The biggest differentiator for me: DeepSeek just does what I ask. I've tried using both GPT and Claude for reverse engineering recently, both refused. I even got a warning on my OpenAI account.
We have an enterprise cursor account so I can try all the mainstream models. Using composer 2 on our own code which I obviously have the source code for I couldn't get it to turn on a debug flag to bypass license checks while I was troubleshooting something. Infuriating. It was like that old Patrick from SpongeBob meme.
I don't understand why we would turn the models into law enforcement officers. Things that are illegal are still illegal and we have professionals to deal with crimes. I don't need Google to be the arbiter of truth and justice. It's already bad enough trying to get accountability from law enforcement and they work for us.
They're probably worried about liability. Let's say that Oracle finds out you reverse engineered their DB using Gemini. You can be sure they will sue Google. Not just for providing the tools, but you could make the argument that it's actually Gemini doing the reverse engineering, and on Google's hardware no less.
We need that lawsuit to happen already so we can establish precedent. The person in the driver's seat of the Tesla should be at fault. The engineer using the llm should be at fault. The person behind the gun not the manufacturer should be at fault.
The difference is IDA Pro doesn’t do something unless you instruct it to, an LLM is unpredictable and may end up performing an action you did not intend. I see it often, it presents me options and does wait for my response, just starts doing what it thinks I want.
> I don't understand why we would turn the models into law enforcement officers
It's a simple corporate risk minimization strategy. Just look at how universally despised Grok is on HN. Not because it's a bad model, but because it has less aggressive alignment which means it can be coaxed into saying things that get Xai pilloried here and elsewhere.
I just think Grok is a bad model. I haven't had success with it.
I also think some of the image generation people are doing with Grok on Twitter is gross. But my issue isn't that Grok allowed those images to be generated, it's that (1) Elon seems to be promoting that sort of use and (2) people are publicly posting the results on Twitter and they're being left up.
No, they've clearly put a lot of work into alignment. It's just that they've been trying to align it with Elon Musk rather than Amanda Askell. Unfortunately the more anti-woke they try to make it, the worse it seems to perform.
This is kind of terrifying to me, regularly. No real manner of recourse to normal people without a following, potential exclusion from real fundamental tooling. Imagine OpenAI goes on to buy 20 companies and now you cant use Figma, Next, whatever just because you once tripped some very foggy line somehow. Not just OpenAI but the entire ecosystem is so... hard to read.
I was asking Gemini about a quote from catch 22 and it kept dying mid stream saying it cant talk about it, god knows why, it had no violent or sexual content -- though that is in the book. I could imagine it dinging my whole workspace account just because ... shrug?...
I know ideally the future is local, but I don't know how real that is for most people at least in the next few years with practical costs and power usage except I guess through a M* processor if you're in that ecosystem.
>Imagine OpenAI goes on to buy 20 companies and now you cant use Figma, Next, whatever just because you once tripped some very foggy line somehow.
Don't worry, you can just make your own Figma, Next, whatever if you have some thousand dollars worth of tokens. This is at least what all of the AI thought leaders have been telling me for the past couple of years.
I was using GPT 5.5 through Cursor recently, and it found what it thought to be a security-related issue. I read the code, didn't see what it was seeing, and said "Run the chain of operations against my local server and provide proof of the exploit."
It thought for a few seconds, then I got a message in the chat window UI saying OpenAI flagged the request as unsafe, and suggested I use a "safer prompt."
Definitely soured me on the model. Whatever guardrails they are putting are too hamfisted and stupid.
The main difference here is not that DeepSeek's model is completely free of censorship (although I'd wager it's less censored), but that it's open-weight. That has two major advantages:
1) If Anthropic/OpenAI/Google bans you - you're screwed, you can't access their model at all, but if DeepSeek bans - you just go to another provider, or host the model yourself.
2) If the model refuses to answer you can uncensor it (and this is getting easier and more automated day-by-day[1]).
I've connected it with my vscode copilot and took it for a ride. I've tried both flash and pro.
For a small POC flash was sufficient enough, quite fast, and dirt cheap. It did stop a few times (maybe latency issue?) but it did a good job.
I used the pro to do some heavy lifting, planning, etc. and it did a fantastic job.
I paid ~10 cents for a small proof of concept, that worked exactly how I prompted it.
For me, this is a real alternative after I cancel my github copilot towards the end of the month..
Deepseek v4 Pro feels like Claude Opus 4.6 in it's personality but here's what I did find out about costs:
I did cut loose Deepseek v4 on a decent sized Typescript codebase and asked it to only focus on a single endpoint and go in depth on it layer by layer (API, DTOs, service, database models) and form a complete picture of types involved and introduced and ensure no adhoc types are being introduced.
It developed a very brief but very to the point summary of types being introduced and which of them were refunded etc.
Then I asked it to simplify it all.
It obviously went through lots of files in both prompts but total cost? Just $0.09 for the Pro version.
On Claude Opus I think (from past experience before price hikes) these two prompts alone would have burned somewhere between $9 to $13 easily with not much benefit.
Note - I didn't use Open router rather used the Deepseek API directly because Open router itself was being rate limited by Deep seek.
I've been having the same experience. Tasks like "go through this entire module and pedantically make it match my preferred styleguide exactly" were not worth a couple dollars with frontier models. It's nice to be able to put deepseek flash on stupid, unnecessary or highly speculative tasks without thinking about the cost.
I find a lot of the inefficiency also comes from the model just randomly poking around and grepping all the time which is the fault of the harness. I ended up building a Prolog based MCP where I use tree-sitter to parse the code into a graph, and then the model can just ask questions like 'what are all the functions connected to this function'. So, in case you're trying to focus on what a particular endpoint is doing, you can trivially and predictably trace the whole subgraphs of calls.
Microsoft just announced the availability of OpenAI GPT-5.5, which they are charging 30x for it. In contrast, they charge 7.5x for Claude Opus 4.6 and 1x for OpenAI GPT-5.4
Check out the token-based pricing, and compare GPT-5.5 with all other models.
I'm guessing downvoted because OpenRouter was mentioned in the note (which may not have been there originally), but aside from that this is a perfectly legitimate question. In order to reproduce we need to know how. Was it a coding agent like opencode, an IDE, or something else?
Only similarity it has to Opus 4.6 is the 4 in the name. I do not understand these dishonest comparisons. OOS models are vool, cheap and promising for a future -- but why are we pretending they are better than they are?
Speak for yourself. I found switching from Opus 4.7 to be completely painless and in fact, due to the reliability of Anthropic’s API, less of a friction despite slower response times. Zero issues on a large mono repro
Hi, I am happy it works well for you. For me personally I struggle finding good use-cases in general for these OOS models. I am lightly technical but I do not manually code. So my flow is /grill-me (can take hours), make plan, review plan with 2. model, implement, review after implementation.
Maybe it is because my tasks are usually chunkier, or because I cant code myself that I struggle using cheaper models. Feels like at every stage of this process SOTA model improves it by 5%, which adds up.
But I am maybe ignorant of Opus level. My main driver is 5.5 and Opus is there for frontend and 2. opinion. In a past I also used Claude models for the chatting phase, but 5.5 took over recently. Maybe Deepseek is closer to Opus and I just overestimated the model compared to 5.5? I tried to give it benefit of being similar.
Recently I started experimenting with Deepseek Flash, maybe hoping if plan is solid enough it can implement quickly and cheaply, but for now it feels not worth it.
How do you use the model to see the benefits? Have you tried 5.5 and can you compare to that one as well?
What provider are you using? I have it a shot through open router and saw some weird half formed words coming through occasionally, would love to switch over and give it a proper go
V4 is definitely a step-up from V3.2 on our multilingual benchmarks.
Two caveats:
- when inferring through Openrouter, we've had a lot of issues with very slow speeds (TPS) and an occasional instability. I just checked and it's still 10-30 TPS on all available providers, which is not a lot for a model that likes to think as much as DeepSeek does.
- the official DeepSeek API makes no guarantees of data privacy even for paying users.
Both points could be moot with using it through Azure AI foundry (the latter is, afaik); I have yet to test that.
In any case, happy to see more open-weights models that are somewhat competitive with SOTA models!
While the cost are lower than frontier models there are two factors that make DS4 Pro and K2.6 not as cheap as they might look.
For DS4 Pro there's a discount going on for the official API, which sometimes gets overlooked and mixed up in discussions. Simon uses the full price in the comparison, so that's not an issue here.
The other issue is that DS4 Pro and K2.6 often use way more reasoning tokens than the frontier models. In my testing there are certain pathological cases where a request can cost the same as with a frontier model because they use so much more tokens.
