▲jari_mustonen52 minutes ago
[-] Open Source as it gets in this space, top notch developer documentation, and prices insanely low, while delivering frontier model capabilities. So basically, this is from hackers to hackers. Loving it!
Also, note that there's zero CUDA dependency. It runs entirely on Huawei chips. In other words, Chinese ecosystem has delivered a complete AI stack. Like it or not, that's a big news. But what's there not to like when monopolies break down?
TrackerFF34 seconds ago
[-] Let's see how long it takes before the big US AI companies start lobbying to outright ban use of Chinese AI. For "national security" reasons, of course.
reply▲ifwinterco32 minutes ago
[-] As a Brit I'm here for it to be honest, I'm tired of America with everything that's going on.
China is not perfect but a bit of competition is healthy and needed
jurgenburgen2 minutes ago
[-] I don’t know if we’re ahead of the curve but that tired feeling has started turning into hate here in the EU. I guess being threatened with invasion does that to you.
The next decade is going to look very different with America Alone.
"not perfect" is a _very_ big simplification of what China is though
reply▲Isn't that the same to every major superpower?
reply▲You can say the same about the US
reply▲Fellow countryman here. I came here to say the same thing
reply▲The incredible arrogance and hybris of the American initiated tech war - it is just a beautiful thing to see it slowly fall apart.
The US-China contest aside - it is in the application layer llms will show their value. There the field, with llm commoditization and no clear monopolies, is wide open.
There was a point in time where it looked like llms would the domain of a single well guarded monopoly - that would have been a very dark world. Luckily we are not there now and there is plenty of grounds for optimism.
Still not sure how I feel about China of all places to control the only alternative AI stack, but I guess it's better than leaving everything to the US alone. If China ever feels emboldened enough to go for Taiwan and the US descends into complete chaos, the rest of the world running on AI will be at the mercy of authoritarian regimes. At the very least you can be sure noone is in this for the good of the people anymore. This is about who will dominate the world of tomorrow.
reply▲"Open Source" is the ultimate romance understood by software engineers.
reply▲sudo_cowsay24 minutes ago
[-] I sometimes wonder if there are any security risks with using Chinese LLMs. Is there?
reply▲dalemhurley5 minutes ago
[-] Theoretically yes. It is entirely possible to poison the training data for a supply chain attack against vibe coders. The trick would be to make it extremely specific for a high value target so it is not picked up by a wide range of people. You could also target a specific open source project that is used by another widely used product.
However there is so many factors involved beyond your control that it would not be a viable option compared to other possible security attacks.
There must be. The executives at my company wouldn't have banned them all for no reason after all.
reply▲But remember to not ask about Taiwan!
reply▲Quit a bit better then made to bomb little girl schools in Iran.
reply▲spiderfarmer25 minutes ago
[-] Just ask it for a summary of the USA’s role in Iran, Gaza, Lebanon and its recent threats against Panama, Cuba and Greenland! It might be able to keep track.
reply▲throwa3562621 hour ago
[-] reply▲It's because they're optimizing for a different problem.
Western Models are optimizing to be used as an interchangeable product. Chinese models are being optimizing to be built upon.
raincole48 minutes ago
[-] > Western Models are optimizing to be used as an interchangeable product
Why? It sounds like the stupidest idea ever. Interchangeability = no lock-in = no moot.
tick_tock_tick6 minutes ago
[-] They are all racing to AGI. They aren't designing them to be interchangeable they just happen to be.
reply▲simonjgreen11 minutes ago
[-] Yeah, it’s an interesting one. I think inertia and expectations at this point? I don’t think the big labs anticipated how low the model switching costs would be and how quickly their leads would be eroded (by each other and the upstarts)
They are developing their moats with the platform tooling around it right now though. Look at Anthropic with Routines and OpenAI with Agents. Drop that capability in to a business with loose controls and suddenly you have a very sticky product with high switching costs. Meanwhile if you stick with purely the ‘chat’ use cases, even Cowork and scheduled tasks, you maintain portability.
peepee198236 minutes ago
[-] If you want other people to know whether you're being genuine or sarcastic, you'll have to put a bit more effort into your comments. Your comment just adds noise.
reply▲vitorgrs51 minutes ago
[-] Meanwhile, they don't actually say which model you are running on Deepseek Chat website.
reply▲You might enjoy Z.ais api docs aswell
reply▲Western orgs have been captured by Silicon Valley style patrimonialism, and aren’t based on merit anymore.
reply▲orbital-decay51 minutes ago
[-] >we implement end-to-end, bitwise batch-invariant, and deterministic kernels with minimal performance overheadPretty cool, I think they're the first to guarantee determinism with the fixed seed or at the temperature 0. Google came close but never guaranteed it AFAIK. DeepSeek show their roots - it may not strictly be a SotA model, but there's a ton of low-level optimizations nobody else pays attention to.
reply▲hodgehog1115 minutes ago
[-] There are quite a few comments here about benchmark and coding performance. I would like to offer some opinions regarding its capacity for mathematics problems in an active research setting.
