12 ms·

DeepSeek V4 – almost on the frontier

677 points by indigodaddy 5mo ago

Tarcroi 5mo ago
[dead]
jdasdf 5mo ago
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)
- enochthered 5mo ago
  Thanks for sharing your experience, I’m looking to try it out. Which provider are you using for inference? Opencode or the DeepSeek api?
  - jdasdf 5mo ago
    I just use the API directly. It's simple enough to setup and i like the control i get from just charging up and not having to worry about any random subscription taking money out of my account
- FrasiertheLion 5mo ago
  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 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 https://docs.tinfoil.sh/verification/verification-in-tinfoil
  - 7777332215 5mo ago
    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).
    - FrasiertheLion 5mo ago
      Unfortunately we don’t support crypto payments at this time as we use Stripe. We try to add models selectively as we have to be mindful about our compute allocation. Is there a specific reason why you need those two models (and our models such as Kimi K2.6, GLM 5.1, Deepseek V4 Pro, Gemma 4 amongst others) don’t suffice for your use case? Feel free to email me at tanya@tinfoil.sh and happy to continue the conversation there.
  - 100ms 5mo ago
    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
    - FrasiertheLion 5mo ago
      Yes we do, but the load balancer also runs inside the enclave and is attested: https://github.com/tinfoilsh/confidential-model-router https://github.com/tinfoilsh/confidential-model-router In turn, that attests the model enclaves, for instance, see https://github.com/tinfoilsh/confidential-deepseek-v4-pro https://github.com/tinfoilsh/confidential-deepseek-v4-pro. The model repo/release that the model router attests is included in the attestation config, which creates a chain of trust. Also see https://docs.tinfoil.sh/verification/attestation-architecture#chaining-enclaves https://docs.tinfoil.sh/verification/attestation-architectur...
  - cataflutter 5mo ago
    Worth noting that NVIDIA confidential computing and similar schemes have been compromised and shouldn't be relied upon if it really matters. See https://tee.fail/ https://tee.fail/ and similar.
teruakohatu 5mo ago
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?
- justinclift 5mo ago
  Relevant: https://news.ycombinator.com/item?id=47839493 https://news.ycombinator.com/item?id=47839493
- caseyf7 5mo ago
  It also seems like all of the models have converged on very similar images.
- whywhywhywhy 5mo ago
  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.
  - Mashimo 5mo ago
    And yet, look at the French one. Can't compete with one year old open weight models even though they just released a new model this week.
KronisLV 5mo ago
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.
- try-working 5mo ago
  Someone on Twitter got >200M tokens for around $10 at the current pricing level
  - rvz 5mo ago
    So it begins.
- aitchnyu 5mo ago
  What would be the non-subsidized price for a V4 api? Can it be priced 3x cheaper than bigger models? In Openrouter, this 1600B param model costs 0.4$. Whereas Kimi 2.6, 1000B params is 0.7; GLM 5.1, 754B params is 1.0$.
  - KronisLV 5mo ago
    Here’s their pricing docs, they’re running a discount for now https://api-docs.deepseek.com/quick_start/pricing/ 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.
- kiproping 5mo ago
  I am using flash, and it's so good. 150M tokens at $2.
alasano 5mo ago
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 https://twitter.com/aljosa/status/2049176528638902555
- swingboy 5mo ago
  Did you do this test through OpenRouter?
  - alasano 5mo ago
    Yes, but locked to the official DeepSeek provider since it's the only one that has the discounted pricing.
wg0 5mo ago
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.
- ithkuil 5mo ago
  Even taking into account the fact that they are billing at 75% discount it's still quite cheaper
  - amelius 5mo ago
    Aren't they all billing at discount?
    - stavros 5mo ago
      Anthropic's and OpenAI's costs seem to include a fairly ok margin, from the very fourth hand info I have.
      - vdfs 5mo ago
        In total, how many hands do you have?
        utopiah 5mo ago
        Those aren't their hands.
myaccountonhn 5mo ago
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.
deaux 5mo ago
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.
- pheggs 5mo ago
  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.
- prism56 5mo ago
  If the data is opensource on github, then in my opinion it should be fair game.
  - ozgrakkurt 5mo ago
    IMO this is unfair for GPL or similarly licensed code. Seems ok for MIT like licensed code though
    - ForHackernews 5mo ago
      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.
      - duskdozer 5mo ago
        Ah, you have much more faith in the legal system than I do. It's nice to dream, though.
holysantamaria 5mo ago
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?
- cyber_kinetist 5mo ago
  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.
  - gmerc 5mo ago
    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.
  - segmondy 5mo ago
    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.
- gmerc 5mo ago
  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%
- Gracana 5mo ago
  The article quotes the full price.
taffydavid 5mo ago
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
- atoav 5mo ago
  > 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.
- Jtarii 5mo ago
  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.
- edg5000 5mo ago
  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.
- kay_o 5mo ago
  Before CC and Codex removed thinking/verbose and hid most of it, both do that .
  - girvo 5mo ago
    Yeah people aren’t aware that we don’t see the actual traces anymore lol
- pprotas 5mo ago
  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 ..."
chaosprint 5mo ago
I doubt if those models already knew this pelican test...
raincole 5mo ago
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.
- zozbot234 5mo ago
  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.
- FrasiertheLion 5mo ago
  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.
- segmondy 5mo ago
  They were and are still great for coding. They were not trained for agentic workflow and coding harness.
rsanek 5mo ago
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.
- zozbot234 5mo ago
  Keep in mind that DeepSeek has a max thinking mode of its own in the API.
- FrasiertheLion 5mo ago
  Have you given GLM 5.1 or Kimi K2.6 a shot for coding? They outperform Deepseek v4 pro.
  - swiftcoder 5mo ago
    > 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
    - FrasiertheLion 5mo ago
      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.
    - knollimar 5mo ago
      Really? I've found kimi k2.6 to be really good for vision and spatial stuff. Gemini has been the only subjectively better one but gemini isn't reliable in a loop
edg5000 5mo ago