To be fair I'm using DS and kimi via 3rd party providers, so they might have issues with their setups.
But if you look at the Artificial Analysis pages of the models you'll see that DSv4 Pro uses 190M tokens and K2.6 170M tokens for their intelligence benchmark, while GPT 5.5 (high) only used 45M.[0][1][2]
I recommend looking at the "Intelligence vs. Cost to Run Artificial Analysis Intelligence Index" ("Intelligence vs Cost" in the UI). The open source models are still cheaper to run, but not by as much as you'd think just looking at the token prices.
They introduce very novel methods to improve long context efficiency and attention. HCA & mCH. It requires only 27% of flops for inference and 10% for KV cache than v3.2. This makes it super efficient. Think of this. For flops, we can now serve more than 3x the amount with the same number of compute, and you would need 30% of prior KV cache.
Furthermore, this release is a PREVIEW, DeepSeek is the real open labs and they not only cook up quite a bit with every single release, but they publish and share it. I'm running this locally.
Let me tell you how "CHEAP" this is. With v3.2 I would run out of GPU ram, spill into system ram with 256k context. It ran quite alright and I was happy with my 7tk/sec. With this, I'm 100% in GPU ram with full 1million token, run more than 2x fast while getting better results.
This is super cheap. moonshot has made it clear that they are starved for GPUs and that's why. If they had GPU capacity like we do in US and subsidized the models like we do here, they would be giving it away for free!
Sure that can happen but it hasn’t been my experience. I just spent a whole day using it for some pretty hefty refactors, many rounds of back-and-forths, thousands of lines of code changes, reviews, investigations, many subagents running parallel tasks, the works. Total cost $0.95, altogether.
I had attempted this with Opus 4.6 in the past and it burned through the $10 budget I’d given it before it returned from my initial prompt.
Even if it’s heavily discounted, it would still have cost me single digits for a complete solution vs double-digits for exactly nothing.
I didn't want to say that they're not cheaper to run, artificial analysis also shows that they're cheaper. My main point was about it being important to also look at token efficiency, not only cost per token, to get the full picture.
I agree! I don't find Claude models to be particularly efficient anyway though. Maybe when running through Claude Code? I don't know, I tried it a while back but it didn't suit me and I kept hitting bugs so I dropped it in favour of something that does something closer to what I want rather than what the provider wants!
Mostly OpenCode but I've been experimenting with Pi a bit lately.
I use Agent Hive [0] for more complex tasks. It sends off subagents with models and parameters I can configure for each different agent (i.e. a low-temp coder, a higher temp with some top_k / top_p for research and architecture, etc).
DeepSeek’s official API has a cache hit rate of over 99% if you use it continuously within the same codebase for long sessions, so it’s much cheaper than frontier models. I have an example of 200M token session in claude code.
Yes, you have to use the same session, I guess you could load up a bunch of context, then fork the session into a few different tasks, although I haven't tried it.
Also curious. With tool calls reading/searching different files, possible compacting reading a large codebase / long threads, I can't imagine how you hit 99% cache rate.
This gives me hope that when the subsidization circus ends and everyone is on pure usage then it won't be entirely exclusionary to mere mortals who don't have $200pm budgets.
IMO there are two things that make me optimistic that we won’t see a big rug pull where price-to-capability ratio skyrockets relative to today:
* As you’ve noted, people keep finding ways of slamming more intelligence into smaller models, meaning that a given hardware spec delivers more model capability over time.
* Hardware will continue to improve and supply will catch up to demand, meaning that a dollar will deliver more hardware spec over time.
I hope that one day we’ll look back on the current model of “accessing AI through provider APIs” the same way we now look back on “everyone connecting to the company mainframe.”
I also hope that we’ll find effective ways to distribute load between small local models and heavyweight remote models. Sort of like what Apple tried to do in iOS.
So much of what I ask codex to do doesn’t require full GPT 5 intelligence, and if 75% of the tokens were generated locally that’d save a massive amount of cost.
By the time the dust settles I wouldn't be surprised if personal interactive usage couldn't even be had for under $200. I can't fit my modelling of the serving costs of these things to any public reporting, even the more bearish examples
Not a lot of people have this budget, and I'm not sure how many people with that type of cash are also interested in paying it for AI.
Of course, this is fine for people in the bay area earning hundreds of thousands of dollars a year. But then your client base becomes so reduced its hard to justify the valuation these companies have.
These AI companies are not hyped so much because they will offer a luxury product, they're valued because they're supposed to "change the world" which luxury does not do.
We pay per token in our company. It is not hard to spend $100 for one morning coding session. So thousands per month per programmer. The company finds it valuable enough to pay for, but if I ever paid these from my own pocket I'd look into DeepSeek et.al.
Comes down to what you mean by interactive usage. Most of chat & say openclaw usage is already within self-host range so no need to spend 200 a month on that.
High end SOTA coding is harder, but even there I suspect a mix of usage based strong models and selfhost small is viable if necessary.
DS V4 Pro has rocked. ~250 million tokens through their API, which has cost me about $10, and some of that was at the non-discount rate. So ~$40 at the non-discount rate. I have yet to have a single request feel slow or get rejected.
I've used K2.6, GLM5.1, and DSV4 all a good amount. They're all very impressive, but DSV4 has taken the cake.
I'm surprised that people here don't care at all about these models openly training on your data, especially if you use them straight from the model developer. Whereas things like "GitHub now automatically opts everyone into using their code for model training" get hundreds of justifiably angry comments, I never see this brought up anymore on posts like these talking about using Chinese models through OpenRouter. This might be explained by "well they're different people", but the difference is very stark for that to be the whole explanation.
At least that’s what they’re telling you. It’s a ”trust me bro” scenario.
I’d rather use the phone home version (deepseeks own endpoint). The benefit is that I’m fairly certain that they actually host the model I’m paying for.
Some providers are based in the US or EU and would face legal repercussions for lying about what they do with your data. It's a bit more than "trust me bro". Off the top of my head, you can use Fireworks, for example, which is based in California and would face the same consequences for lying about their data policy as OpenAI or Anthropic would.
I am personally okay helping them as long as they publish the models and dont keep them closed. And I dont trust the settings where providers say they wont train on it.
Because they give it away for free and offer APIs at very acceptable rates. Not that hard to figure out, Robin Hood stealing our data tax back comes to mind.
User publishes to github => Copilot trains with GitHub data => MS Sells copilot => User workes for Microsoft (in the sense of giving it's labour for MS to make money)
User publishes to github => Deepseek trains with GitHub data => Deepseek gives model away for free => User did not work for Deepseek (in the sense of giving it's labour for Deepseek to make money)
I am fine with them training on my open source code (which is pretty bad but not the point, because they're providing the service for free). I will be super pissed if I pay for enterprise and they train on it though. I believe this is the opinion of majority programmers.
You definitely have a bone to pick. Chinese researchers usually have given the world the most cheap and consistent high quality research around LLMs. They don't pretend, they do the work and release the goodies. Mostly so cheap, every one in the world has a chance to use close to frontier models. Why would you respond with "Anger"?
You let us know what your real complaint is about and let's not feign indignation at open models and research.
I made no such claims. Maybe you have something to share about why we need to have a negative view of free and open models based on publicly available frontier research.
My policy is that I don't allow agents to access all code. Some of it is shielded behind bind mounts. Maybe this is a pathetic, artisanal (or ego-driven), reaction of mine to the inevitable. I allow them to work on about 90% of the code (most codebases fully), with some code being considered too valuable to expose to the vendor. When data is involved, LLMs only get to see anonymized data.
This cute policy of mine won't affect anything though. The more we use the models, the more the models will replace this kind of work. Centralisation of power is inevitable; in Medival Europe, we used to have state & church ruling. In modern times but before the internet, it was probably state and banks. Maybe with ongoing digitization (bank offices disappearing) making banks less costly to operate; combined with with bank bailouts, maybe govenments will fully nationalize or at least banks will consolidate.
Then the AI companies will consolidate with the internet information and communication companies (Google/Meta for the US, and Alibaba/Tencent for China). Maybe we'll end up with a few de-facto governmental megacorps that rule in tandem and close cooperation with the formal government, who might handle mostly infra, utilities and the army. The megacorp would control narrative more and take more of a paternal role (educating and protecting the citizens, normally handled by formal governments).
It's totally fair to use GPL code, it just means all the models built by Anthropic, OpenAI, etc. using GPL-licensed source are themselves bound by the GPL. Plus, any works created downstream using those AI tools.
We're on the verge of a golden age of software as soon as someone finds a court with courage.
