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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

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The link has 6 well-known benchmarks where this beats Fable (out of 14 I counted). If the numbers hold up scrutiny, this is scary good.

Forget about their pricing but the companies that do have means to host such models fully on-prem are also the same companies that are paying tens of millions of $ in inference cost every month, and are by extension the biggest customers of OAI and Anthropic

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> If the numbers hold up scrutiny, this is scary good.

After using it for a few hours, I believe these benchmarks.

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Open Source >>> Closed Source [1]

I don't want to cheer against my country, but we've given up on open source. The way Anthropic and OpenAI treat their customers as adversaries is embarrassing.

I will cheer for China, for Kimi, and for z.ai until we have something in the same category.

[1] I'd even be fine with open weights, fair source, or anything that let us have direct access to the weights. Even if that came with stipulations. Don't hide the weights from us.

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I am with you in the spirit of openweights but I am trying to hard-avoid bringing countries into this. The narrative of US vs China only benefits those who want regulatory capture in the US since attacking China is politically much easier than attacking open-weights, so certain groups like to repeatedly call them 'Chinese models'.
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It's much more a rallying cry for open weights funding than it is for regulatory capture.

The argument on our side wins - if America or the West don't do open source, China will. And that means -- with certainty -- that China wins the market.

Every politician and VC should hear that loud and clear.

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I think given how much benchmaxxing we're seeing - the anecdotal evidence of how competent this model is (and efficient) will depend on user's actual real-world use cases.

Given the pricing, it suggests that this model is much more efficient/competent than previous-gen OS/distilled models.

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It's like reading Anthropic's obituary.
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Certainly for their IPO, anyway
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This is weird and reactionary. Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns. Anthropic/american models aren't going anywhere anytime soon.
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> Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns

This is such a common omission: the Chinese models are open, you can host them yourself on your premises. So privacy and independence.

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it's well documented that models can be adversarially trained with essentially backdoors in response to special inputs

while I am skeptical that this is happening atm, there are probably many industries where the risk does not seem worthwhile

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I suppose this is like when Anthropic was using “prompt modification, steering vectors, or parameter-efficient fine-tuning” to poison the work of people working in the LLM field, including academic researchers.
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When the model is open weights you can even pass every token (including the chain of thought) though a fourth-party lightweight model like gpt-oss-safeguard to check that it has not become adversarial.
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I feel like that's a threat that isn't super difficult to block. Unplug it from the internet, require it to go through an API intermediary to access web pages.

Maybe I just don't have any imagination.

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It could generate code that's plausible but has intentional flaws, kind of like the defunct underhanded C contest [0], except through a LLM.

[0] https://en.wikipedia.org/wiki/Underhanded_C_Contest

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It could, but exposing that would doom the company entirely, and AI doesn't generate code with near the quality needed to get a model to mass adoption, insert malicious underhanded code, ensure that consistently looks innocuous enough to never be noticed, and- most importantly- actually exfiltrate data without being noticed. Once it is noticed, it's game over across the board.
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Good luck hosting 2.8T params yourself. A box capable of this at a useful performance level is at least $100k.
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> Lots of organizations are continuing to refuse to use chinese models

Correction: Lots of organizations are refusing to use Anthropic Fable because they have forced opt-in data collection as part of their privacy policy, even for Enterprise.

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Both things, and both reasons, can be true at the same time.

Not everyone's going to care about Anthropic requiring data collection (a similar debate plays out with regards to "pay or consent" on website tracking), just as not everyone cares about China with regards to security/IP issues (if they did, a lot more would be banned besides occasionally-Huawei).

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This is apparently Open Weights, so no reason Amazon can't serve it alongside GLM which they already do.
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Nope, but I think this is maybe the critical mass needed to finally crash the AI hype/datacenter cost problem everyones is talking about.

With Oracle being junk before this, more will follow.

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I would assume the opposite is true — with an open-weight Fable-class model, doesn't demand for GPUs go up? Plenty of companies can now look at what Anthropic is offering — high per token costs for a very intelligent model — and do the math, and at some point it makes sense to just rent the GPU yourself and run Kimi on it if you get similar intelligence without paying Anthropic's margins (albeit with high upfront capital cost).

This would drive down Anthropic's margins, but drive up demand for datacenter and GPU capacity. It's not that people would be using fewer GPUs, they'd just shift demand from high priced token vendors to direct GPU rental, which benefits datacenter companies while hurting Anthropic.