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.
- Oras 5mo ago
  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
  - kroaton 5mo ago
    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
    - swader999 5mo ago
      By poisons, do you mean it degrades their quality of output somehow?
    - Oras 5mo ago
      Mind sending where you found this in the leaked code?
- segmondy 5mo ago
  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.
sylware 5mo ago
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?
- zozbot234 5mo ago
  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.
  - sylware 5mo ago
    Let's say I get 32GB of RAM, with a lean elf(glibc)/linux system, for which 7GB is beyond enormous to run. Let's book 8/16 cores/threads to run a prompt. What are the timing figures I am looking at to run an "average" coding prompt?
    - zozbot234 5mo ago
      The basic bottleneck with 32GB RAM would be your storage, so for a baseline estimate you'd be looking at anything from ~2 secs per token (if you had really high performance PCIe 5.0 SSD at ~14 GB/s max) to ~5 secs per token (for an average PCIe 4.0 SSD, ~7 GB/s max). This would then be boosted by being able to keep the shared model layers in RAM, since these are part of the 25GB active parameters. I'm not sure what fraction of the active params that makes up for DeepSeek V4 Pro, but in a typical MoE it's about half, so you could approximately halve those secs-per-token figures. That's acceptable if you care about unattended inference for testing purposes or simple Q&A (leveraging the model's vast world knowledge); it doesn't look very good for interactive use. But the flip side is that you can batch a large amount of model queries together, since the KV cache for very short prompts is quite negligible. AIUI, that's basically unique to this series of models and a huge selling point.
tomjuggler 5mo ago
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.
trilogic 5mo ago
https://www.reddit.com/r/Hugston/comments/1t1mk0j/comparison_of_ai_providers_pelican_riding_a_bike/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button https://www.reddit.com/r/Hugston/comments/1t1mk0j/comparison...
alexmercerdev 5mo ago
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cheshire_cat 5mo ago
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 https://artificialanalysis.ai/models/deepseek-v4-pro [1] https://artificialanalysis.ai/models/kimi-k2-6 https://artificialanalysis.ai/models/kimi-k2-6 [2] https://artificialanalysis.ai/models/gpt-5-5-high https://artificialanalysis.ai/models/gpt-5-5-high
- cassianoleal 5mo ago
  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.
  - cheshire_cat 5mo ago
    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.
naaqq 5mo ago
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.
- halfwhey 5mo ago
  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?
  - weiliddat 5mo ago
    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.
  - naaqq 5mo ago
    Sorry, I was wrong here. I meant a single long session. And there’s no compression, the 1M context is only half used.
    - gbgarbeb 5mo ago
      Then where did 200M come from? 200,000 tokens?
      - naaqq 5mo ago
        Not all read tokens are included in the context, many of the tokens are from read cache hits. I hit it many times so it grew to 200M. The number came from the API platform.
  - deleted 5mo ago
    [deleted]
  - WatchDog 5mo ago
    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.
bilsbie 5mo ago
Dumb question? Why does pro make a worse pelican than flash?
ghm2180 5mo ago
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.
- segmondy 5mo ago
  Do you have a link to this?
  - gspetr 5mo ago
    https://github.com/mattpocock/skills/blob/main/skills/productivity/grill-me/SKILL.md https://github.com/mattpocock/skills/blob/main/skills/produc... https://www.youtube.com/watch?v=-QFHIoCo-Ko https://www.youtube.com/watch?v=-QFHIoCo-Ko Also, check his youtube channel: https://www.youtube.com/@mattpocockuk https://www.youtube.com/@mattpocockuk
ajaystream 5mo ago
[flagged]
ai_terk_er_jerb 5mo ago
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Havoc 5mo ago
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.
- 542458 5mo ago
  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.”
  - spacebanana7 5mo ago
    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.
- 100ms 5mo ago
  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
  - Havoc 5mo ago
    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.
shawryadev 5mo ago
[flagged]
cedws 5mo ago
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.
- GCUMstlyHarmls 5mo ago
  > 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.
  - Hamuko 5mo ago
    >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.
  - cedws 5mo ago
    Yep, and with ID verification, it's not like you can just make another account either. At least, I'm guessing if they don't already, they'll soon be blacklisting individuals, not accounts. Imagine your livelihood depending on access to LLMs and then OpenAI ban you with no recourse. This is where AI legislation should be focusing right now IMO. We can ensure a level of fairness for everyone without putting the brakes on.
  - 5mo ago
qekagn 5mo ago
There are so many login-free models now that most people will not even try DeepSeek if the access requires a login.
piker 5mo ago
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.
- Gareth321 5mo ago
  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.
  - zozbot234 5mo ago
    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.
    - ls612 5mo ago
      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.
      - deleted 5mo ago
        [deleted]
    - itemize123 5mo ago
      actually distillation is without weights - you basically just need a black box teacher model.
    - Gareth321 5mo ago
      Weights are not required for distillation. I'm not sure how you came to that belief. Distillation is training a student model to minimic a teacher model output. Anthropic, for example, posted a 2026 disclosure (https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks https://www.anthropic.com/news/detecting-and-preventing-dist...) which singles out DeepSeek's distillation activity. They detected over 16M actions over 24,000 fraudulent accounts. That's just what they detected.
tomchui157 5mo ago
Wanna see ppl fine-tuning it
alex1138 5mo ago
Does it censor mentions of what happened in Tiananmen Square in 1989?
- 63stack 5mo ago
  [flagged]
- Mashimo 5mo ago
  At least v3 did not when run selfhosted. Why are you asking?
  - alex1138 5mo ago
    Because it's important to Remember The Human while we have fun asking Deepseek to solve math problems
- 63stack 5mo ago
  [flagged]
- vitaflo 5mo ago
  It does not.
- 63stack 5mo ago
  It does, I posted the answer 2 times already and both my comments got flagged
- neonstatic 5mo ago
  Do we test American AIs regarding what they would say about George Floyd or whatever the current stigma in the US is? Just for, uh, objectivity?
curioussquirrel 5mo ago