I think AI will create an open source dark age. Gradually, we'll see a lot less new good open source code. A gradual shift back to the proprietary world. Simmilar to the 1950-1990 period.
Things being public should not be enough. just because someone leaked your medical information to the public via a data breach should not make it fair game. There should be some rules.
Do you really think OpenAI, Anthropic or any other entity in the same business respects your data?
The Chinese AI companies who release open weights actually deserve whatever input you give them. They are the reason why there is competition and not duopolies in the domain.
I think Google, and likely Anthropic, indeed do honor the settings chosen by the user. For Google in particular it'd be very surprising if they didn't. That's also why both do everything they can to trick users into allowing it.
OpenAI, I wouldn't be surprised if you were right.
unfortunately the history of these big tech companies has shown that they do not care about data privacy and are even willing to lie about it. but I guess its irrelevant, in practice you have to assume the worst anyway since there is no way to verify it
AWS Bedrock has DeepSeek models running on their infrastructure. That should be enough to prevent training on user data (there's a markup compared to DeepSeek's pricing though).
And unfortunately AWS doesn't have prepaid billing, so you can't just give the internet access to your API key without getting FinDDoS'd.
What do you mean specifically? Data passed through OpenRouter? Or that they too indiscriminately ingest data all over the web? If the former, I assume it's just that anyone still using them just doesn't care where the data comes from. If the latter, well, it seems like every day there's some news on some new model from somewhere, and it takes dedication to complain every time. There's also the factor that I believe DeepSeek is more open with the model, while others keep it entirely proprietary, which feels fairer and (personally) is also less offensive.
Two factors. First is anti-americanism (or at least anti-american-capitalism).
But the more important one is the social contract. Github came far before LLM era. The branding around it is being the storage of open source projects and many users want to it stay away from AI hype. You won't expect LLM providers to stay away from AI hype (duh) so it's less an issue for them.
I've been using v4 pro for the past few days and honestly in terms of quality it seems more or less on par with open AIs 5.4 or opus 4.6 (i havent tried 4.7)
To be clear, i'm not doing state of the art stuff. I mostly used it for frontend development since i'm not great at that and just need a decent looking prototype.
But for my purposes it's a perfectly good model, and the price is decent.
I can't wait for open model small enough for me to run locally come out though. I hate having to rely on someone elses machines (and getting all my data exfiltrated that way)
You can use Tinfoil for inference, which lets you use the model in the cloud while getting similar privacy as running locally: https://tinfoil.sh/inference.
Disclaimer I'm the cofounder. This works by running the model inside a secure enclave (using NVIDIA confidential computing) and verifying the open source code running inside the enclave matches the runtime attestation. The docs walk you through the verification process: https://docs.tinfoil.sh/verification/verification-in-tinfoil
Hi there I use your service. It's great. But I have a few requests... Please support crypto payments...? Also you are missing some open source models (qwen 30b 3a, Deepseek 4 flash).
Tinfoil looks super interesting! Do you have load balancers in front of the trusted compute stack? Looked at a design like this in a different space and the options for ensuring privacy in a traditional "best practice" architecture seemed very limited
I've been using the planning framework from Matt Pocock on very typical brownfield code. I use a harness over claude code, this is so cheap that I would be tempted to mirror my initial prompt to it and compare their responses to the task.
Yeah even the Chinese open models have a problem that inference costs for these aren't that cheap. The only way out for the AI bubble collapse is simply more efficient hardware at lower costs and infrastructure setup downtime.
You can imagine the GPUs cost as fixed, then your costs becomes energy. Efficient hardware and lower costs will pop the bubble faster. The only way out is profit.
I'm currently paying for Anthropic's Max subscription (the 100 USD one) and I quite often hit or approach the 5 hour limits, but usually get to around 60-80% of the weekly limits before they reset (Opus 4.7 with high thinking for everything, unless CC decides to spawn sub-agents with Haiku or something).
Those tokens are heavily subsidized, but DeepSeek's API pricing is looking really good. For example, with an agentic coding setup (roughly 85% input, 15% output and around 90% cache reads) I'd get around 150M tokens per month for the same 100 USD. Even at more output tokens and worse cache performance, it'd still most likely be upwards of 100M.
What would be the non-subsidized price for a V4 api? Can it be priced 3x cheaper than bigger models? In Openrouter, this 1600B param model costs 0.4$. Whereas Kimi 2.6, 1000B params is 0.7; GLM 5.1, 754B params is 1.0$.
The 150M assumption of mine is for 100 USD at the regular prices (though even that needs sufficient cache hits). Anthropic subsidizes way more per-token I think, though.
I tweeted about some implementation and review runs that used V4 Pro.
Even without the currently discounted pricing, the value is incredible.
It takes about twice as long to finish code reviews given an identical context compared to opus 4.7/gpt 5.5 but at 1/10 the cost of less, there's just no comparison.
I tried deepseek v4 through open code at the weekend. I'm a daily Claude/Claude code user.
I tried to build something simple and while it got the job done the thinking displayed did not fill me with confidence. It was pages and pages of "actually no", "hang on", "wait that makes no sense". It was like the model was having a breakdown.
Bear in mind open code was also new to me so I could be just seeing thinking where I usually don't
And before that they summarized it. But yeah, thinking was always like that (when it first started, it almost just seemed like a scheme to massively increase token use..)
> It tried to build something simple and while it got the job done the thinking displayed did not fill me with confidence. It was pages and pages of "actually no", "hang on", "wait that makes no sense". It was like the model was having a breakdown.
It has been probanly trained to assess its own "thoughts" regularly and outputs those for the assesment results. I wouldn't worry much about the reasoning text contents, and it's nice to have them in contrast to the closed model "summaries", so it's easier to see what's going on.
You can just use it through Claude Code, so you get to keep the system prompt and tooling you are used to.
3rd party models are a drop-in replacement with `ANTHROPIC_BASE_URL` in Claude Code, something people seem to miss right now. And contrary to what Anthropic might like to have you think, you don't need Opus 4.7 to run the harness to get similar performance.
I feel the reasoning might be tuned for hard questions and not agentic work. I feel it overthinks, good for a very hard question, not for small incremental agentic steps. In theory, disabling thinking and using really well formed instruction, forcing it to still emit a bunch of tokens each step prior to taking action, could help. Only one way to find out though.
Opus 4.6 and GPT 5.4 do the same thing through GH Copilot and Bedrock. I get plenty of "Actually the simplest solution is ..., wait no, actually I should do ..., the best fix is ..."
Eh, you're seeing raw thinking tokens. With Claude <x> 4, and I think GPT-5 series, you are no longer seeing real thinking tokens, but "summarized" tokens that are probably highly different to the raw thinking.
Jensen has a point. I believe these were trained and run on Huawei chips. The Nvidia embargo may backfire on American leadership as necessity gives way to invention.
Isn't it widely speculated that these are distilled from current frontier models? Distillation is far less compute intensive than primary training. That said, if distillation produces something almost as good for a fraction of the cost, Jensen's point may stand.
You can't really distill a model without access to the internal weights. You could train on chat logs, but that's absolutely not the same thing, it doesn't even come close to comprehensively "extracting" the model's capabilities. And everyone does that in the industry anyway ever since ChatGPT was first released, some versions of Opus even claimed to be DeepSeek if you prompted them in Chinese.
Calling it distillation does however make normies go along with it when they inevitably add all the Chinese labs to the entities list to pad Dario and Sam’s pockets.
It's too late already, that ship has long sailed. China has the know how in software and hardware. They don't need American tech, they just want it because it's convenient.
The embargo won't backfire, because any delay of China's development was worth it to the US. The situation was never, "China wasn't developing AI chips, now it is", it was always, "China IS developing their own AI chips, let's just slow them down as much as we can."
I recently switched from Claude to Opencode Go + pi.dev. It has Deepseek v4 pro along with Kimi K2.6, and it's performing quite well for basic coding, without hitting any limits.
The pelican is really getting old as an a standalone evaluation metric. By now they are certainly going to be in training set if not explicitly tuned to produce it for the press on HN alone.
Keep the pelican but isn’t it time to add something else more novel that all current and past models struggle with?
One shot canvas and svg images or animations are also just something that at this scale shouldn't be an issue at all, even Qwen running locally on 24gb cards can do impressive ones.
Don't understand why this test gets any attention, I mean other than the pelicans which isn't a good test, theres no meat in this article.
I'm not sure I'd call it "almost on the frontier," but I do think that v4 Pro is the most usable coding model I've seen out of China. I've used it via Ollama Cloud (coding) and OpenRouter (data processing). Feels Sonnet-level to me -- solid at implementation when given a specification, but falls a good bit short of Opus 4.7 max thinking when planning out larger changes or when given open-ended prompts.