I have a collection of novel probability and statistics problems at the masters and PhD level with varying degrees of feasibility. My test suite involves running these problems through first (often with about 2-6 papers for context) and then requesting a rigorous proof as followup. Since the problems are pretty tough, there is no quantitative measure of performance here, I'm just judging based on how useful the output is toward outlining a solution that would hopefully become publishable.
Just prior to this model, Gemini led the pack, with GPT-5 as a close second. No other model came anywhere near these two (no, not even Claude). Gemini would sometimes have incredible insight for some of the harder problems (insightful guesses on relevant procedures are often most useful in research), but both of them tend to struggle with outlining a concrete proof in a single followup prompt. This DeepSeek V4 Pro with max thinking does remarkably well here. I'm not seeing the same level of insights in the first response as Gemini (closer to GPT-5), but it often gets much better in the followup, and the proofs can be _very_ impressive; nearly complete in several cases.
Given that both Gemini and DeepSeek also seem to lead on token performance, I'm guessing that might play a role in their capacity for these types of problems. It's probably more a matter of just how far they can get in a sensible computational budget.
Despite what the benchmarks seem to show, this feels like a huge step up for open-weight models. Bravo to the DeepSeek team!
nibbleyou14 minutes ago
[-] Curious to know what kind of problems you are talking about here
reply▲hodgehog116 minutes ago
[-] I don't want to give away too much due to anonymity reasons, but the problems are generally in the following areas (in order from hardest to easiest):
- One problem on using quantum mechanics and C*-algebra techniques for non-Markovian stochastic processes. The interchange between the physics and probability languages often trips the models up, so pretty much everything tends to fail here.
- Three problems in random matrix theory and free probability; these require strong combinatorial skills and a good understanding of novel definitions, requiring multiple papers for context.
- One problem in saddle-point approximation; I've just recently put together a manuscript for this one with a masters student, so it isn't trivial either, but does not require as much insight.
- One problem pertaining to bounds on integral probability metrics for time-series modelling.
> pricing "Pro" $3.48 / 1M output tokens vs $4.40
I’d like somebody to explain to me how the endless comments of "bleeding edge labs are subsidizing the inference at an insane rate" make sense in light of a humongous model like v4 pro being $4 per 1M. I’d bet even the subscriptions are profitable, much less the API prices.
edit: $1.74/M input
$3.48/M output on OpenRouter
API prices may be profitable. Subscriptions may still be subsidized for power users. Free tiers almost certainly are. And frontier labs may be subsidizing overall business growth, training, product features, and peak capacity, even if a normal metered API call is profitable on marginal inference.
reply▲This price is high even because of the current shortage of inference cards available to DeepSeek; they claimed in their press release that once the Ascend 950 computing cards are launched in the second half of the year, the price of the Pro version will drop significantly
reply▲Bombthecat24 minutes ago
[-] In six month deepseek won't be sota anymore und usage will be wayyyy down.
reply▲I was thinking the same. How can it be than other providers can offer third-party open source models with roughly the similar quality like this, Kimi K2.6 or GLM 5.1 for 10 times less the price? How can it be that GPT 5.5 is suddenly twice the price as GPT 5.4 while being faster? I don't believe that it's a bigger, more expensive model to run, it's just they're starting to raise up the prices because they can and their product is good (which is honest as long as they're transparent with it). Honestly the movement about subscription costing the company 20 times more than we're paying is just a PR movement to justify the price hike.
reply▲peepee198226 minutes ago
[-] I'm pretty sure OpenAI and Anthropic are overpricing their token billed API usage mainly as an incentive to commit to get their subscriptions instead.
reply▲simonjgreen8 minutes ago
[-] Anthropic recently dropped all inclusive use from new enterprise subscriptions, your seat sub gets you a seat with no usage. All usage is then charged at API rates. It’s like a worst of both worlds!
reply▲weird-eye-issue16 minutes ago
[-] The target audience for the APIs is third party apps which are not compatible with the subscriptions.
reply▲They are profitable to opex costs, but not capex costs with the current depreciation schedules, though those are now edging higher than expected.
reply▲vitorgrs45 minutes ago
[-] And they actually say the prices will be "significantly" lower in second semester when Huawei 650 chips comes in.
reply▲Insert always has been meme.