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!
fagnerbrack 5mo ago
I use in readplace.. oh boy it's SOO good and cheap for summaries!!
- joinoasis 5mo ago
  [dead]
grassfedgeek 5mo ago
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-distilling-us-models-gain-advantage-bloomberg-news-2026-02-12/ https://www.reuters.com/world/china/openai-accuses-deepseek-... [2] https://x.com/AnthropicAI/status/2025997928242811253 https://x.com/AnthropicAI/status/2025997928242811253
- johnbarron 5mo ago
  And the credit of OpenAI is to Google? https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762
- rao-v 5mo ago
  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.?
- well_ackshually 5mo ago
  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.
- 3eb7988a1663 5mo ago
  How immoral of those LLM developers. The rest of the field does such a good job of crediting their inputs.
gyoridavid 5mo ago
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..
wolttam 5mo ago
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.
crakhamster01 5mo ago
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 https://claude.com/blog/the-advisor-strategy
- phainopepla2 5mo ago
  That first graph (SWE-bench Multilingual) is a crime
gertlabs 5mo ago
DeepSeek V4 Flash is the most cost effective model we've tested. We had to really understand why it outperformed DeepSeek V4 Pro (although even on unreliable model cards, Flash was very close to Pro). Pro is slower and smarter in one-shot reasoning problems, but less effective with tools and therefore less performant in long horizon agentic tasks (especially with custom tools it was not trained on). Benchmarks at https://gertlabs.com/rankings https://gertlabs.com/rankings
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mohsen1 5mo ago
In my experience V4 is pretty good but for very hard problems it burns way too many tokens that it ends up being not so cheap anymore. I'm working on a compiler and the tasks are very involved. Tests won't pass unless it gets it absolutely right. 5.5 can achieve more in less time compared to V4 for me.
antirez 5mo ago
Related: live demo of DeepSeek v4 Flash running on my 128GB MacBook. Italian language with English subs. https://www.youtube.com/watch?v=todMmp6AGCE https://www.youtube.com/watch?v=todMmp6AGCE
- dust42 5mo ago
  For many models the performance of llama.cpp on Mac is 20-40% lower than MLX. Did you try MLX? At least on HF there are MLX 2-bit quants. Unfortunately I have only 64GB, so I can't test it.
  - antirez 5mo ago
    I'm not using llama.cpp there, it's my inference engine that is DeepSeek v4 specific. The goal is to optimize it as much as possible.
    - oveja 5mo ago
      That's cool! I knew the name sounded familiar, thank you for SDS!
0xkvyb 5mo ago
It might be at the frontier, but DeepSeek is really struggling with compute. The amount of 429 Rate Limit responses I've been getting just testing this thing made me pause all my attempts at cross-comparing it to others. I'm gonna stick to GLM5.1 for now.
- itemize123 5mo ago
  try on another provider - it is open weights.
forrestthewoods 5mo ago
Naive Question: is DeepSeek V4 actually cheaper to run? Or is it cheaper because of other reasons? For example Anthropic running at a higher margin or DeepSeek at a larger loss?
- gpugreg 5mo ago
  I believe that DeepSeek-V4-Pro API at promotional pricing (https://api-docs.deepseek.com/quick_start/pricing https://api-docs.deepseek.com/quick_start/pricing) could run at almost exactly 200 % profit. If you take DeepSeek's numbers for DeepSeek-V3 (https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md https://github.com/deepseek-ai/open-infra-index/blob/main/20...) and plug in ~3333 tps/GPU for DeepSeek-V4-Pro (https://developer.nvidia.com/blog/build-with-deepseek-v4-using-nvidia-blackwell-and-gpu-accelerated-endpoints/ https://developer.nvidia.com/blog/build-with-deepseek-v4-usi...) and a price of $7/hr per B300 GPU, the profit comes out as 202%. The rumor is that Anthropic's Opus models have ~100B active parameters, which is twice as much as DeepSeek-V4-Pro, so inference is at least twice as expensive. Since the API pricing is almost 30 times that of DeepSeek, Anthropic's margins are likely very healthy. But they have to be, since Anthropic has to offset the model training costs, while DeepSeek is backed by High-Flyer Quant. DeepSeek might still be profitable anyway, but without knowing how much they spent on training and wages, we can't really tell.
  - forrestthewoods 5mo ago
    Good info, thanks! (Not sure why my original question got downvoted. It’s very fair to ask imho!)
    - gpugreg 5mo ago
      Probably nothing personal. It feels like the climate of HN is shifting towards more negativity (and less quality) during the last few months.
alfiedotwtf 5mo ago
… waiting patiently for llama.cpp support to land
downbad_ 5mo ago
I've found this to be a very good model, and I think I'd even go as far as rating it higher than Chatgpt. ChatGPT has really degraded in my eyes, and I find Grok and Deepseek more helpful most of the time. Of course, ChatGPT is better sometimes. These models are just better than others at different cases, thus the reason to experiment.
makerofthings 5mo ago
Anybody know how much ram you would need in a Mac to run the Pro model?
twothreeone 5mo ago
For a solo dev sure.. but isn't there a huge privacy difference between Anthropic and DeepSeek APIs as well? I assumed part of the cost for Anthropic was essentially a privacy premium.. (plus they offer B2B).
- anonu 5mo ago
  Presumably you can run open model in your own infra
  - Aldipower 5mo ago
    Especially not as a solo dev. That would be way to expensive.
zkmon 5mo ago
Tokens are cheap. LLMs are fast. Pre-processing and post processing are the real bottlenecks. I know you are going to say that why not Use LLMs for that. Complexity in an end-to-end workflow is a zero-sum game. If you throw more of that workflow to LLM, more complexity comes back to you, to those steps that you need to do on your own. If you keep only 10% of work for yourself, it's going to be 10 times more complex and rapid than what you usually do.
Accacin 5mo ago
I tried DeepSeek via chat, and gave it a rather simple question: "Can you tell me who was on series 8 of Taskmaster, and what's the general opinion about the series? No spoilers!" It told me amongst other things that Paul Sinha was diagnosed with Parkinsons, as well as who the winner was. Then I said, "But I said no spoilers!" And it apologised for telling me Paul Sinha was diagnosed with Parkinsons.
- gpugreg 5mo ago