Glm5.1 is fantastic for me. But that could be how I use it, I don't ask it to build entire apps or entire features, instead asking it to build piecemeal functionality. For that it compares very well to chatgpt 5.4 (I haven't extensively tried 5.5, it might be better, might be same). I have given deepseekv4 pro a try but not much more than a try, as it performed subpar on 4 tasks in a row (missing the obvious/intended path, generating subpar slightly buggy code to make things work the not obvious way) , I gave up on it.
Glm5.1 for me was a bit of a llama3.1 moment (first open model i could chat with that was usable in manging my inputs the intended way) for code, the first open model that was actually usable.
> Kimi K2.6 a shot for coding? They outperform Deepseek v4 pro
I think this probably depends quite a bit on the specific problem. I'm finding that Deepseek v4 Flash often outdoes Kimi 2.6 on a variety of coding problems that involve complex spatial reasoning
Oh that's quite interesting and hasn't been my experience with regular backend code specifically with respect to tool calling. However that could be because the tool calling format in vllm for Deepseek v4 was broken until a few days ago and that's how I'm running it.
I've been hearing amazing things about Flash, I should give it a try.
Has anybody used V4 hard, for the most challenging tasks (agentically, locally)? It's so hard to compare without putting serious time in it. Like spending a year daily with the model.
I tried it for two tasks using Claude Code, on max effort.
1. Web platform, asking it to analyse a feature to create reports, and coming up with better solution and better UX. it did great, I would say on par with Sonnet 4.6 or even opus considering the thinking and explanation
2. Mac app with some basic functionality, it did well from functional perspective but then I used Opus 4.7 to evaluate and suggest improvements, where I noticed it missed many vital points in design system and usability.
I think it’s a leap, I haven’t used a model this capable that is not OpenAI or Anthropic
The V3/R1 time and now are in such contrast. V3/R1 were hyped hard and barely usable for coding. V4 is much less hyped but (anecdotally) it has completely demolished all the Flash/Lite/Spark models.
Because V4 doesn't even beat Kimi K2.6 and GLM 5.1, which have been out longer. It's only talked about as much as it is because it's Deepseek and R1 was the first open source reasoning model. V4 isn't even multimodal (unlike Kimi) and the 1M context doesn't seem to perform particularly well.
Huh? R1 was one of the earliest openly available MoE and reasoning models, that's definitely not "hype". People tried to do reasoning before by asking the model to "think it through step by step" but that was a hack. The later V3.1 and V3.2 releases AIUI unified reasoning/non-reasoning use under a single model.
DeepSeek V4 Pro has about 25GB worth of active parameters, so if you can fit the whole ~870GB weights + cache in RAM your tok/s is bounded above by 25GB divided into your system memory bandwidth in GB/s. If you can't fit your whole model in RAM you'll be bottlenecked to some degree by storage bandwidth which is in the single or low double digits in GB/s.
Mind you, it's an absolutely sensible setup either way if you are just testing a few queries and are willing to run them unattended/overnight. Especially since the KV-cache size is apparently really low (~10GB is said to be typical) so you get a lot of batching potential even in consumer setups, which amortizes the cost of fetching weights.
Is there real evidence that the volume was meaningful for distillation vs say extensive benchmarking and testing?
It’s certain all the labs use each others APIs extensively for testing - what’s the actual evidence that Deepseek was at significantly higher scale etc.?
Aw man, I'm going to shed a tear, the poor AI companies that stole books, works of art, writings any anything they could get their grubby hands on while happily telling everyone that their jobs are over by the exabyte are getting their precious little tokens stolen by big evil chinese LLMs :(
It's morally right to fuck over Anthropic (and OpenAI, or any other lab). Works generated by AI are not copyrightable anyways, and their terms of service have zero legal value.
I realize this post is about the pelican test, but in regards to coding, has anyone tried out the advisor strategy with V4?[0]
e.g. Have V4 call out to Opus when it's uncertain, but otherwise handle execution.
The results with Sonnet/Haiku in the blog post seemed promising, so I'm curious how it would go with these latest open models.
[0] https://claude.com/blog/the-advisor-strategy
The biggest differentiator for me: DeepSeek just does what I ask. I've tried using both GPT and Claude for reverse engineering recently, both refused. I even got a warning on my OpenAI account.
We have an enterprise cursor account so I can try all the mainstream models. Using composer 2 on our own code which I obviously have the source code for I couldn't get it to turn on a debug flag to bypass license checks while I was troubleshooting something. Infuriating. It was like that old Patrick from SpongeBob meme.
I don't understand why we would turn the models into law enforcement officers. Things that are illegal are still illegal and we have professionals to deal with crimes. I don't need Google to be the arbiter of truth and justice. It's already bad enough trying to get accountability from law enforcement and they work for us.
They're probably worried about liability. Let's say that Oracle finds out you reverse engineered their DB using Gemini. You can be sure they will sue Google. Not just for providing the tools, but you could make the argument that it's actually Gemini doing the reverse engineering, and on Google's hardware no less.
We need that lawsuit to happen already so we can establish precedent. The person in the driver's seat of the Tesla should be at fault. The engineer using the llm should be at fault. The person behind the gun not the manufacturer should be at fault.
We shouldn't need a lawsuit. The legislative branch should pass a law clarifying those things, that's their job.
Let's say that Oracle finds out you reverse engineered their DB using IDA Pro. Would you expect Oracle to sue Hex Rays?
I don't understand why everything changes as soon as an LLM is involved. An LLM is just software.
The difference is IDA Pro doesn’t do something unless you instruct it to, an LLM is unpredictable and may end up performing an action you did not intend. I see it often, it presents me options and does wait for my response, just starts doing what it thinks I want.
If they thought they would succeed, no doubt oracle would sue. I expect bad behavior from multinationals, especially oracle
They would not even expect it to succeed, just make an example of the company (the lawsuit is the punishment) to discourage others.
> I don't understand why we would turn the models into law enforcement officers
It's a simple corporate risk minimization strategy. Just look at how universally despised Grok is on HN. Not because it's a bad model, but because it has less aggressive alignment which means it can be coaxed into saying things that get Xai pilloried here and elsewhere.
It's mostly just a bad model. Plenty of people would be willing to overlook the baggage if the model was even marginally better than the competition.
I just think Grok is a bad model. I haven't had success with it.
I also think some of the image generation people are doing with Grok on Twitter is gross. But my issue isn't that Grok allowed those images to be generated, it's that (1) Elon seems to be promoting that sort of use and (2) people are publicly posting the results on Twitter and they're being left up.
No, they've clearly put a lot of work into alignment. It's just that they've been trying to align it with Elon Musk rather than Amanda Askell. Unfortunately the more anti-woke they try to make it, the worse it seems to perform.
Grok is despised because it has more aggressive alignment.
Maybe control is also profitable.
> I even got a warning on my OpenAI account.
This is kind of terrifying to me, regularly. No real manner of recourse to normal people without a following, potential exclusion from real fundamental tooling. Imagine OpenAI goes on to buy 20 companies and now you cant use Figma, Next, whatever just because you once tripped some very foggy line somehow. Not just OpenAI but the entire ecosystem is so... hard to read.
I was asking Gemini about a quote from catch 22 and it kept dying mid stream saying it cant talk about it, god knows why, it had no violent or sexual content -- though that is in the book. I could imagine it dinging my whole workspace account just because ... shrug?...
I know ideally the future is local, but I don't know how real that is for most people at least in the next few years with practical costs and power usage except I guess through a M* processor if you're in that ecosystem.
>Imagine OpenAI goes on to buy 20 companies and now you cant use Figma, Next, whatever just because you once tripped some very foggy line somehow.
Don't worry, you can just make your own Figma, Next, whatever if you have some thousand dollars worth of tokens. This is at least what all of the AI thought leaders have been telling me for the past couple of years.
>> I even got a warning on my OpenAI account.
I was using GPT 5.5 through Cursor recently, and it found what it thought to be a security-related issue. I read the code, didn't see what it was seeing, and said "Run the chain of operations against my local server and provide proof of the exploit."
It thought for a few seconds, then I got a message in the chat window UI saying OpenAI flagged the request as unsafe, and suggested I use a "safer prompt."
Definitely soured me on the model. Whatever guardrails they are putting are too hamfisted and stupid.
> even got a warning on my OpenAI account
Edit: https://chatgpt.com/cyber
> https://openai.com/cyber
that link 404s
Yikes. Thx. It is https://chatgpt.com/cyber
Announcements:
Introducing Trusted Access for Cyber, https://openai.com/index/trusted-access-for-cyber/ (Feb 2026)
https://openai.com/index/scaling-trusted-access-for-cyber-de... (Apr 2026)
Silicon Valley has do to dirty tricks now. Next phase is they win....