But seriously, it just stems from the fact some people want AI to go away. If you set your conclusion first, you can very easily derive any premise. AI must go away -> AI must be a bad business -> AI must be losing money.
Before the AI bubble that will burst any time now, there was the AI winter that would magically arrive before the models got good enough to rival humans.
reply▲jimmydoe42 minutes ago
[-] They’ve also announced Pro price will further drop 2H26 once they have more HUAWEI chips.
reply▲My thoughts exactly. I also believe that subscription services are profitable, and the talk about subsidies is just a way to extract higher profit margins from the API prices businesses pay.
reply▲Bombthecat22 minutes ago
[-] Google stated a while back, that with tpus they are able to sell at cost / with profit.
Aka: everyone who uses Nvidia isn't selling at cost, because Nvidia is so expensive.
I mean, not one "bleeding edge" lab has stated they are profitable. They don't publish financials aside from revenue. And in Anthropic's case, they fuck with pricing every week. Clearly something is wrong here.
reply▲Point taken but there isnt any western providers there yet. Power is cheaper in china.
reply▲NitpickLawyer1 hour ago
[-] As this is a new arch with tons of optimisations, it'll take some time for inference engines to support it properly, and we'll see more 3rd party providers offer it. Once that settles we'll have a median price for an optimised 1.6T model, and can "guesstimate" from there what the big labs can reasonably serve for the same price. But yeah, it's been said for a while that big labs are ok on API costs. The only unknown is if subscriptions were profitable or not. They've all been reducing the limits lately it seems.
reply▲These models are open and there are tons of western providers offering it at comparable rates.
reply▲> I’d like somebody to explain to me how the endless comments of "bleeding edge labs are subsidizing the inference at an insane rate" make sense in light of a humongous model like v4 pro being $4 per 1M. I’d bet even the subscriptions are profitable, much less the API prices.
One answer - Chinese Communist Party. They are being subsidized by the state.
primaprashant1 hour ago
[-] While SWE-bench Verified is not a perfect benchmark for coding, AFAIK, this is the first open-weights model that has crossed the threshold of 80% score on this by scoring 80.6%.
Back in Nov 2025, Opus 4.5 (80.9%) was the first proprietary model to do so.
There's something heartwarming about the developer docs being released before the flashy press release.
reply▲Their audience is people who build stuff, techs audience is enterprise CEOs and politicians, and anyone else happy to hype up all the questionably timed releases and warnings of danger, white collar irrelevence, or promises of utopian paradise right before a funding round.
reply▲Where's the training data and training scripts since you are calling this open
source?
Edit: it seems "open source" was edited out of the parent comment.
doesn't it get tiring after a while? using the same (perceived) gotcha, over and over again, for three years now?
no one is ever going to release their training data because it contains every copyrighted work in existence. everyone, even the hecking-wholesome safety-first Anthropic, is using copyrighted data without permission to train their models. there you go.
There is an easy fix already in widespread use: "open weights".
It is very much a valuable thing already, no need to taint it with wrong promise.
Though I disagree about being used if it was indeed open source: I might not do it inside my home lab today, but at least Qwen and DeepSeek would use and build on what eg. Facebook was doing with Llama, and they might be pushing the open weights model frontier forward faster.
it's not a gotcha but people using words in ways others don't like.
reply▲Aww yes, let me push a couple petabytes to my git repo for everyone to download...
reply▲An easier thing would be to say "open weights", yes.
reply▲Weights are the source, training data is the compiler.
reply▲You got it the wrong way round. It's more akin to.
1. Training data is the source.
2. Training is compilation/compression.
3. Weights are the compiled source akin to optimized assembly.
However it's an imperfect analogy on so many levels. Nitpick away.
So, this is the version that's able to serve inference from Huawei chips, although it was still trained on nVidia. So unless I'm very much mistaken this is the biggest and best model yet served on (sort of) readily-available chinese-native tech. Performance and stability will be interesting to see; openrouter currently saying about 1.12s and 30tps, which isn't wonderful but it's day one after all.