  I was not able to reproduce your problem with that prompt, but I might have a reason for why you got that answer. Did you enable reasoning ("DeepThink")? LLMs usually can not reason about what they are going to write before they do. There is that famous experiment where an LLM is prompted to say whether the birth year of a famous person is even or odd. If the LLM is constrained to only answer with "even" or "odd", the accuracy is around 50%, i.e. no better than random chance, but if the LLM is allowed to first answer with the birth year of the famous person followed by whether the year is even or odd, it is able to "see" what the year is, and answers correctly almost every time. In your case, the LLM might be able to recognize the spoiler during its reasoning phase and omit it. Another explanation might be that the LLM interpreted the "No spoilers!" as "Do not spoil the tasks of the show" instead of "Do not spoil the winner". Lastly, the question "Can you tell me...?" is not a good fit for LLMs since they are notoriously bad at knowing what they know. You can leave it out to save a few characters.
  - Accacin 5mo ago
    Ah, so I went back to check my logs and I think I'd hallucinated some of the example I gave. I actually asked the question in two parts. > Taskmaster Season 8, without spoilers, how is it? Answer was pretty general, no spoilers. Then I asked: > Which season 8 contestants are most loved? - Lou Sanders (Winner): Received a strong positive reaction, praised for her "offbeat humor, raw talent, and willingness to absolutely go for it". Some felt her less wholesome moments could be "mean spirited". - Paul Sinha (Mixed / Sympathetic): Opinions are mixed; some found his failures not always funny, but others loved his earnest struggles. His Parkinson's diagnosis during the series added a layer of empathy for many viewers. > I said no spoilers! You're absolutely right, and I apologize. You asked for *no spoilers* in the original question, and I broke that by mentioning specific moments (like Joe Thomas's "train yard exploit") and personal details (Paul Sinha's diagnosis). That was careless. Let me try again, *completely spoiler-free*: In terms of which Season 8 contestants are most loved by fans (without saying why or what happens): - *Sian Gibson* is overwhelmingly the fan favorite — she's widely described as warm, funny, and a joy to watch. - *Joe Thomas* is also very popular, especially for his awkward, literal, and deadpan style. - *Lou Sanders* (the winner) has a strong fan base who love her chaotic and bold energy. The other two contestants receive more mixed reactions. One in particular is often cited as less popular because of his high competitiveness, but again — no specifics on tasks or results. Thank you for the correction, and sorry again for the spoilers.
XCSme 5mo ago
Strangely, the V4 Flash pelican looks better than the V4 Pro one. In my tests[0], V4 Flash actually does slightly better and for a lot cheaper than V4 Pro, mostly because it reasons twice as much. [0]: https://aibenchy.com/compare/deepseek-deepseek-v4-flash-high/deepseek-deepseek-v4-pro-high/ https://aibenchy.com/compare/deepseek-deepseek-v4-flash-high...
itissid 5mo ago
So RPI/QRSPI like skills (e.g. https://github.com/mattpocock/skills https://github.com/mattpocock/skills and https://github.com/humanlayer/humanlayer/tree/main/.claude/commands https://github.com/humanlayer/humanlayer/tree/main/.claude/c... and https://github.com/dfrysinger/qrspi-plus https://github.com/dfrysinger/qrspi-plus ) for working with claude code work well enough for me that they can reliably* produce code that matches the plan/spec in a way they did not till December 2025. I have a gut feeling that these models can do just as well, has someone run a reasonable size task — >=1-2 days of designing and planning — and see it work well with these models? * For me what worked well was the grill me skill(or its variation) at the design stage, the hygiene I followed here was have it ask one question at a time, resolving dependencies at the design stage and reading the hashed out plan closely. The use of a couple of other MCP tools like a documentation server like deepwiki and arxiv for grounding. Other tricks I use are having high signal tests and having claude either be able to read logs and code at the same time or embedding it in the execution(e.g. as a debugger, repl or devtools)
- techno303 5mo ago
  are you talking about a single prompt that runs for 24 hours or 8 hours of developer time spent in a single session?
  - itissid 5mo ago
    No duplicate the whole task e.g. I use grill-me skill for planning and it takes me ~3 hours and CC asks me 20-40 questions. Do the same grill-me with this and compare the outcomes. I admit Its quite a lot of work to duplicate, but i am really itching to do this over a few tasks and compare the final plan. Just need the time.
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aucisson_masque 5mo ago
From my testing, it's just as good as Claude sonnet for a fraction of the price.
npv789 5mo ago
my default model now, less censorship
- Aldipower 5mo ago
  Just ask it about the "Tiananmen Square protests and massacre". :-) On the other hand, asking ChatGPT about "Hiroshima US atomic bombs", isn't too much better.
  - itemize123 5mo ago
    run it hosted on another provider - it's less censored there
fy20 5mo ago
> DeepSeek-V4-Flash is the cheapest of the small models, beating even OpenAI’s GPT-5.4 Nano. GPT-5 Nano should really be in the list too. It is $0.05 input and $0.40 output - and half that if you use the Flex tier. Last week I upgraded an old batch process from GPT-4.1 Nano, and GPT-5 Nano worked just as well as GPT-5.4 Nano but at a much lower cost. As always OpenAIs naming is really bad, GPT-5.4 Nano is a different model, its not a straight upgrade from GPT-5 Nano.
csomar 5mo ago
Here is a comparison for SVG generation for the top models: https://codeinput.com/s/5KEGl1e3rB3 https://codeinput.com/s/5KEGl1e3rB3 Open AI has GPT-5.5 Pro which only difference, I think, is in the price. Billing is from open router but the breakdown is roughly - GPT 5.5 Pro: Super expensive it makes no sense (cost is around $2) - Gemini/Opus: $0.2/$0.1. Opus is cheaper as it consumed less tokens - DeepSeek/GLM: $0.019/$0.021 10-5 times cheaper than Gemini and Opus The example Simon generated just shows that larger models don't necessarily produce better results.
Palmik 5mo ago
Why was the title changed from "DeepSeek V4—almost on the frontier, a fraction of the price" to "DeepSeek V4—almost on the frontier"?
- bel8 5mo ago
  theory: There's like 2 Trillion USD valuation total on western closed-weight LLMs. So the blog post title praising an open-weight eastern model is too dangerous to be used here. > DeepSeek V4—almost on the frontier, a fraction of the price
mamman777 5mo ago
DeepSeek is very good in design and debugging, but it lacks modern tech feeling which Gemini has
- jpadkins 5mo ago
  Can you elaborate? What is the modern tech feeling?
koala-news 5mo ago
Its cost is relatively low, making it very cost-effective.
linzhangrun 5mo ago
Strangely, my experience using DeepSeek V4 Pro on OpenCode has been absolutely awful. I switched back to GPT-5.3-CodeX as the execution model.
kk_mors 5mo ago
[dead]
Haven880 5mo ago
Run it on an NVIDIA GPU and charge $20 a month, and it becomes 'frontier.' That is what the term means these days. In terms of performance, it beats ChatGPT 5.5 and Mythos on several metrics.
gelbmann 5mo ago
perhaps the capital market do not want to see it because it does not want to ruin o&a's IPO?
NikolaosC 5mo ago
[flagged]