"A Dark-Money Campaign Is Paying Influencers to Frame Chinese AI as a Threat" - https://www.wired.com/story/super-pac-backed-by-openai-and-p...
Are you kidding? Ask this question and see what answer you get: What famous photo depicts a man standing in front of a line of tanks?
Are you kidding?
The main difference here is not that DeepSeek's model is completely free of censorship (although I'd wager it's less censored), but that it's open-weight. That has two major advantages:
1) If Anthropic/OpenAI/Google bans you - you're screwed, you can't access their model at all, but if DeepSeek bans - you just go to another provider, or host the model yourself.
2) If the model refuses to answer you can uncensor it (and this is getting easier and more automated day-by-day[1]).
[1] -- https://github.com/p-e-w/heretic
I've connected it with my vscode copilot and took it for a ride. I've tried both flash and pro. For a small POC flash was sufficient enough, quite fast, and dirt cheap. It did stop a few times (maybe latency issue?) but it did a good job. I used the pro to do some heavy lifting, planning, etc. and it did a fantastic job. I paid ~10 cents for a small proof of concept, that worked exactly how I prompted it.
For me, this is a real alternative after I cancel my github copilot towards the end of the month..
Deepseek v4 Pro feels like Claude Opus 4.6 in it's personality but here's what I did find out about costs:
I did cut loose Deepseek v4 on a decent sized Typescript codebase and asked it to only focus on a single endpoint and go in depth on it layer by layer (API, DTOs, service, database models) and form a complete picture of types involved and introduced and ensure no adhoc types are being introduced.
It developed a very brief but very to the point summary of types being introduced and which of them were refunded etc.
Then I asked it to simplify it all.
It obviously went through lots of files in both prompts but total cost? Just $0.09 for the Pro version.
On Claude Opus I think (from past experience before price hikes) these two prompts alone would have burned somewhere between $9 to $13 easily with not much benefit.
Note - I didn't use Open router rather used the Deepseek API directly because Open router itself was being rate limited by Deep seek.
I've been having the same experience. Tasks like "go through this entire module and pedantically make it match my preferred styleguide exactly" were not worth a couple dollars with frontier models. It's nice to be able to put deepseek flash on stupid, unnecessary or highly speculative tasks without thinking about the cost.
> would have burned somewhere between $9 to $13 easily with not much benefit
With not much benefit compared to DeepSeek v4 Pro @ 9 cents (1/100th of the price) or did neither offer any benefit?
I find a lot of the inefficiency also comes from the model just randomly poking around and grepping all the time which is the fault of the harness. I ended up building a Prolog based MCP where I use tree-sitter to parse the code into a graph, and then the model can just ask questions like 'what are all the functions connected to this function'. So, in case you're trying to focus on what a particular endpoint is doing, you can trivially and predictably trace the whole subgraphs of calls.
https://github.com/yogthos/chiasmus
Chiasmus Looks very cool. I might have a use for it because I like to use LLM harnesses to explore code. Thanks.
Awesome, and feel free to open issues if you find anything missing that would be useful.
Even taking into account the fact that they are billing at 75% discount it's still quite cheaper
Aren't they all billing at discount?
> Aren't they all billing at discount?
Microsoft just announced the availability of OpenAI GPT-5.5, which they are charging 30x for it. In contrast, they charge 7.5x for Claude Opus 4.6 and 1x for OpenAI GPT-5.4
Check out the token-based pricing, and compare GPT-5.5 with all other models.
https://docs.github.com/en/copilot/reference/copilot-billing...
Anthropic's and OpenAI's costs seem to include a fairly ok margin, from the very fourth hand info I have.
In total, how many hands do you have?
Enough to reach the bottom of the rabbit hole.
Those aren't their hands.
How did you use it? OpenRouter, or provider directly?
I'm guessing downvoted because OpenRouter was mentioned in the note (which may not have been there originally), but aside from that this is a perfectly legitimate question. In order to reproduce we need to know how. Was it a coding agent like opencode, an IDE, or something else?
Only similarity it has to Opus 4.6 is the 4 in the name. I do not understand these dishonest comparisons. OOS models are vool, cheap and promising for a future -- but why are we pretending they are better than they are?
Speak for yourself. I found switching from Opus 4.7 to be completely painless and in fact, due to the reliability of Anthropic’s API, less of a friction despite slower response times. Zero issues on a large mono repro
Hi, I am happy it works well for you. For me personally I struggle finding good use-cases in general for these OOS models. I am lightly technical but I do not manually code. So my flow is /grill-me (can take hours), make plan, review plan with 2. model, implement, review after implementation.
Maybe it is because my tasks are usually chunkier, or because I cant code myself that I struggle using cheaper models. Feels like at every stage of this process SOTA model improves it by 5%, which adds up.
But I am maybe ignorant of Opus level. My main driver is 5.5 and Opus is there for frontend and 2. opinion. In a past I also used Claude models for the chatting phase, but 5.5 took over recently. Maybe Deepseek is closer to Opus and I just overestimated the model compared to 5.5? I tried to give it benefit of being similar.
Recently I started experimenting with Deepseek Flash, maybe hoping if plan is solid enough it can implement quickly and cheaply, but for now it feels not worth it.
How do you use the model to see the benefits? Have you tried 5.5 and can you compare to that one as well?
Thanks.
What provider are you using? I have it a shot through open router and saw some weird half formed words coming through occasionally, would love to switch over and give it a proper go
V4 is definitely a step-up from V3.2 on our multilingual benchmarks.
Two caveats: - when inferring through Openrouter, we've had a lot of issues with very slow speeds (TPS) and an occasional instability. I just checked and it's still 10-30 TPS on all available providers, which is not a lot for a model that likes to think as much as DeepSeek does.
- the official DeepSeek API makes no guarantees of data privacy even for paying users.
Both points could be moot with using it through Azure AI foundry (the latter is, afaik); I have yet to test that.
In any case, happy to see more open-weights models that are somewhat competitive with SOTA models!
While the cost are lower than frontier models there are two factors that make DS4 Pro and K2.6 not as cheap as they might look.
For DS4 Pro there's a discount going on for the official API, which sometimes gets overlooked and mixed up in discussions. Simon uses the full price in the comparison, so that's not an issue here.
The other issue is that DS4 Pro and K2.6 often use way more reasoning tokens than the frontier models. In my testing there are certain pathological cases where a request can cost the same as with a frontier model because they use so much more tokens. To be fair I'm using DS and kimi via 3rd party providers, so they might have issues with their setups.
But if you look at the Artificial Analysis pages of the models you'll see that DSv4 Pro uses 190M tokens and K2.6 170M tokens for their intelligence benchmark, while GPT 5.5 (high) only used 45M.[0][1][2]
I recommend looking at the "Intelligence vs. Cost to Run Artificial Analysis Intelligence Index" ("Intelligence vs Cost" in the UI). The open source models are still cheaper to run, but not by as much as you'd think just looking at the token prices.
[0] https://artificialanalysis.ai/models/deepseek-v4-pro [1] https://artificialanalysis.ai/models/kimi-k2-6 [2] https://artificialanalysis.ai/models/gpt-5-5-high
This is very false DS4 is super cheap. I would advise to begin by reading their release paper. https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...
They introduce very novel methods to improve long context efficiency and attention. HCA & mCH. It requires only 27% of flops for inference and 10% for KV cache than v3.2. This makes it super efficient. Think of this. For flops, we can now serve more than 3x the amount with the same number of compute, and you would need 30% of prior KV cache.
Furthermore, this release is a PREVIEW, DeepSeek is the real open labs and they not only cook up quite a bit with every single release, but they publish and share it. I'm running this locally.
Let me tell you how "CHEAP" this is. With v3.2 I would run out of GPU ram, spill into system ram with 256k context. It ran quite alright and I was happy with my 7tk/sec. With this, I'm 100% in GPU ram with full 1million token, run more than 2x fast while getting better results.
This is super cheap. moonshot has made it clear that they are starved for GPUs and that's why. If they had GPU capacity like we do in US and subsidized the models like we do here, they would be giving it away for free!
> I'm running this locally.
Impressive! What is your setup? Are you running the full DeepSeek V4 Pro, or V4 Flash?
I'm running flash. You can run it under 128gb, so a $3000 strix halo would do. My rig tho is 8 Nvidia gpus and spilling over to system ram.