For reference, the huawei Ascend 950 that this thing runs on is supposed to be roughly comparable to nVidia's H100 from 2022. In other words, things are hotting up in the GPU war!
npodbielski3 minutes ago
[-] Great! Can't wait to buy decent GPU for interference for <1k$
reply▲Truly open source coming from China. This is heartwarming. I know if the potential ulterior motives.
reply▲American companies want a scan of your asshole for the privilege of paying to access their models, and unapologetically admit to storing, analyzing, training on, and freely giving your data to any authorities if requested. Chinese ulteriority is hypothetical, American is blatant.
reply▲It’s not remotely hypothetical you’d have to be living under a rock to believe that. And the fusion with a one-party state government that doesn’t tolerate huge swathes of thoughtspace being freely discussed is completely streamlined, not mediated by any guardrails or accountability.
This “no harm to me” meme about a foreign totalitarian government (with plenty of incentive to run influence ops on foreigners) hoovering your data is just so mind-bogglingly naive.
As a non-American, everything you wrote other than "one party" applies to the current US regime.
Relatively speaking, DeepSeek is less untrustworthy than Grok.
When I try ChatGPT on current events from the White House it interprets them as strange hypotheticals rather than news, which is probably more a problem with DC than with GPT, but whatever.
randomNumber725 minutes ago
[-] The USA has one of the highest percentages of their population in prison.
Even for minor stuff like beeing addicted to drugs.
Looks pretty totalitarian to me.
And in China the state can harvest your organs for political crimes or even just being the wrong religion.
Not quite the same.
thesmtsolver218 minutes ago
[-] Do you really trust China’s stats on prison population?
Note: you can have this conversation criticizing the US on a US website. Try criticizing Xi or the CCP or calling him Pooh on a Chinese website.
You think China doesn’t imprison drug users?
China recently executed a low level drug trafficker
https://www.lemonde.fr/en/international/article/2026/04/05/c...
China is one of the top executioners. China executes more than rest of the world combined
https://www.amnesty.org/en/latest/news/2017/04/china-must-co...
You think China is honest about political prisoners in Tibet and Xinjiang?
Criticize the US all you want but I can’t understand the whitewashing of a real totalitarian and genocidal state like mainland China.
oceanplexian51 minutes ago
[-] > And the fusion with a one-party state government that doesn’t tolerate huge swathes of thoughtspace being freely discussed
That would be a great argument if the American models weren’t so heavily censored.
The Chinese model might dodge a question if I ask it about 1-2 specific Chinese cultural issues but then it also doesn’t moralize me at every turn because I asked it to use a piece of security software.
It’s an open model? So you can run it yourself if you want to
reply▲b65e8bee43c2ed038 minutes ago
[-] >This “no harm to me” meme about a foreign totalitarian government (with plenty of incentive to run influence ops on foreigners) hoovering your data is just so mind-bogglingly naive.
yes, this is exactly what I'm saying.
> This “no harm to me” meme about a foreign totalitarian government (with plenty of incentive to run influence ops on foreigners) hoovering your data is just so mind-bogglingly naive.
This is why I’ve been urging everyone I know to move away from American based services and providers. It’s slow but honest work.
And you're saying Americans aren't banned from criticising their elites?
reply▲Pretty sure you guys have a strong laws about free-speech, and criticizing elites is part of that. Though there are some groups that do not really want the 1st amendment to be a thing.
reply▲> Though there are some groups that do not really want the 1st amendment to be a thing.
The executive branch?
That would be a naïve perspective.
reply▲mjamesaustin20 minutes ago
[-] Foreigners are literally being denied entry into the country due to opposing viewpoints expressed on social media. People have to disable FaceID on their phones prior to going through customs in case an agent decides to investigate whether their political views are in opposition to the current administration.
reply▲thesmtsolver217 minutes ago
[-] As someone with Tibetan friends and as someone from India, Chinese ulterior motives are way more clear.
reply▲Quothling36 minutes ago
[-] It's a little sad that tech now comes down to geopolitics, but if you're not in the USA then what is the difference? I'm Danish, would I rather give my data to China or to a country which recently threatened the kingdom I live in with military invasion? Ideally I'd give them to Mistral, but in reality we're probably going to continue building multi-model tools to make sure we share our data with everyone equally.
reply▲spaceman_202052 minutes ago
[-] I don’t care about whatever “ulterior motives” they might have
My country’s per capita income is $2500 a year. We can’t pay perpetual rent to OAI/Anthropic
Do they also open-source censoring filter rules? Like, you can't ask what happened at Tiananmen Square in 1989.
reply▲try-working2 hours ago
[-] reply▲> Internet comments say that open sourcing is a national strategy, a loss maker subsidized by the government. On the contrary, it is a commercial strategy and the best strategy available in this industry.