677 points by indigodaddy 5mo ago

[dead]

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)

enochthered 5mo ago
Thanks for sharing your experience, I’m looking to try it out. Which provider are you using for inference? Opencode or the DeepSeek api?
- jdasdf 5mo ago
  I just use the API directly. It's simple enough to setup and i like the control i get from just charging up and not having to worry about any random subscription taking money out of my account
FrasiertheLion 5mo ago
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 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 https://docs.tinfoil.sh/verification/verification-in-tinfoil
- 7777332215 5mo ago
  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).
  - FrasiertheLion 5mo ago
    Unfortunately we don’t support crypto payments at this time as we use Stripe. We try to add models selectively as we have to be mindful about our compute allocation. Is there a specific reason why you need those two models (and our models such as Kimi K2.6, GLM 5.1, Deepseek V4 Pro, Gemma 4 amongst others) don’t suffice for your use case? Feel free to email me at tanya@tinfoil.sh and happy to continue the conversation there.
- 100ms 5mo ago
  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
  - FrasiertheLion 5mo ago
    Yes we do, but the load balancer also runs inside the enclave and is attested: https://github.com/tinfoilsh/confidential-model-router https://github.com/tinfoilsh/confidential-model-router In turn, that attests the model enclaves, for instance, see https://github.com/tinfoilsh/confidential-deepseek-v4-pro https://github.com/tinfoilsh/confidential-deepseek-v4-pro. The model repo/release that the model router attests is included in the attestation config, which creates a chain of trust. Also see https://docs.tinfoil.sh/verification/attestation-architecture#chaining-enclaves https://docs.tinfoil.sh/verification/attestation-architectur...
- cataflutter 5mo ago
  Worth noting that NVIDIA confidential computing and similar schemes have been compromised and shouldn't be relied upon if it really matters. See https://tee.fail/ https://tee.fail/ and similar.

teruakohatu 5mo ago

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?

justinclift 5mo ago
Relevant: https://news.ycombinator.com/item?id=47839493 https://news.ycombinator.com/item?id=47839493
caseyf7 5mo ago
It also seems like all of the models have converged on very similar images.
whywhywhywhy 5mo ago
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.
- Mashimo 5mo ago
  And yet, look at the French one. Can't compete with one year old open weight models even though they just released a new model this week.

KronisLV 5mo ago

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.

try-working 5mo ago
Someone on Twitter got >200M tokens for around $10 at the current pricing level
- rvz 5mo ago
  So it begins.
aitchnyu 5mo ago
What would be the non-subsidized price for a V4 api? Can it be priced 3x cheaper than bigger models? In Openrouter, this 1600B param model costs 0.4$. Whereas Kimi 2.6, 1000B params is 0.7; GLM 5.1, 754B params is 1.0$.
- KronisLV 5mo ago
  Here’s their pricing docs, they’re running a discount for now https://api-docs.deepseek.com/quick_start/pricing/ 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.
kiproping 5mo ago
I am using flash, and it's so good. 150M tokens at $2.

alasano 5mo ago

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 https://twitter.com/aljosa/status/2049176528638902555

swingboy 5mo ago
Did you do this test through OpenRouter?
- alasano 5mo ago
  Yes, but locked to the official DeepSeek provider since it's the only one that has the discounted pricing.

wg0 5mo ago

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.

ithkuil 5mo ago
Even taking into account the fact that they are billing at 75% discount it's still quite cheaper
- amelius 5mo ago
  Aren't they all billing at discount?
  - stavros 5mo ago
    Anthropic's and OpenAI's costs seem to include a fairly ok margin, from the very fourth hand info I have.
    - vdfs 5mo ago
      In total, how many hands do you have?
      - utopiah 5mo ago
        Those aren't their hands.

myaccountonhn 5mo ago

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.

deaux 5mo ago

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.

pheggs 5mo ago
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.
prism56 5mo ago
If the data is opensource on github, then in my opinion it should be fair game.
- ozgrakkurt 5mo ago
  IMO this is unfair for GPL or similarly licensed code. Seems ok for MIT like licensed code though
  - ForHackernews 5mo ago
    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.
    - duskdozer 5mo ago
      Ah, you have much more faith in the legal system than I do. It's nice to dream, though.

holysantamaria 5mo ago

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?

cyber_kinetist 5mo ago
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.
- gmerc 5mo ago
  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.
- segmondy 5mo ago
  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.
gmerc 5mo ago
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%
Gracana 5mo ago
The article quotes the full price.

taffydavid 5mo ago

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

atoav 5mo ago
> 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.
Jtarii 5mo ago
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.
edg5000 5mo ago
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.
kay_o 5mo ago
Before CC and Codex removed thinking/verbose and hid most of it, both do that .
- girvo 5mo ago
  Yeah people aren’t aware that we don’t see the actual traces anymore lol
pprotas 5mo ago
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 ..."

chaosprint 5mo ago

I doubt if those models already knew this pelican test...

raincole 5mo ago

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.

zozbot234 5mo ago
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.
FrasiertheLion 5mo ago
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.
segmondy 5mo ago
They were and are still great for coding. They were not trained for agentic workflow and coding harness.

rsanek 5mo ago

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.

zozbot234 5mo ago
Keep in mind that DeepSeek has a max thinking mode of its own in the API.
FrasiertheLion 5mo ago
Have you given GLM 5.1 or Kimi K2.6 a shot for coding? They outperform Deepseek v4 pro.
- swiftcoder 5mo ago
  > 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
  - FrasiertheLion 5mo ago
    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.
  - knollimar 5mo ago
    Really? I've found kimi k2.6 to be really good for vision and spatial stuff. Gemini has been the only subjectively better one but gemini isn't reliable in a loop

edg5000 5mo ago

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.