Sure that can happen but it hasn’t been my experience. I just spent a whole day using it for some pretty hefty refactors, many rounds of back-and-forths, thousands of lines of code changes, reviews, investigations, many subagents running parallel tasks, the works. Total cost $0.95, altogether.
I had attempted this with Opus 4.6 in the past and it burned through the $10 budget I’d given it before it returned from my initial prompt.
Even if it’s heavily discounted, it would still have cost me single digits for a complete solution vs double-digits for exactly nothing.
Sounds promising, thanks for your report.
I didn't want to say that they're not cheaper to run, artificial analysis also shows that they're cheaper. My main point was about it being important to also look at token efficiency, not only cost per token, to get the full picture.
I agree! I don't find Claude models to be particularly efficient anyway though. Maybe when running through Claude Code? I don't know, I tried it a while back but it didn't suit me and I kept hitting bugs so I dropped it in favour of something that does something closer to what I want rather than what the provider wants!
What harness do you use?
Mostly OpenCode but I've been experimenting with Pi a bit lately.
I use Agent Hive [0] for more complex tasks. It sends off subagents with models and parameters I can configure for each different agent (i.e. a low-temp coder, a higher temp with some top_k / top_p for research and architecture, etc).
[0] https://github.com/rretsiem/opencode-hive
DeepSeek’s official API has a cache hit rate of over 99% if you use it continuously within the same codebase for long sessions, so it’s much cheaper than frontier models. I have an example of 200M token session in claude code.
Might be a dumb question but do you have to read the files in the same order in new sessions to ensure the correct prefix for the cache?
Yes, you have to use the same session, I guess you could load up a bunch of context, then fork the session into a few different tasks, although I haven't tried it.
Also curious. With tool calls reading/searching different files, possible compacting reading a large codebase / long threads, I can't imagine how you hit 99% cache rate.
Sorry, I was wrong here. I meant a single long session. And there’s no compression, the 1M context is only half used.
This gives me hope that when the subsidization circus ends and everyone is on pure usage then it won't be entirely exclusionary to mere mortals who don't have $200pm budgets.
IMO there are two things that make me optimistic that we won’t see a big rug pull where price-to-capability ratio skyrockets relative to today:
* As you’ve noted, people keep finding ways of slamming more intelligence into smaller models, meaning that a given hardware spec delivers more model capability over time.
* Hardware will continue to improve and supply will catch up to demand, meaning that a dollar will deliver more hardware spec over time.
I hope that one day we’ll look back on the current model of “accessing AI through provider APIs” the same way we now look back on “everyone connecting to the company mainframe.”
I also hope that we’ll find effective ways to distribute load between small local models and heavyweight remote models. Sort of like what Apple tried to do in iOS.
So much of what I ask codex to do doesn’t require full GPT 5 intelligence, and if 75% of the tokens were generated locally that’d save a massive amount of cost.
By the time the dust settles I wouldn't be surprised if personal interactive usage couldn't even be had for under $200. I can't fit my modelling of the serving costs of these things to any public reporting, even the more bearish examples
Not a lot of people have this budget, and I'm not sure how many people with that type of cash are also interested in paying it for AI.
Of course, this is fine for people in the bay area earning hundreds of thousands of dollars a year. But then your client base becomes so reduced its hard to justify the valuation these companies have.
These AI companies are not hyped so much because they will offer a luxury product, they're valued because they're supposed to "change the world" which luxury does not do.
We pay per token in our company. It is not hard to spend $100 for one morning coding session. So thousands per month per programmer. The company finds it valuable enough to pay for, but if I ever paid these from my own pocket I'd look into DeepSeek et.al.
Comes down to what you mean by interactive usage. Most of chat & say openclaw usage is already within self-host range so no need to spend 200 a month on that.
High end SOTA coding is harder, but even there I suspect a mix of usage based strong models and selfhost small is viable if necessary.
DS V4 Pro has rocked. ~250 million tokens through their API, which has cost me about $10, and some of that was at the non-discount rate. So ~$40 at the non-discount rate. I have yet to have a single request feel slow or get rejected.
I've used K2.6, GLM5.1, and DSV4 all a good amount. They're all very impressive, but DSV4 has taken the cake.
I'm surprised that people here don't care at all about these models openly training on your data, especially if you use them straight from the model developer. Whereas things like "GitHub now automatically opts everyone into using their code for model training" get hundreds of justifiably angry comments, I never see this brought up anymore on posts like these talking about using Chinese models through OpenRouter. This might be explained by "well they're different people", but the difference is very stark for that to be the whole explanation.
[delayed]
The cool thing about open-weights model is that you are free to use alternative providers that won't phone home to the original model creators.
I see 6 alternative providers listed on Openrouter for DeepSeek V4 Pro for example.
At least that’s what they’re telling you. It’s a ”trust me bro” scenario.
I’d rather use the phone home version (deepseeks own endpoint). The benefit is that I’m fairly certain that they actually host the model I’m paying for.
[delayed]
Some providers are based in the US or EU and would face legal repercussions for lying about what they do with your data. It's a bit more than "trust me bro". Off the top of my head, you can use Fireworks, for example, which is based in California and would face the same consequences for lying about their data policy as OpenAI or Anthropic would.
I am personally okay helping them as long as they publish the models and dont keep them closed. And I dont trust the settings where providers say they wont train on it.
At this point, that's kind of the reason I use open-weight models through the official providers when I can now.
There's some use cases I won't use a hosted model for, and will only do self hosted.
Otherwise, if they're going to keep releasing open-weight models, I'm going to keep giving them data.
Because they give it away for free and offer APIs at very acceptable rates. Not that hard to figure out, Robin Hood stealing our data tax back comes to mind.
GitHub is free.
User publishes to github => Copilot trains with GitHub data => MS Sells copilot => User workes for Microsoft (in the sense of giving it's labour for MS to make money)
User publishes to github => Deepseek trains with GitHub data => Deepseek gives model away for free => User did not work for Deepseek (in the sense of giving it's labour for Deepseek to make money)
In the first case MS is giving part of Github itself away for free.
Exactly, it's intuitively different.
I am fine with them training on my open source code (which is pretty bad but not the point, because they're providing the service for free). I will be super pissed if I pay for enterprise and they train on it though. I believe this is the opinion of majority programmers.
You definitely have a bone to pick. Chinese researchers usually have given the world the most cheap and consistent high quality research around LLMs. They don't pretend, they do the work and release the goodies. Mostly so cheap, every one in the world has a chance to use close to frontier models. Why would you respond with "Anger"?
You let us know what your real complaint is about and let's not feign indignation at open models and research.
You're making completely unfounded assumptions about me. I use Chinese models myself.
I made no such claims. Maybe you have something to share about why we need to have a negative view of free and open models based on publicly available frontier research.
My policy is that I don't allow agents to access all code. Some of it is shielded behind bind mounts. Maybe this is a pathetic, artisanal (or ego-driven), reaction of mine to the inevitable. I allow them to work on about 90% of the code (most codebases fully), with some code being considered too valuable to expose to the vendor. When data is involved, LLMs only get to see anonymized data.
This cute policy of mine won't affect anything though. The more we use the models, the more the models will replace this kind of work. Centralisation of power is inevitable; in Medival Europe, we used to have state & church ruling. In modern times but before the internet, it was probably state and banks. Maybe with ongoing digitization (bank offices disappearing) making banks less costly to operate; combined with with bank bailouts, maybe govenments will fully nationalize or at least banks will consolidate.
Then the AI companies will consolidate with the internet information and communication companies (Google/Meta for the US, and Alibaba/Tencent for China). Maybe we'll end up with a few de-facto governmental megacorps that rule in tandem and close cooperation with the formal government, who might handle mostly infra, utilities and the army. The megacorp would control narrative more and take more of a paternal role (educating and protecting the citizens, normally handled by formal governments).
Does this make sense?
If the data is opensource on github, then in my opinion it should be fair game.
IMO this is unfair for GPL or similarly licensed code.
Seems ok for MIT like licensed code though
There's no difference. Either you need to follow the license or you don't. MIT has requirements still.
It's totally fair to use GPL code, it just means all the models built by Anthropic, OpenAI, etc. using GPL-licensed source are themselves bound by the GPL. Plus, any works created downstream using those AI tools.
We're on the verge of a golden age of software as soon as someone finds a court with courage.
Ah, you have much more faith in the legal system than I do. It's nice to dream, though.
I think AI will create an open source dark age. Gradually, we'll see a lot less new good open source code. A gradual shift back to the proprietary world. Simmilar to the 1950-1990 period.
Why would giving more people software freedom and the ability to reverse engineer nonfree code result in a dark age?
