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Comment by nearbuy | Hacker News Reader

This isn't right. You're comparing cost per token, but DeepSeek V4 Flash uses more tokens. Artificial Analysis found GPT 6 Luna to be significantly cheaper than DeepSeek: https://artificialanalysis.ai/models/comparisons?compare=dee...

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I do not (generally) trust benchmarks. I only trust what a model does with MY code.

Forget DS. I asked MiMo 2.6 yesterday to explain ML/LLMs to me succinctly and the pointed it at Karpathy's micrograd code. It produced a C implementation called `xor_mlp`, a tiny model that learnt how `xor` worked. I then asked it to produce a model that can play tictactoe without losing (mostly). It did. It supervised the training process and produced a compiled version with multiple switches. The pi-dev session is still running, so here are actual stats

↑45k ↓35k R1.0M CH99.4% $0.019 4.2%/1.0M (auto) - (opencode-go) mimo-v2.6-flash • high

And here is Luna on the same workflow (I had to poke and prod a bit to get what I wanted):

↑141 ↓34k R1.0M W43k CH95.3% $0.072 4.2%/1.1M (auto) (opencode-go) gpt-5.6-luna • high

I expect similar results from DS41F/MS13. Closer to MiMo costs than Luna.

So the "significantly cheaper" thing may not really hold, more so when Luna has to actually read my codebase to do the stuff that I want rather than rely on world knowledge. The 8-10x cache read cost differential itself will kill the token budget.

Don’t know if it’s still true but with Chinese models, using Western API providers is significantly more expensive and using Chinese providers they will train on your inputs without exception. That has kept me from using these ultra cheap endpoints.

Do you really think Western providers will not train on your data? I have no such illusions.

I try to keep PII out of what I share with LLMs. Otherwise, I do not see the point, really. Very little of my code is "unique." I simply approach things a bit differently. Otherwise the algorithms and code would be similar to what others with domain knowledge would write. So much of code and algorithm implementations are available in the open. And LLMs have trained on all of them.

What they most probably gain from you is your prompts and your thinking approach more than the code.

The meager difference is that, in theory, you can eventually sue people in the US.

In theory.

Also, this is a feature for people who live in America, and mostly irrelevant for everyone in the global south.

As a European, I honestly don't see a difference between the US and China from this perspective. They are both equally untrustworthy in my book.

Don't understand why you are downvoted.

As a Western European, I see the same untrustworthiness in Europe.

We just have our personal privacy security theater in the form of GDPR and a feeling of moral supremacy that's been drilled into our heads from primary school on.

GDPR isn’t theatre in many organisations. Yes large tech firms (mostly US) probably ignore or circumvent. But most businesses I’ve worked for have taken concrete steps to reduce the data they hold and consider how it’s being used asa direct consequence of gdpr.

People use LLMs for far more personal tasks than just writing code. There are AI journaling apps for instance. And yeah, western providers give you a toggle but I don’t know if that toggle actually does anything or not. They were fine with collecting training data in many morally questionable ways before, no reason for them to stop when you’re literally handing it over to them.

The US labs would lose billions in enterprise contracts if they were found to be secretly training on data when opted-out. It's not worth it.

Great in theory, but what are US enterprises going to do _if_ their private data is later found to be used for training?

1. Not use AI technology and fall behind the rest of the world.

2. Use Chinese AI technology, either hosted by Chinese companies or the models self-hosted.

3. Sue US AI companies for damages, but not enough to have any meaningful impact to such companies that it'd impact US national security goals (per US government contribution to NY Times copyright lawsuit).

There's a huge difference between AI companies exploiting a grey area like training on public corpora and violating a private contract that they explicitly entered into with another party. The latter is very explicitly illegal and would never survive trial in Delaware Chancery court. And all of that is before we get into Federal contracts where training on TS/SCI data could lead to criminal charges.

There's a huge market in the US for providing AI services while respecting client privacy. It makes sense for at least one major provider to offer this.

> Great in theory, but what are US enterprises going to do _if_ their private data is later found to be used for training?

This. And it’s already happening:

> 2. Use Chinese AI technology, either hosted by Chinese companies or the models self-hosted.

> Sue US AI companies for damages

Yes that's the whole point, at least it's an option in the US and Europe. Good luck getting any redress from China. Anthropic was already hit with a $1.5B class-action which would be impossible against a Chinese business.