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Its a margins game. If its too cheap to run, its not worth the investment.
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Oracle is fine, it's just that they can't really expect political decisions that hindered it to accquire TikTok which will be slated to be the biggest customer if the deal went through.

Now they are betting with Project Stargate but it also seems to be crumbling down.

But don't forget that they literally hold the biggest databases, both in commercial and open source, that is, Oracle Database and MySQL. Plus Oracle Java they literally controls at least 30% of the internet's software infrastructure.

And also with a good team of attorneies enforcing the licenses, they can squeeze so much money at the cost of morality.

Also recently they downgraded the always free OCI ARM instance from 4C24G to 2C12G without telling anyone.

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New enterprise java licenses are going to milk enterprise just like broadcom is doing. New license deals makes you pay for employee total number (including contractors) instead of for users of oracle java.
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> Oracle is fine

They're drowning in debt and risk is increasing. If these US models don't keep holding up their valuation will tank further and some will recall the loans or ask for different terms.

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Models need datacenters to run. It also need other services to do anything useful
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The point: Fable isn't worth what Anthropic says it is, so Anthropic isn't as valuable as they make themselves out to be.

The DeepSeek incident has already shown it, this is a reminder.

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If it ends up being open weights, companies will use it running in US data centers.
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You can run open weight models anywhere.
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Cursor will rebrand it as Composer 3.0 to assuage any such concerns, as they did with the previous Kimi models.
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More likely for them to use Kimi 2.7 since Grok is now the flagship product.
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Nah:

https://www.youtube.com/watch?v=LSlV206xPqM

These real world examples show it's one tier away.

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These "real world" examples are nothing like the way I use LLMs from within a harness. GPT 5.6 Sol and Fable are clearly more impressive, but how does this translate to interactive agent use, or use under an agent orchestration framework?
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This is a question I am going to get an answer tomorrow with evals. Extremely interesting...
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Fable is by Anthropic, and this is too expensive, GLM 5.2 is roughly the same quality at a much cheaper price.

(I mantain a client with llama.cpp and 101 models across 14 companies by http)

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As much as I like GLM 5.2 it's clearly a step below Opus (or even Fable) for more complicated tasks. I would place it at Opus 4.6/4.7 level.

Having said that, the safety system on Fable makes it an extremely unattractive model. It feels that half of the time you're paying double for Opus level performance.

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Fable won’t even generate a jwt to test endpoints because it is security related. It is crazy capable but useless for real work
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Unless your real work is outside the scope of one tiny niche of work.
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GLM has issues with tool calls and nested JSON and it wastes tokens pretty often. I see it being a bit above half the price of Opus in a bit more complex eval tasks. With some RL you could probably get the tool calls sorted and the price down.
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If anecdote is data, then here's another point:

https://nitter.net/synthwavedd/status/2077537805715005724#m

(As an aside, I don't know how it was professional of Arena to unmask an unreleased cloaked model on their platform. Also practically, upstream could have been A/B testing multiple variants under same endpoint, casting validity of such pre-announcement tests into question)

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Crazy how their models always come out after the US labs and just lag the performance of top models. Almost like they are performing distillation attacks... how strange.
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distillation attack? why the violent word choice? When OpenAI crawled Github was that an attack?
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Do you have moat if your advanced model can be distilled in a month or two ?
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Distillation is not an attack. It simply a way to train a model. Not doing it when you are behind is akin to snatching defeat from the jaws of victory.
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It is an attack at a sufficient level of sophisticated analysis. If you destroy the game theoretic first mover advantage, then you destroy the economic incentive to improve things.
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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.

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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.

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> 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.

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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.
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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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Yes, almost all work people share which seeks to measure the capabilities and differences of models needs to get more precise. We are clamoring to say something meaningful about these things.
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But even that isn't the whole story because the models can produce wildly amount of thinking output as well as regular output for a similar query. Sometimes you can take a cheap model and have it think a ton or an expensive model that thinks little and get similar results. But the number of tokens generated will be wildly different.
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A better metric is price per byte. Most thinking traces, prompts, skills are in plain English, which is roughly 1 byte per character, assuming UTF-8 encoding (even code should not be much more either). As an aside, it is common to use bits-per-byte as a loss metric instead of the per token calculation, precisely because of the effect of different tokenizers.
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It's going to vary dramatically based on which text you put in. Really it's hard to make one benchmark number that's relevant to all cases. But maybe we can make something a little more specific, like regular English text, code, the model's own thinking tokens, image inputs etc.
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It is kind of a shame we ended up comparing token pricing across models and providers when it doesn’t really make sense. Not sure what would be better though.
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Use price per page (standard English text)? That would also help make the metric easier to visualize.