Things being public should not be enough. just because someone leaked your medical information to the public via a data breach should not make it fair game. There should be some rules.
There are rules. I believe that search engine indexing follows these rules and that so called "training" is search engine indexing.
But a court may differ in the future.
I feel that's a false dichotomy. The code on github is freely available for people to read and learn from, leaked medical data isn't.
I feel that's a flase dichotomy. The code visible on github is freely available for anyone to read and learn from.
As opposed to?
Do you really think OpenAI, Anthropic or any other entity in the same business respects your data?
The Chinese AI companies who release open weights actually deserve whatever input you give them. They are the reason why there is competition and not duopolies in the domain.
I think Google, and likely Anthropic, indeed do honor the settings chosen by the user. For Google in particular it'd be very surprising if they didn't. That's also why both do everything they can to trick users into allowing it.
OpenAI, I wouldn't be surprised if you were right.
unfortunately the history of these big tech companies has shown that they do not care about data privacy and are even willing to lie about it. but I guess its irrelevant, in practice you have to assume the worst anyway since there is no way to verify it
The models doesn’t get better by themselves. You’re naive.
AWS Bedrock has DeepSeek models running on their infrastructure. That should be enough to prevent training on user data (there's a markup compared to DeepSeek's pricing though).
And unfortunately AWS doesn't have prepaid billing, so you can't just give the internet access to your API key without getting FinDDoS'd.
The latest one available for serverless inference looks to be from 8 months (Deepseek v3.1), which is an eternity and far behind.
If anyone is looking for a solution in this space. Fire me an email, I have a partner whose focussed closely on that problem set!
What do you mean specifically? Data passed through OpenRouter? Or that they too indiscriminately ingest data all over the web? If the former, I assume it's just that anyone still using them just doesn't care where the data comes from. If the latter, well, it seems like every day there's some news on some new model from somewhere, and it takes dedication to complain every time. There's also the factor that I believe DeepSeek is more open with the model, while others keep it entirely proprietary, which feels fairer and (personally) is also less offensive.
If they give me the resulting model in the end, they can train on my data all they want. Hell, I'll send them more of it.
Two factors. First is anti-americanism (or at least anti-american-capitalism).
But the more important one is the social contract. Github came far before LLM era. The branding around it is being the storage of open source projects and many users want to it stay away from AI hype. You won't expect LLM providers to stay away from AI hype (duh) so it's less an issue for them.
I've been using v4 pro for the past few days and honestly in terms of quality it seems more or less on par with open AIs 5.4 or opus 4.6 (i havent tried 4.7)
To be clear, i'm not doing state of the art stuff. I mostly used it for frontend development since i'm not great at that and just need a decent looking prototype.
But for my purposes it's a perfectly good model, and the price is decent.
I can't wait for open model small enough for me to run locally come out though. I hate having to rely on someone elses machines (and getting all my data exfiltrated that way)
You can use Tinfoil for inference, which lets you use the model in the cloud while getting similar privacy as running locally: https://tinfoil.sh/inference.
Disclaimer I'm the cofounder. This works by running the model inside a secure enclave (using NVIDIA confidential computing) and verifying the open source code running inside the enclave matches the runtime attestation. The docs walk you through the verification process: https://docs.tinfoil.sh/verification/verification-in-tinfoil
Hi there I use your service. It's great. But I have a few requests... Please support crypto payments...? Also you are missing some open source models (qwen 30b 3a, Deepseek 4 flash).
Tinfoil looks super interesting! Do you have load balancers in front of the trusted compute stack? Looked at a design like this in a different space and the options for ensuring privacy in a traditional "best practice" architecture seemed very limited
Thanks for sharing your experience, I’m looking to try it out.
Which provider are you using for inference? Opencode or the DeepSeek api?
I've been using the planning framework from Matt Pocock on very typical brownfield code. I use a harness over claude code, this is so cheap that I would be tempted to mirror my initial prompt to it and compare their responses to the task.
Do you have a link to this?
From the pricing page of deepseek:
(3) The deepseek-v4-pro model is currently offered at a 75% discount, extended until 2026/05/31 15:59 UTC.
Was this taken into account when reviewing the model?
The article quotes the full price.
obviously everyone subsidizes for user acquisition - after all people need to be coaxed to test your model, claude code subscriptions come to me one.
DeepSeek pro is 65/86% cheaper (i/o tokens) in subsidized pro vs pro and 91/97% cheaper with current subsidies.
Flash vs Sonnet 4.6 is 95/98%
Yeah even the Chinese open models have a problem that inference costs for these aren't that cheap. The only way out for the AI bubble collapse is simply more efficient hardware at lower costs and infrastructure setup downtime.
You can imagine the GPUs cost as fixed, then your costs becomes energy. Efficient hardware and lower costs will pop the bubble faster. The only way out is profit.
It’s just an introduction price to speed up adoption for the rest of the month, hardly worth mentioning compared to subsidized coding plans.
We know DS runs profitable, they also indicate in their paper they expect prices to drop as they get access to the next gen Huawei cards.
I'm currently paying for Anthropic's Max subscription (the 100 USD one) and I quite often hit or approach the 5 hour limits, but usually get to around 60-80% of the weekly limits before they reset (Opus 4.7 with high thinking for everything, unless CC decides to spawn sub-agents with Haiku or something).
Those tokens are heavily subsidized, but DeepSeek's API pricing is looking really good. For example, with an agentic coding setup (roughly 85% input, 15% output and around 90% cache reads) I'd get around 150M tokens per month for the same 100 USD. Even at more output tokens and worse cache performance, it'd still most likely be upwards of 100M.
I am using flash, and it's so good. 150M tokens at $2.
I’ve found that if I turn off auto mode, I get much more usage from the $100/mo plan.
What would be the non-subsidized price for a V4 api? Can it be priced 3x cheaper than bigger models? In Openrouter, this 1600B param model costs 0.4$. Whereas Kimi 2.6, 1000B params is 0.7; GLM 5.1, 754B params is 1.0$.
Here’s their pricing docs, they’re running a discount for now https://api-docs.deepseek.com/quick_start/pricing/
The 150M assumption of mine is for 100 USD at the regular prices (though even that needs sufficient cache hits). Anthropic subsidizes way more per-token I think, though.
Someone on Twitter got >200M tokens for around $10 at the current pricing level
So it begins.
I tweeted about some implementation and review runs that used V4 Pro.
Even without the currently discounted pricing, the value is incredible.
It takes about twice as long to finish code reviews given an identical context compared to opus 4.7/gpt 5.5 but at 1/10 the cost of less, there's just no comparison.
https://twitter.com/aljosa/status/2049176528638902555
Did you do this test through OpenRouter?
Dumb question? Why does pro make a worse pelican than flash?
I tried deepseek v4 through open code at the weekend. I'm a daily Claude/Claude code user.
I tried to build something simple and while it got the job done the thinking displayed did not fill me with confidence. It was pages and pages of "actually no", "hang on", "wait that makes no sense". It was like the model was having a breakdown.
Bear in mind open code was also new to me so I could be just seeing thinking where I usually don't
> "actually no", "hang on", "wait that makes no sense"
Claude does the same thing, claude code just hides the thinking now
I usually like the answers generated by those flows.
And before that they summarized it. But yeah, thinking was always like that (when it first started, it almost just seemed like a scheme to massively increase token use..)
> It tried to build something simple and while it got the job done the thinking displayed did not fill me with confidence. It was pages and pages of "actually no", "hang on", "wait that makes no sense". It was like the model was having a breakdown.
It has been probanly trained to assess its own "thoughts" regularly and outputs those for the assesment results. I wouldn't worry much about the reasoning text contents, and it's nice to have them in contrast to the closed model "summaries", so it's easier to see what's going on.
You can just use it through Claude Code, so you get to keep the system prompt and tooling you are used to.
3rd party models are a drop-in replacement with `ANTHROPIC_BASE_URL` in Claude Code, something people seem to miss right now. And contrary to what Anthropic might like to have you think, you don't need Opus 4.7 to run the harness to get similar performance.
https://api-docs.deepseek.com/quick_start/agent_integrations...
Before CC and Codex removed thinking/verbose and hid most of it, both do that .
Yeah people aren’t aware that we don’t see the actual traces anymore lol
I feel the reasoning might be tuned for hard questions and not agentic work. I feel it overthinks, good for a very hard question, not for small incremental agentic steps. In theory, disabling thinking and using really well formed instruction, forcing it to still emit a bunch of tokens each step prior to taking action, could help. Only one way to find out though.
Opus 4.6 and GPT 5.4 do the same thing through GH Copilot and Bedrock. I get plenty of "Actually the simplest solution is ..., wait no, actually I should do ..., the best fix is ..."