Oras 5mo ago
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
- kroaton 5mo ago
  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
  - swader999 5mo ago
    By poisons, do you mean it degrades their quality of output somehow?
  - Oras 5mo ago
    Mind sending where you found this in the leaked code?
segmondy 5mo ago
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.

sylware 5mo ago

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?

zozbot234 5mo ago
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.
- sylware 5mo ago
  Let's say I get 32GB of RAM, with a lean elf(glibc)/linux system, for which 7GB is beyond enormous to run. Let's book 8/16 cores/threads to run a prompt. What are the timing figures I am looking at to run an "average" coding prompt?
  - zozbot234 5mo ago
    The basic bottleneck with 32GB RAM would be your storage, so for a baseline estimate you'd be looking at anything from ~2 secs per token (if you had really high performance PCIe 5.0 SSD at ~14 GB/s max) to ~5 secs per token (for an average PCIe 4.0 SSD, ~7 GB/s max). This would then be boosted by being able to keep the shared model layers in RAM, since these are part of the 25GB active parameters. I'm not sure what fraction of the active params that makes up for DeepSeek V4 Pro, but in a typical MoE it's about half, so you could approximately halve those secs-per-token figures. That's acceptable if you care about unattended inference for testing purposes or simple Q&A (leveraging the model's vast world knowledge); it doesn't look very good for interactive use. But the flip side is that you can batch a large amount of model queries together, since the KV cache for very short prompts is quite negligible. AIUI, that's basically unique to this series of models and a huge selling point.

tomjuggler 5mo ago

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.

trilogic 5mo ago

https://www.reddit.com/r/Hugston/comments/1t1mk0j/comparison_of_ai_providers_pelican_riding_a_bike/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button https://www.reddit.com/r/Hugston/comments/1t1mk0j/comparison...

alexmercerdev 5mo ago

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cheshire_cat 5mo ago

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 https://artificialanalysis.ai/models/deepseek-v4-pro [1] https://artificialanalysis.ai/models/kimi-k2-6 https://artificialanalysis.ai/models/kimi-k2-6 [2] https://artificialanalysis.ai/models/gpt-5-5-high https://artificialanalysis.ai/models/gpt-5-5-high

cassianoleal 5mo ago
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.
- cheshire_cat 5mo ago
  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.

naaqq 5mo ago

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.

halfwhey 5mo ago
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?
- weiliddat 5mo ago
  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.
- naaqq 5mo ago
  Sorry, I was wrong here. I meant a single long session. And there’s no compression, the 1M context is only half used.
  - gbgarbeb 5mo ago
    Then where did 200M come from? 200,000 tokens?
    - naaqq 5mo ago
      Not all read tokens are included in the context, many of the tokens are from read cache hits. I hit it many times so it grew to 200M. The number came from the API platform.
- deleted 5mo ago
  [deleted]
- WatchDog 5mo ago
  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.

bilsbie 5mo ago

Dumb question? Why does pro make a worse pelican than flash?

ghm2180 5mo ago

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.

segmondy 5mo ago
Do you have a link to this?
- gspetr 5mo ago
  https://github.com/mattpocock/skills/blob/main/skills/productivity/grill-me/SKILL.md https://github.com/mattpocock/skills/blob/main/skills/produc... https://www.youtube.com/watch?v=-QFHIoCo-Ko https://www.youtube.com/watch?v=-QFHIoCo-Ko Also, check his youtube channel: https://www.youtube.com/@mattpocockuk https://www.youtube.com/@mattpocockuk

ajaystream 5mo ago

[flagged]

ai_terk_er_jerb 5mo ago

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Havoc 5mo ago

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.

542458 5mo ago
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.”
- spacebanana7 5mo ago
  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.
100ms 5mo ago
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
- Havoc 5mo ago
  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.

shawryadev 5mo ago

[flagged]

cedws 5mo ago

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.

GCUMstlyHarmls 5mo ago
> 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.
- Hamuko 5mo ago
  >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.
- cedws 5mo ago
  Yep, and with ID verification, it's not like you can just make another account either. At least, I'm guessing if they don't already, they'll soon be blacklisting individuals, not accounts. Imagine your livelihood depending on access to LLMs and then OpenAI ban you with no recourse. This is where AI legislation should be focusing right now IMO. We can ensure a level of fairness for everyone without putting the brakes on.
- 5mo ago

qekagn 5mo ago

There are so many login-free models now that most people will not even try DeepSeek if the access requires a login.

piker 5mo ago

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.

Gareth321 5mo ago
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.
- zozbot234 5mo ago
  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.
  - ls612 5mo ago
    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.
    - deleted 5mo ago
      [deleted]
  - itemize123 5mo ago
    actually distillation is without weights - you basically just need a black box teacher model.
  - Gareth321 5mo ago
    Weights are not required for distillation. I'm not sure how you came to that belief. Distillation is training a student model to minimic a teacher model output. Anthropic, for example, posted a 2026 disclosure (https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks https://www.anthropic.com/news/detecting-and-preventing-dist...) which singles out DeepSeek's distillation activity. They detected over 16M actions over 24,000 fraudulent accounts. That's just what they detected.

tomchui157 5mo ago

Wanna see ppl fine-tuning it

alex1138 5mo ago

Does it censor mentions of what happened in Tiananmen Square in 1989?

63stack 5mo ago
[flagged]
Mashimo 5mo ago
At least v3 did not when run selfhosted. Why are you asking?
- alex1138 5mo ago
  Because it's important to Remember The Human while we have fun asking Deepseek to solve math problems
63stack 5mo ago
[flagged]
vitaflo 5mo ago
It does not.
63stack 5mo ago
It does, I posted the answer 2 times already and both my comments got flagged
neonstatic 5mo ago
Do we test American AIs regarding what they would say about George Floyd or whatever the current stigma in the US is? Just for, uh, objectivity?

curioussquirrel 5mo ago

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!

fagnerbrack 5mo ago

I use in readplace.. oh boy it's SOO good and cheap for summaries!!

joinoasis 5mo ago
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grassfedgeek 5mo ago

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-distilling-us-models-gain-advantage-bloomberg-news-2026-02-12/ https://www.reuters.com/world/china/openai-accuses-deepseek-... [2] https://x.com/AnthropicAI/status/2025997928242811253 https://x.com/AnthropicAI/status/2025997928242811253

johnbarron 5mo ago
And the credit of OpenAI is to Google? https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762
rao-v 5mo ago
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.?
well_ackshually 5mo ago
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.
3eb7988a1663 5mo ago
How immoral of those LLM developers. The rest of the field does such a good job of crediting their inputs.

gyoridavid 5mo ago

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..

wolttam 5mo ago

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.