If you think a page is too vague, use a famous known writer's work as a reference.

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Well isn't that what benchmarks are for? They compare total cost for a unit of work.
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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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> Please don’t respond if you are speculating.

I doubt you are going to get a response from an anthropic employee, but I think it is safe to assume they have swapped to a new tokenizer because it improves the performance of their models.

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> the reason for the newer apparently less efficient Anthropic token encoding

Less efficient in token usage but per the blogs; it enables the model to perform better.

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With that kind of pricing, I don't think they're competing with GLM with this new launch.
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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.
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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.

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> 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.

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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 use V4 flash as my personal agent. It categorizes documents, organizes my calendar, searches information etc. for pennies. Amazing model.

Not very good for programming though.

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And DeepSeek V4 Flash + GLM 5.2 is a really good blend of both (fast/cheap DS + more intelligent GLM)
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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.
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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
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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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DeepSeek is a whole other story. It and a few others are quite economical. But they're also not nearly at the same level.

I can get by working on code strictly in GLM. I can't with DeepSeek. It makes some pretty careless mistakes and isn't a very deep thinker.

It is very useful as a general purpose model for non-coding purposes though.

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I don't know, DeepseekV4 is so dirt cheap that it makes lots of sense to use over Sonnet.
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Compared to the flagship models GLM is still a 1/10th the price on the task I have tested.
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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.

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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!
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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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Anthropic’s position being that it is entitled to train models on the creative works of anyone at any time, but its own slop generators’ outputs are sacred jewels that must be protected from being learned from.
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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.
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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.
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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.

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> 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.

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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.
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Will be interesting to see how it stacks up pricing wise on the various inference providers.
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Agreed re reasoning. I’ve seen this play out with 5x reasoning negating cost savings.
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API prices are amazing, but hosting this on-premise will be real challenge.
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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.
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neither ClosedAI nor Misanthropic will let you use their models without them watching and storing the exchanges indefinitely. no sane company dealing with PII and/or trade secrets allows its employees to use those.
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Is this really true? I was led to believe my company had an enterprise zero data retention agreement with them and it’s why we didn’t get access to Fable

Is there proof of what you’re saying or is it just a guess?

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Read the terms of the ZDR policy with a critical eye. You’ll find that Anthropic retains almost arbitrary rights to retain anything it wants.

https://code.claude.com/docs/en/zero-data-retention

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https://trustedrouter.com/models/moonshotai/kimi-k3 is a good option if you want that to actually be the case.
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AFAIK there’s no ZDR with Claude models accessed directly via Anthropic. You’d have to go through either Google Vertex, Azure or AWS for true ZDR (at least legally/on paper).
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oh, I've no doubt the US government and giga corporations can get zero data retention without ten pages of fine print. the rest of us can't.
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Unless you spend 5min googling and see that you can do zero retention via AWS Bedrock.
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Yeah even the chatgpt teams subscription claims ZDR. I believe the business plan from anthropic does too.

Of course maybe there is some fine print I haven’t read, and obviously I get the point that it may not be trustworthy.

edit: whoops I just checked and the “business”/“teams” plans just agree not to use your data for training

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> zero data retention

Zero data retention is also "trust me dude".

There is no viable way of checking they are actually doing that.

That's assuming they don't put carve-out clauses in, like Anthropic did with Fable, which means data retention is back on the cards, no exceptions.

Also don't forget a zero data retention clause is still subject to the good old "law, or court or administrative order" contract clauses. :)

To get properly close to real zero-retention in a hosted model, you would have to use one of the verifiably private AI that runs in enclaves, e.g. Tinfoil (US) or Privatemode (Germany)[2]. Yes, still not the same as running on your own hardware, but a million lightyears ahead of "zero data retention" "trust me dude" clauses.

[1]https://tinfoil.sh/ [2]https://www.privatemode.ai/

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No I know of course, I don’t trust them as far as I can throw them when all of these companies committed the largest copyright theft in human history to build the models.

I just wanted to know if that other person had proof or not, and I guess they didn’t. I would still rather have some semblance of an agreement than not have one at all — if you’re coding on a consumer plan you should just 100% assume anything you write with it will end up in the training set

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In context it seems your recommendation is to instead send those data to models within Chinese nation-network space. I’m not here to defend US frontier model companies; your accusation is probably accurate. But I doubt sending data to China is an improvement.
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with open weight models, you have three other options

A) use a provider that pinky-swears not to store your data. they obviously don't give a fuck about 'distillation attacks', so they have little motivation to voluntarily monitor and store your queries. reasonably high likelihood of privacy.

