Kimi, but if their subscription were to be anywhere comparable to Anthropic/OpenAI, I might just switch over because competition is good.
DeepSeek V4 Pro is really affordable per-token but regularly kept making mistakes in the tasks I gave it. I mean I could at least afford the tokens to go over the work a 2nd, 3rd, 4th and 5th time and gradually fix most of the issues, but it was a very frustrating mode of work.
I know GLM is relatively expensive and so is Kimi, in comparison to those DeepSeek V4 pro and flash are a godsend and are absolutely good value.
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I'm on the Z.ai quarterly subscription plan (got in when the price was lower) and I was using it through opencode and it was like I'd only get maybe an hour of usage (if that, sometimes) before it would time out and say come back in 5 hours. Now I'm using it through their Zcode harness and I rarely hit that - they say they're giving 1.5x usage if you use it through Zcode, sometimes seems like even more than that.
I found this with kimi k2.7 as well: on paper it should be quite cheap, but it's not because it uses a lot of tokens for quite simple tasks
re:
> Right now unless you're paying by the token, there's no cost based reason to use the open weight models for daily coding work because the monthly coding plans from Anthropic and OpenAI are a better deal.
Maybe. I am on a $20/month Anthropic subscription this month but I also use Claude Code frequently with Deepseek v4 flash and pro, GML5.2. For simple work Deepseek v4 flash is so nice because it is fast.
What you say is true however, the US hyper-scalers are still (desperately?) subsidizing subscriptions for market share to boost there valuations.
I really want to see AI inference costs approach zero, and I think I just need to wait a few years to see that.
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I don't know, DeepseekV4 is so dirt cheap that it makes lots of sense to use over Sonnet.
Compared to the flagship models GLM is still a 1/10th the price on the task I have tested.
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 have severe complaints about Anthropic's product managers on this front. Their preference for hiding, obscuring, and trying to wrest control from the user are a bit harrowing. It would be wonderful to go back to Claude Code from before March. It seems like every release destroys value for me!
It's a defensive tactic to reduce the effectiveness of distillation.
Say of that what you will, but it's not because they want to wrest control from users.
It's because they don't want Chinese companies to do exactly what Moonshot (Kimi creators) and others have done.
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The reasoning is key as most of the time the summary provided by fable is not enough to understand the choice and correct the logic. You have to either fully trust it or go to an exhaustive code review. This with the fact that you can only use 4.8 to security review the code produce by fable are the reasons I will not renew my anthropic subscription, the current experience is way to degraded.
What will you be replacing it with, if anything?
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.
> 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.
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.
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.
API prices are amazing, but hosting this on-premise will be real challenge.
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
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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Or just host it yourself or on your country's cloud provider once they release the weights.
The thing is - as a European, I can choose between plague and cholera.
One has mostly been reliable, stayed peaceful towards us and is primarily concerned with their internal matters and the countries right next to it. They have long-term strategy and understanding of win-win situations.
The other one keeps threatening to invade/steal Greenland. Keeps waging an economic war against the entire bloc. Positions their propagandists right in our middle and does the best to influence our elections. Exports fascism and finances antidemocratic forces. Supports the genocide in that certain country. And still have their soldiers in our country, against the wishes of a majority of the population. Oh and they don't honor any treaties if they feel like it.
Easy choice.
Does that make china an angel? Hell no, they are still committed to enslaving the Uyghur people, keep threatening neighbors and are mostly han supremacists. Human rights are seen as merely a suggestion by them.
But at the time being, one is clearly more reliable than the other. Long-term, I'd like to avoid both the US and China.
>One has mostly been reliable, stayed peaceful towards us and is primarily concerned with their internal matters and the countries right next to it.
This is textbook international relations realism. Rising powers pretend they aren't powerful so countries don't balance against them.
Their actions are entirely predictable.
Then suddenly they will begin to do imperialism, like all great powers, and suddenly they will pretend to be stronger than they are.
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The last time China bombed a foreign country was nearly 50 years ago.
A very inconvenient truth for the China hawks.
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>Keeps waging an economic war against the entire bloc.
>Positions their propagandists right in our middle and does the best to influence our elections.
>Exports fascism and finances antidemocratic forces.
>Supports the genocide in that certain country.
>Oh and they don't honor any treaties if they feel like it.
I don't know how anyone can really mention any of these when trying to paint a bad picture of anyone as compared to China. It's just an obscene exercise in ignorance. I just can't make sense of discourse like this except as a result of propaganda.
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>committed to enslaving the Uyghur people
What?
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Better than handing it over to the US regime.
I'd much rather give my data to China because I don't live there, so there's not a whole lot they can do to me. The US, on the other hand, has a lot leverage over my life and freedom.
and yet here you are on an american site providing data. what about youtube or reddit? I don't think you actually care in reality. otherwise you wouldn't be here to comment.
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Or the American one :)
Sadly these days this seems like the least worse of the three major regimes.
You are in a bubble. They just raided independent book stores in Hong Kong.
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It's an open model, you can just wait a few days and you'll get to choose who to hand it over to, or given the resources you can run it on your own box.
I have absolutely zero sympathy for Western model providers.
Bring on the Chinese token-dumping onslaught.
Right at this moment, there are more people in the world on the side of China than on the side of the USA. Which can translate into raw market numbers at some point. So these comments are kinda moot.
Maybe the Democracy Index can make this a little more fact-based: https://en.wikipedia.org/wiki/The_Economist_Democracy_Index
USA = Flawed democracy
China = Authoritarian
I don't really know how well they do this index, but probably better than a random HN comment.
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That's not what the actual data shows. The American frontier providers captured the entire market. China is getting the scraps.
https://gs.statcounter.com/ai-chatbot-market-share
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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.
That’s not what this indicates. This is the biggest and most expensive to serve, and most capable open weights model yet. They’re just pricing it in line with capabilities.
Kimi also offers generous subscriptions. Subs aren’t going anywhere. Think of subs like running an insurance business. There might be some users you lose money on (ones who max out their weekly quota without fail), but they’re managed such that the average subscription turns a healthy profit. There’s never been subsidies in model serving, inference is just cheaper in terms of ops TCO than people assume, and API margins are very high.
> They’re just pricing it in line with capabilities.
So... convergence?
> but they’re managed such that the average subscription turns a healthy profit.
It didn't work like that, or at least that's not how it played out. People max-out their subs all the time which is why strict and multiple limits were implemented by all providers. Also, I subscribe to z.ai and recently they dropped the quota significantly that now their sub offers less than Claude and OpenAI. It's still x5-6 what it would cost on API costs though.
> inference is just cheaper in terms of ops TCO than people assume, and API margins are very high.
API margins (at least american ones) are probably healthy. But I don't think that inference is that cheap. It would cost 300-500k to just run GLM 5.2. There are lots of other factors too: reliability (can you keep the GPUs running all time), electricity cost, sys. admin costs, location costs, etc.. I wouldn't be surprised if the API margins are quite close to operational costs.
Ah, the old "subsidized" meme always rearing its head. Yawn.