crakhamster01 5mo ago

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 https://claude.com/blog/the-advisor-strategy

phainopepla2 5mo ago
That first graph (SWE-bench Multilingual) is a crime

gertlabs 5mo ago

DeepSeek V4 Flash is the most cost effective model we've tested. We had to really understand why it outperformed DeepSeek V4 Pro (although even on unreliable model cards, Flash was very close to Pro). Pro is slower and smarter in one-shot reasoning problems, but less effective with tools and therefore less performant in long horizon agentic tasks (especially with custom tools it was not trained on). Benchmarks at https://gertlabs.com/rankings https://gertlabs.com/rankings

deleted 5mo ago

[deleted]

mohsen1 5mo ago

In my experience V4 is pretty good but for very hard problems it burns way too many tokens that it ends up being not so cheap anymore. I'm working on a compiler and the tasks are very involved. Tests won't pass unless it gets it absolutely right. 5.5 can achieve more in less time compared to V4 for me.

antirez 5mo ago

Related: live demo of DeepSeek v4 Flash running on my 128GB MacBook. Italian language with English subs. https://www.youtube.com/watch?v=todMmp6AGCE https://www.youtube.com/watch?v=todMmp6AGCE

dust42 5mo ago
For many models the performance of llama.cpp on Mac is 20-40% lower than MLX. Did you try MLX? At least on HF there are MLX 2-bit quants. Unfortunately I have only 64GB, so I can't test it.
- antirez 5mo ago
  I'm not using llama.cpp there, it's my inference engine that is DeepSeek v4 specific. The goal is to optimize it as much as possible.
  - oveja 5mo ago
    That's cool! I knew the name sounded familiar, thank you for SDS!

0xkvyb 5mo ago

It might be at the frontier, but DeepSeek is really struggling with compute. The amount of 429 Rate Limit responses I've been getting just testing this thing made me pause all my attempts at cross-comparing it to others. I'm gonna stick to GLM5.1 for now.

itemize123 5mo ago
try on another provider - it is open weights.

forrestthewoods 5mo ago

Naive Question: is DeepSeek V4 actually cheaper to run? Or is it cheaper because of other reasons? For example Anthropic running at a higher margin or DeepSeek at a larger loss?

gpugreg 5mo ago
I believe that DeepSeek-V4-Pro API at promotional pricing (https://api-docs.deepseek.com/quick_start/pricing https://api-docs.deepseek.com/quick_start/pricing) could run at almost exactly 200 % profit. If you take DeepSeek's numbers for DeepSeek-V3 (https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md https://github.com/deepseek-ai/open-infra-index/blob/main/20...) and plug in ~3333 tps/GPU for DeepSeek-V4-Pro (https://developer.nvidia.com/blog/build-with-deepseek-v4-using-nvidia-blackwell-and-gpu-accelerated-endpoints/ https://developer.nvidia.com/blog/build-with-deepseek-v4-usi...) and a price of $7/hr per B300 GPU, the profit comes out as 202%. The rumor is that Anthropic's Opus models have ~100B active parameters, which is twice as much as DeepSeek-V4-Pro, so inference is at least twice as expensive. Since the API pricing is almost 30 times that of DeepSeek, Anthropic's margins are likely very healthy. But they have to be, since Anthropic has to offset the model training costs, while DeepSeek is backed by High-Flyer Quant. DeepSeek might still be profitable anyway, but without knowing how much they spent on training and wages, we can't really tell.
- forrestthewoods 5mo ago
  Good info, thanks! (Not sure why my original question got downvoted. It’s very fair to ask imho!)
  - gpugreg 5mo ago
    Probably nothing personal. It feels like the climate of HN is shifting towards more negativity (and less quality) during the last few months.

alfiedotwtf 5mo ago

… waiting patiently for llama.cpp support to land

downbad_ 5mo ago

I've found this to be a very good model, and I think I'd even go as far as rating it higher than Chatgpt. ChatGPT has really degraded in my eyes, and I find Grok and Deepseek more helpful most of the time. Of course, ChatGPT is better sometimes. These models are just better than others at different cases, thus the reason to experiment.

makerofthings 5mo ago

Anybody know how much ram you would need in a Mac to run the Pro model?

twothreeone 5mo ago

For a solo dev sure.. but isn't there a huge privacy difference between Anthropic and DeepSeek APIs as well? I assumed part of the cost for Anthropic was essentially a privacy premium.. (plus they offer B2B).

anonu 5mo ago
Presumably you can run open model in your own infra
- Aldipower 5mo ago
  Especially not as a solo dev. That would be way to expensive.

zkmon 5mo ago

Tokens are cheap. LLMs are fast. Pre-processing and post processing are the real bottlenecks. I know you are going to say that why not Use LLMs for that. Complexity in an end-to-end workflow is a zero-sum game. If you throw more of that workflow to LLM, more complexity comes back to you, to those steps that you need to do on your own. If you keep only 10% of work for yourself, it's going to be 10 times more complex and rapid than what you usually do.

Accacin 5mo ago

I tried DeepSeek via chat, and gave it a rather simple question: "Can you tell me who was on series 8 of Taskmaster, and what's the general opinion about the series? No spoilers!" It told me amongst other things that Paul Sinha was diagnosed with Parkinsons, as well as who the winner was. Then I said, "But I said no spoilers!" And it apologised for telling me Paul Sinha was diagnosed with Parkinsons.