This sounds whole lot like potatoh potahto. I think the former argument is very much the correct one: China can undercut everyone and win, even at a loss. Happened with solar panels, steel, evs, sea food - it's a well tested strategy and it works really well despite the many flavors it comes in.
That being said a job well done for the wrong reasons is still a job well done so we should very much welcome these contributions, and maybe it's good to upset western big tech a bit so it's remains competitive.
try-working35 minutes ago
[-] It is not only that Chinese labs can undercut on price. It is that they must. They must give away their models for free by open sourcing them, and they must even give away free inference services for people to try them. That is the point of the post.
reply▲I_am_tiberius2 hours ago
[-] Open weight!
reply▲reply▲DeepSeek's models are indeed open weight. Why do you feel that pointing this out would be considered slander?
reply▲kortilla19 minutes ago
[-] It’s not slander to say something true. These are open weights, not open source. They don’t provide the training data or the methodology requires to reproduce these weights.
So you can’t see what facts are pruned out, what biases were applied, etc. Even more importantly, you can’t make a slightly improved version.
This model is as open source as a windows XP installation ISO.
Weights are the source, training data is the compiler
reply▲Training data == source code, training algorithm == compiler, model weights == compiled binary.
reply▲Training algorithm is the programmer, weights are the code that you run in an interpreter
reply▲isn't it more like the data is the source, the training process is the compiler, and the weights are the binary output.
reply▲I’m deeply interested and invested in the field but I could really use a support group for people burnt out from trying to keep up with everything. I feel like we’ve already long since passed the point where we need AI to help us keep up with advancements in AI.
reply▲satvikpendem2 hours ago
[-] Don't keep up. Much like with news, you'll know when you need to know, because someone else will tell you first.
reply▲The players barely ever change. People don't have problems following sports, you shouldn't struggle so much with this once you accept top spot changes.
reply▲I didn't express this well but my interest isn't "who is in the top spot", and is more _why and _how various labs get the results they do. This is also magnified by the fact that I'm not only interested in hosted providers of inference but local models as well. What's your take on the best model to run for coding on 24GB of VRAM locally after the last few weeks of releases? Which harness do you prefer? What quants do you think are best? To use your sports metaphor it's more than following the national leagues but also following college and even high school leagues as well. And the real interest isn't even who's doing well but WHY, at each level.
reply▲Follow the AI newsletters. They bundle the news along with their Op-Ed and summarize it better.
reply▲It is funny seeing people ping pong between Anthropic and ChatGPT, with similar rhetoric in both directions.
At this point I would just pick the one who's "ethics" and user experience you prefer. The difference in performance between these releases has had no impact on the meaningful work one can do with them, unless perhaps they are on the fringes in some domain.
Personally I am trying out the open models cloud hosted, since I am not interested in being rug pulled by the big two providers. They have come a long way, and for all the work I actually trust to an LLM they seem to be sufficient.
DiscourseFan2 hours ago
[-] I find ChatGPT annoying mostly
reply▲awakeasleep2 hours ago
[-] Open settings > personalization. Set it to efficient base style. Turn off enthusiasm and warmth. You’re welcome
reply▲It honestly has all kinda felt like more of the same ever since maybe GPT4?
New model comes out, has some nice benchmarks, but the subjective experience of actually using it stays the same. Nothing's really blown my mind since.
Feels like the field has stagnated to a point where only the enthusiasts care.
ifwinterco22 minutes ago
[-] For coding Opus 4.5 in q3 2025 was still the best model I've used.
Since then it's just been a cycle of the old model being progressively lobotomised and a "new" one coming out that if you're lucky might be as good as the OG Opus 4.5 for a couple of weeks.