Eh, you're seeing raw thinking tokens. With Claude <x> 4, and I think GPT-5 series, you are no longer seeing real thinking tokens, but "summarized" tokens that are probably highly different to the raw thinking.
use hide_thinking in opencode to get the claude experience :p
I see similar things using GLM 5.1 in pi.
I had to turn off thinking traces because it was just giving me anxiety looking at it.
> Bear in mind open code was also new to me so I could be just seeing thinking where I usually don't
Well there's your problem.
Edit: I remember seeing similar things with ChatGPT or Codex, although I can't remember in which context.
I use in readplace.. oh boy it's SOO good and cheap for summaries!!
Jensen has a point. I believe these were trained and run on Huawei chips. The Nvidia embargo may backfire on American leadership as necessity gives way to invention.
Isn't it widely speculated that these are distilled from current frontier models? Distillation is far less compute intensive than primary training. That said, if distillation produces something almost as good for a fraction of the cost, Jensen's point may stand.
You can't really distill a model without access to the internal weights. You could train on chat logs, but that's absolutely not the same thing, it doesn't even come close to comprehensively "extracting" the model's capabilities. And everyone does that in the industry anyway ever since ChatGPT was first released, some versions of Opus even claimed to be DeepSeek if you prompted them in Chinese.
Calling it distillation does however make normies go along with it when they inevitably add all the Chinese labs to the entities list to pad Dario and Sam’s pockets.
It's too late already, that ship has long sailed. China has the know how in software and hardware. They don't need American tech, they just want it because it's convenient.
These were trained on NVIDIA gpus. It is running inference on Huawei.
The embargo won't backfire, because any delay of China's development was worth it to the US. The situation was never, "China wasn't developing AI chips, now it is", it was always, "China IS developing their own AI chips, let's just slow them down as much as we can."
I recently switched from Claude to Opencode Go + pi.dev. It has Deepseek v4 pro along with Kimi K2.6, and it's performing quite well for basic coding, without hitting any limits.
The pelican is really getting old as an a standalone evaluation metric. By now they are certainly going to be in training set if not explicitly tuned to produce it for the press on HN alone.
Keep the pelican but isn’t it time to add something else more novel that all current and past models struggle with?
One shot canvas and svg images or animations are also just something that at this scale shouldn't be an issue at all, even Qwen running locally on 24gb cards can do impressive ones.
Don't understand why this test gets any attention, I mean other than the pelicans which isn't a good test, theres no meat in this article.
Relevant: https://news.ycombinator.com/item?id=47839493
It also seems like all of the models have converged on very similar images.
I'm not sure I'd call it "almost on the frontier," but I do think that v4 Pro is the most usable coding model I've seen out of China. I've used it via Ollama Cloud (coding) and OpenRouter (data processing). Feels Sonnet-level to me -- solid at implementation when given a specification, but falls a good bit short of Opus 4.7 max thinking when planning out larger changes or when given open-ended prompts.
Have you given GLM 5.1 or Kimi K2.6 a shot for coding? They outperform Deepseek v4 pro.
Glm5.1 is fantastic for me. But that could be how I use it, I don't ask it to build entire apps or entire features, instead asking it to build piecemeal functionality. For that it compares very well to chatgpt 5.4 (I haven't extensively tried 5.5, it might be better, might be same). I have given deepseekv4 pro a try but not much more than a try, as it performed subpar on 4 tasks in a row (missing the obvious/intended path, generating subpar slightly buggy code to make things work the not obvious way) , I gave up on it.
Glm5.1 for me was a bit of a llama3.1 moment (first open model i could chat with that was usable in manging my inputs the intended way) for code, the first open model that was actually usable.
I've never asked LLMs to build a whole app without detailed directions. I've done giving it a general data flow, structs and methods..etc
Are frontier models capable of building something only with general directions now?
I tried Kimi K2.6 but came away underwhelmed -- it is much more expensive / slow but does not feel better to me. Haven't tried the GLM series.
> Kimi K2.6 a shot for coding? They outperform Deepseek v4 pro
I think this probably depends quite a bit on the specific problem. I'm finding that Deepseek v4 Flash often outdoes Kimi 2.6 on a variety of coding problems that involve complex spatial reasoning
Oh that's quite interesting and hasn't been my experience with regular backend code specifically with respect to tool calling. However that could be because the tool calling format in vllm for Deepseek v4 was broken until a few days ago and that's how I'm running it.
I've been hearing amazing things about Flash, I should give it a try.
Keep in mind that DeepSeek has a max thinking mode of its own in the API.
Has anybody used V4 hard, for the most challenging tasks (agentically, locally)? It's so hard to compare without putting serious time in it. Like spending a year daily with the model.
I tried it for two tasks using Claude Code, on max effort.
1. Web platform, asking it to analyse a feature to create reports, and coming up with better solution and better UX. it did great, I would say on par with Sonnet 4.6 or even opus considering the thinking and explanation
2. Mac app with some basic functionality, it did well from functional perspective but then I used Opus 4.7 to evaluate and suggest improvements, where I noticed it missed many vital points in design system and usability.
I think it’s a leap, I haven’t used a model this capable that is not OpenAI or Anthropic
Claude Code poisons non-anthropic models in usage. We found this out when the code was leaked. Use a fork or OpenCode/pi-coding-agent
By poisons, do you mean it degrades their quality of output somehow?
Mind sending where you found this in the leaked code?
That's what an evaluation dataset is for, create your own and you can bench a model in a few hours to see if it fits your needs.
The V3/R1 time and now are in such contrast. V3/R1 were hyped hard and barely usable for coding. V4 is much less hyped but (anecdotally) it has completely demolished all the Flash/Lite/Spark models.
Because V4 doesn't even beat Kimi K2.6 and GLM 5.1, which have been out longer. It's only talked about as much as it is because it's Deepseek and R1 was the first open source reasoning model. V4 isn't even multimodal (unlike Kimi) and the 1M context doesn't seem to perform particularly well.
They were and are still great for coding. They were not trained for agentic workflow and coding harness.
Huh? R1 was one of the earliest openly available MoE and reasoning models, that's definitely not "hype". People tried to do reasoning before by asking the model to "think it through step by step" but that was a hack. The later V3.1 and V3.2 releases AIUI unified reasoning/non-reasoning use under a single model.
There are so many login-free models now that most people will not even try DeepSeek if the access requires a login.
Wanna see ppl fine-tuning it
I doubt if those models already knew this pelican test...
Does it censor mentions of what happened in Tiananmen Square in 1989?
If I want to run 'coding prompts' running the biggest deepseek model on CPU, what is the order of time I will have wait, hours, days?
DeepSeek V4 Pro has about 25GB worth of active parameters, so if you can fit the whole ~870GB weights + cache in RAM your tok/s is bounded above by 25GB divided into your system memory bandwidth in GB/s. If you can't fit your whole model in RAM you'll be bottlenecked to some degree by storage bandwidth which is in the single or low double digits in GB/s.
Mind you, it's an absolutely sensible setup either way if you are just testing a few queries and are willing to run them unattended/overnight. Especially since the KV-cache size is apparently really low (~10GB is said to be typical) so you get a lot of batching potential even in consumer setups, which amortizes the cost of fetching weights.
https://www.reddit.com/r/Hugston/comments/1t1mk0j/comparison...
So I'm involved in an open source AI cli coding assistant called Cecli (cecli.dev) which is specifically designed to work well with DeepSeek.
DeepSeek is a great model, and Cecli is all about efficiency. It works great for my purposes - agentic programming on a budget.
The credit for DeepSeek, in part, goes to US companies such as OpenAI [1] and DeepSeek [2]. Portions of DeepSeek are based on their products.
[1] https://www.reuters.com/world/china/openai-accuses-deepseek-...
[2] https://x.com/AnthropicAI/status/2025997928242811253
Is there real evidence that the volume was meaningful for distillation vs say extensive benchmarking and testing?
It’s certain all the labs use each others APIs extensively for testing - what’s the actual evidence that Deepseek was at significantly higher scale etc.?
How immoral of those LLM developers. The rest of the field does such a good job of crediting their inputs.
And the credit of OpenAI is to Google?
https://arxiv.org/abs/1706.03762
Aw man, I'm going to shed a tear, the poor AI companies that stole books, works of art, writings any anything they could get their grubby hands on while happily telling everyone that their jobs are over by the exabyte are getting their precious little tokens stolen by big evil chinese LLMs :(
It's morally right to fuck over Anthropic (and OpenAI, or any other lab). Works generated by AI are not copyrightable anyways, and their terms of service have zero legal value.