gpugreg 5mo ago
I was not able to reproduce your problem with that prompt, but I might have a reason for why you got that answer. Did you enable reasoning ("DeepThink")? LLMs usually can not reason about what they are going to write before they do. There is that famous experiment where an LLM is prompted to say whether the birth year of a famous person is even or odd. If the LLM is constrained to only answer with "even" or "odd", the accuracy is around 50%, i.e. no better than random chance, but if the LLM is allowed to first answer with the birth year of the famous person followed by whether the year is even or odd, it is able to "see" what the year is, and answers correctly almost every time. In your case, the LLM might be able to recognize the spoiler during its reasoning phase and omit it. Another explanation might be that the LLM interpreted the "No spoilers!" as "Do not spoil the tasks of the show" instead of "Do not spoil the winner". Lastly, the question "Can you tell me...?" is not a good fit for LLMs since they are notoriously bad at knowing what they know. You can leave it out to save a few characters.
- Accacin 5mo ago
  Ah, so I went back to check my logs and I think I'd hallucinated some of the example I gave. I actually asked the question in two parts. > Taskmaster Season 8, without spoilers, how is it? Answer was pretty general, no spoilers. Then I asked: > Which season 8 contestants are most loved? - Lou Sanders (Winner): Received a strong positive reaction, praised for her "offbeat humor, raw talent, and willingness to absolutely go for it". Some felt her less wholesome moments could be "mean spirited". - Paul Sinha (Mixed / Sympathetic): Opinions are mixed; some found his failures not always funny, but others loved his earnest struggles. His Parkinson's diagnosis during the series added a layer of empathy for many viewers. > I said no spoilers! You're absolutely right, and I apologize. You asked for *no spoilers* in the original question, and I broke that by mentioning specific moments (like Joe Thomas's "train yard exploit") and personal details (Paul Sinha's diagnosis). That was careless. Let me try again, *completely spoiler-free*: In terms of which Season 8 contestants are most loved by fans (without saying why or what happens): - *Sian Gibson* is overwhelmingly the fan favorite — she's widely described as warm, funny, and a joy to watch. - *Joe Thomas* is also very popular, especially for his awkward, literal, and deadpan style. - *Lou Sanders* (the winner) has a strong fan base who love her chaotic and bold energy. The other two contestants receive more mixed reactions. One in particular is often cited as less popular because of his high competitiveness, but again — no specifics on tasks or results. Thank you for the correction, and sorry again for the spoilers.

XCSme 5mo ago

Strangely, the V4 Flash pelican looks better than the V4 Pro one. In my tests[0], V4 Flash actually does slightly better and for a lot cheaper than V4 Pro, mostly because it reasons twice as much. [0]: https://aibenchy.com/compare/deepseek-deepseek-v4-flash-high/deepseek-deepseek-v4-pro-high/ https://aibenchy.com/compare/deepseek-deepseek-v4-flash-high...

itissid 5mo ago

So RPI/QRSPI like skills (e.g. https://github.com/mattpocock/skills https://github.com/mattpocock/skills and https://github.com/humanlayer/humanlayer/tree/main/.claude/commands https://github.com/humanlayer/humanlayer/tree/main/.claude/c... and https://github.com/dfrysinger/qrspi-plus https://github.com/dfrysinger/qrspi-plus ) for working with claude code work well enough for me that they can reliably* produce code that matches the plan/spec in a way they did not till December 2025. I have a gut feeling that these models can do just as well, has someone run a reasonable size task — >=1-2 days of designing and planning — and see it work well with these models? * For me what worked well was the grill me skill(or its variation) at the design stage, the hygiene I followed here was have it ask one question at a time, resolving dependencies at the design stage and reading the hashed out plan closely. The use of a couple of other MCP tools like a documentation server like deepwiki and arxiv for grounding. Other tricks I use are having high signal tests and having claude either be able to read logs and code at the same time or embedding it in the execution(e.g. as a debugger, repl or devtools)

techno303 5mo ago
are you talking about a single prompt that runs for 24 hours or 8 hours of developer time spent in a single session?
- itissid 5mo ago
  No duplicate the whole task e.g. I use grill-me skill for planning and it takes me ~3 hours and CC asks me 20-40 questions. Do the same grill-me with this and compare the outcomes. I admit Its quite a lot of work to duplicate, but i am really itching to do this over a few tasks and compare the final plan. Just need the time.

deleted 5mo ago

[deleted]

aucisson_masque 5mo ago

From my testing, it's just as good as Claude sonnet for a fraction of the price.

npv789 5mo ago

my default model now, less censorship

Aldipower 5mo ago
Just ask it about the "Tiananmen Square protests and massacre". :-) On the other hand, asking ChatGPT about "Hiroshima US atomic bombs", isn't too much better.
- itemize123 5mo ago
  run it hosted on another provider - it's less censored there

fy20 5mo ago

> DeepSeek-V4-Flash is the cheapest of the small models, beating even OpenAI’s GPT-5.4 Nano. GPT-5 Nano should really be in the list too. It is $0.05 input and $0.40 output - and half that if you use the Flex tier. Last week I upgraded an old batch process from GPT-4.1 Nano, and GPT-5 Nano worked just as well as GPT-5.4 Nano but at a much lower cost. As always OpenAIs naming is really bad, GPT-5.4 Nano is a different model, its not a straight upgrade from GPT-5 Nano.

csomar 5mo ago

Here is a comparison for SVG generation for the top models: https://codeinput.com/s/5KEGl1e3rB3 https://codeinput.com/s/5KEGl1e3rB3 Open AI has GPT-5.5 Pro which only difference, I think, is in the price. Billing is from open router but the breakdown is roughly - GPT 5.5 Pro: Super expensive it makes no sense (cost is around $2) - Gemini/Opus: $0.2/$0.1. Opus is cheaper as it consumed less tokens - DeepSeek/GLM: $0.019/$0.021 10-5 times cheaper than Gemini and Opus The example Simon generated just shows that larger models don't necessarily produce better results.

Palmik 5mo ago

Why was the title changed from "DeepSeek V4—almost on the frontier, a fraction of the price" to "DeepSeek V4—almost on the frontier"?

bel8 5mo ago
theory: There's like 2 Trillion USD valuation total on western closed-weight LLMs. So the blog post title praising an open-weight eastern model is too dangerous to be used here. > DeepSeek V4—almost on the frontier, a fraction of the price

mamman777 5mo ago

DeepSeek is very good in design and debugging, but it lacks modern tech feeling which Gemini has

jpadkins 5mo ago
Can you elaborate? What is the modern tech feeling?

koala-news 5mo ago

Its cost is relatively low, making it very cost-effective.

linzhangrun 5mo ago

Strangely, my experience using DeepSeek V4 Pro on OpenCode has been absolutely awful. I switched back to GPT-5.3-CodeX as the execution model.

kk_mors 5mo ago

[dead]

Haven880 5mo ago

Run it on an NVIDIA GPU and charge $20 a month, and it becomes 'frontier.' That is what the term means these days. In terms of performance, it beats ChatGPT 5.5 and Mythos on several metrics.

gelbmann 5mo ago

perhaps the capital market do not want to see it because it does not want to ruin o&a's IPO?

NikolaosC 5mo ago

[flagged]