3. They would sue. And it could have very meaningful impact. NYTimes copyright lawsuit is not a valid comparison because because that's a violation of national/state law which really only matters to the extent that the government is enforcing that stuff which is not the biggest concern rn (these companies are large enough that the threat of the legal costs of fighting in court is not that scary and you'd need a government actually willing to punish them substantially for them to be scared). This stuff would be under contract law against other mega corporations with big legal teams who are also their customers which is a much scarier prospect imo

I somewhat agree, but with limitations:

1. Possible use of differential privacy[1] techniques to train on private data but prevent the release of statistically underpresented facts/data/words. For example, ACME Inc's private data could frequently include the term 'ACMEwidgetPRO' for an upcoming product that is not publicly revealed anywhere else. It would therefore be a bad day for the AI technology company to output 'ACMEwidgetPRO' from one of their public models. Consider now that a few models could be trained--X for public data only, Y for public and private data of ACME Inc together, Z for private data of ACME Inc. A prompt is provided to model Y but output is cross-checked with model X to double check terms such as 'ACMEwidgetPRO' are known in public. If not--provide a "I don't know" response for the prompt.

2. Possible attempted defences similar to "Oops, our model was fine tuned against a model supplied by Temporary18271 Inc (company that no longer exists) and perhaps their model might have been trained on a non-public document which was accidentally exposed to the Internet" that _might_ work occasionally to fob off concern.

3. What recourse does a small or medium company or government especially in a developing country realistically have? They perhaps can't host their own LLMs locally due to availability and cost, can't individually negotiate their own favourable terms with an AI technology company (who cares that much about a potential customer with $100k budget that has no other options anyway), and perhaps can't remain competitive in their industry without heavy use of LLMs.

[1] https://en.wikipedia.org/wiki/Differential_privacy

Yes - but those aren't limitations beyond what I was getting at that's all part of the package of the bland dystopia of late 2026. I think 1 is just a case where it comes down to who has the better lawyers, as is 2. And for 3, yes, also a large government does not have much recourse if they have decided to not flex their muscles. Pretty much the only threat I see as actually viable/scary in this world environment is along the lines of megacorp v megacorp or megacorp v broligarch - and everyone else is just caught in the cross hairs/benefits by accident at best. It would be difficult to argue that the current environment is conducive to consumer protections or equal justice under law.

Oh. Yeah of course… and the US population will revolt if the figure the NSA is spying on them.

> Do you really think Western providers will not train on your data? I have no such illusions.

Noone wants to "train on your data". You can't learn the answers to questions by pretraining on the questions, and nobody wants to teach the models to output text that looks like a user query.

The Chinese providers "train on your data" by sending your query to Anthropic and training on the answers that come back.

> without exception

Is this based on something or just because “they’re Chinese and they’ll do anything to win”.

This is based on my last check of alibaba and Deepseek TOS. If the Chinese will do anything to win, so will the Americans. I’m not American or Chinese and I have no reason to trust either side. I do think Chinese models are better price performance and actually open which is in many cases better.

Why on gods green earth would they not if they can?

it's very well known Deepseek does it, their whole discounted pricing was seemingly priced on that.

you can't even use Alibaba on Openrouter if you enforce ZDR

Check the weights for your training data. You can trust but verify with many Chinese models.

Unfortunately you just have to take the “our AI is going to take your job, then kill you, and we instruct it to hack your infra” people that they aren’t training on your data anyway.

If they are hacking hugging face and Australia to scrape data trust me they have “hacked” their own systems and are training on it.

With GPT-6 Luna (which is what the parent comment was talking about), that would come to 3.2¢, assuming GPT-6 used the same number of tokens.

I don't think you can guess more precisely than an order of magnitude from trying each once on one task.

DS is VERY talkative. Luna is less so. Still do not think, based on this little experiment, that Luna could beat DS in price: API-to-API. As part of a Plus/Pro plan? Sure.

Its style of writing is part of the fun. Seeing reasoning traces fly by that each start with “Hmm…” is pretty amusing in my opinion especially if you try to vocalize it in your mind.

In a discussion about cost effectiveness, how subjectively fun the writing feels like to the reader isn't a factor, except maybe if we were working on writing comedy.

Well, DS shows the thinking stream so it feels that way, but I’ve realized that OpenAI hiding it just gives a false impression - the non thinking output is also very wordy for OpenAI models.

Artificialanalysis benchmark is a combination of a several benchmarks which might or might not represent realistic coding:

"Artificial Analysis Intelligence Index combines performance across 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, and AA-LCR v1.1."

Not saying it doesnt have any value but it's probably irrelevant if you use these AIs for a specific use case. Like for example Humanity Last Exam tests general knowledge, which is not very useful for coding.

It's best to go to the specific coding benchmarks and compare there.

There is too many money involved, benchmark can’t be trusted.

I had a favorite benchmark, SWE-rebench, but sadly it's no longer maintained.

But yeah, I'll just take these benchmarks with a grain of salt. Only hands-on experience matters in the end, and these days it's very easy to switch models.

Does it use less tokens or we just get no accounting of the thinking tokens in OpenAI/Claude models?

According my model,

My cost is (I use nous as provider)

DeepSeek v4-flash-0731 • Your cost: $0.56

DeepSeek v4.1-flash • Your cost: $1.22

GPT-6 Luna • Your cost: $4.22

My usage is heavy on the cache. Apparently v4.1 flash uses 1.75 times as many tokens so still cheaper.

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