B) rent the hardware and run the model yourself. very high likelihood of privacy.

C) buy the hardware and run the model yourself. absolute certainty of privacy.

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That depends entirely on the hosting situation. If someone can provide a subscription plan at slightly lower rates, it's absolutely compelling.
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Moonshot has subscriptions maxing out at $199/month. Not home so not had a chance to see if K3 is included yet.

EDIT: Just switched my Kimi-CLI session to K3 and resumed my ongoing /goal... Will be interesting to see if I notice a difference.

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

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>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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And then I'm of course going to root for getting rid of them.

What alternative would you propose? Currently, there's no alternative I know of, either you rely on the US or on China or both.

Me and many others are doing our best building that alternative and promoting local solutions in all areas, but it takes time. And until then, I'd like to use the one that isn't threatening to steal our territory, thank you very much.

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You are rooting for the dictatorship that has 0 political freedom, devalues their currency and hurts their own population, they kill their people and cover it up, and have no freedom of speech.

Why?

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You did not offer me an alternative. Please don't move the goalposts.

And I'm still not rooting _for_ them, I'm rooting for choosing their services above american ones for the time being. That's quite a different thing, as should be obvious. Respond to things I actually said and not things you think I might possibly think.

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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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No, just aesthetic trivia that can be paraded around to make them look good.

Given how China behaves it should be evident that the only reason they don't apply military force is because they are not in position to. Not abusing military strength is not exactly being the paragon of virtue when your opposition could probably glass the world thrice before the day is over.

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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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I won't go through everything, but just as an example:

You are not mentioning the greenland situation - why? That's the really big one and the one that made the US much closer to "enemy" than "friend". After all, friends don't threaten to annex your territory.

Regarding propagandists and financing of antidemocratic forces: this refers to a current issue. US is deliberately financing spreading of its ideology in the EU, as they confirmed themselves. [0]

With the genocide, that discussion I'm going to stay clear of, as nobody will be convinced of the other position anyway, too heated. Shouldn't have mentioned it in the first place, as this always leads to flamewars. mb.

Regarding honoring of treaties: let's start with the budapest memorandum - I think that was the first really big one. Then, the 1967 Refugee Protocol which forbids third-country deportations. Then, the UN Framework Convention On Climate Change. Violation of the UN charter, withholding of promised funds. The Convention Against TOrture.

Then all the broken/ignored/overturned trade treaties, all the promises made and not kept - how would anything rely on their word at all anymore?

I could go on for multiple pages. Why do those not count? Why do they have to be "propaganda"?

It is unbelievably difficult being reliant on the US in any way right now. And that's what I'm talking about. Not, which is the "better" country. Reliability and ... well, utility to its partners is the basis of it all. Which right now - compared to china - is rapidly sinking. So where is that ignorance you are speaking of?

[0]: https://web.archive.org/web/20260716141817/https://www.thegu...

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>committed to enslaving the Uyghur people

What?

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Context: https://en.wikipedia.org/wiki/Uyghurs

> Since 2014, the Chinese government has been accused of subjecting Uyghurs in Xinjiang to widespread persecution, including arbitrary arrest and detention, forced sterilization, and forced labor. This is denied by China.

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Better than handing it over to the US regime.
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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.
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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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  But thinking China is better?
This is not what they said.
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Or the American one :)
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Sadly these days this seems like the least worse of the three major regimes.
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You are in a bubble. They just raided independent book stores in Hong Kong.
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Everybody is in a bubble. Which is why it's worth looking into other people's bubbles occasionally.

https://www.pewresearch.org/global/2026/07/15/people-in-many...

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I measure good and bad by proximity to me. China can directly hurt me the least, the US can hurt me the most.
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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.
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I have absolutely zero sympathy for Western model providers.

Bring on the Chinese token-dumping onslaught.

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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.
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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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Again, you might be against Chinese government. People aren’t the world perceive China in a better light than the USA right at this moment.
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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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That is correct, but that’s not what I’m talking about. A lot of people complain about handing their data to Chinese government. My argument is, as of today, people like China more than the US. And the American government has publicly said that they’re basically controlling all AI labs if needed. So yeah.
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[flagged]
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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.
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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.

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> 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.

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Ah, the old "subsidized" meme always rearing its head. Yawn.
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