Subjective but as far as I can tell no progress in almost a year, which is a lifetime in 2022-25 LLM timelines
holy shit im right there with you
reply▲Already on Openrouter. Pro version is $1.74/m/input, $3.48/m/output, while flash $0.14/m/input, 0.28/m/output.
reply▲Getting 'Api Error' here :(
Every other model is working fine.
reply▲Try interacting with it through the website, it will give an error and some explanation on the issue. I had to relax my guardrail settings.
reply▲reply▲Its on OR - but currently not available on their anthropic endpoint. OR if you read this, pls enable it there! I am using kimi-2.6 with Claude Code, works well, but Deepseek V4 gives an error:
`https://openrouter.ai/api/messages with model=deepseek/deepseek-v4-pro, OR returns
an error because their Anthropic-compat translator doesn't cover V4 yet. The Claude CLI dutifully surfaces that error as "model...does not exist"
For comparison on openrouter DeepSeek v4 Flash is slightly cheaper than Gemma 4 31b, more expensive than Gemma 4 26b, but it does support prompt caching, which means for some applications it will be the cheapest. Excited to see how it compares with Gemma 4.
reply▲MillionOClock43 minutes ago
[-] I wonder why there aren't more open weights model with support for prompt caching on OpenRouter.
reply▲It is tricky to build good infrastructure for prompt caching.
reply▲For those who rely on open source models but don't want to stop using frontier models, how do you manage it? Do you pay any of the Chinese subscription plans? Do you pay the API directly? After GPT 5.5 release, however good it is, I am a bit tired of this price hiking and reduced quota every week. I am now unemployed and cannot afford more expensive plans for the moment.
reply▲reply▲0xbadcafebee2 hours ago
[-] I don't think we need to compare models to Opus anymore. Opus users don't care about other models, as they're convinced Opus will be better forever. And non-Opus users don't want the expense, lock-in or limits.
As a non-Opus user, I'll continue to use the cheapest fastest models that get my job done, which (for me anyway) is still MiniMax M2.5. I occasionally try a newer, more expensive model, and I get the same results. I have a feeling we might all be getting swindled by the whole AI industry with benchmarks that just make it look like everything's improving.
Which model's best depends on how you use it. There's a huge difference in behaviour between Claude and GPT and other models which makes some poor substitutes for others in certain use cases. I think the GPT models are a bad substitute for Claude ones for tasks such as pair-programming (where you want to see the CoT and have immediate responses) and writing code that you actually want to read and edit yourself, as opposed to just letting GPT run in the background to produce working code that you won't inspect. Yes, GPT 5.4 is cheap and brilliant but very black-box and often very slow IME. GPT-5.4 still seems to behave the same as 5.1, which includes problems like: doesn't show useful thoughts, can think for half an hour, says "Preparing the patch now" then thinks for another 20 min, gives no impression of what it's doing, reads microscopic parts of source files and misses context, will do anything to pass the tests including patching libraries...
reply▲Agree with your assessment, I think after models reached around Opus 4.5 level, its been almost indistinguishable for most tasks. Intelligence has been commoditized, what's important now is the workflows, prompting, and context management. And that is unique to each model.
reply▲Same for me. There
are tasks when I want the smartest model. But for a whole lot of tasks I now default to Sonnet, or go with cheaper models like GLM, Kimi, Qwen. DeepSeek hasn't been in the mix for a while because their previous model had started lagging, but will definitely test this one again.
The tricky part is that the "number of tokens to good result" does absolutely vary, and you need a decent harness to make it work without too much manual intervention, so figuring out which model is most cost-effective for which tasks is becoming increasingly hard, but several are cost-effective enough.
This is not true for some cases e.g. there are stark differences in the correctness of answers in certain type of case work.
reply▲spaceman_202051 minutes ago
[-] I found Opus 4.7 to be actually worse than Opus 4.6 for my use case
Substantially worse at following instructions and overoptimized for maximizing token usage
Is Opus nerfed somehow in Copilot? Ive tried it numerous times, it has never reallt woved me. They seem to have awfully small context windows, but still. Its mostly their reasoning which has been off
Codex is just so much better, or the genera GPT models.
This resonates with me a lot.
I do some stuff with gemini flash and Aider, but mostly because I want to avoid locking myself into a walled garden of models, UIs and company
What do you run these on? I've gotten comfortable with Claude but if folks are getting Opus performance for cheaper I'll switch.
reply▲oceanplexian1 hour ago
[-] You can just use Claude Code with a few env vars, most of these providers offer an Anthropic compatible API
reply▲slopinthebag2 hours ago
[-] Try Charm Crush first, it's a native binary. If it's unbearable, try opencode, just with the knowledge your system will probably be pwned soon since it's JS + NPM + vibe coding + some of the most insufferable devs in the industry behind that product.
If you're feeling frisky, Zed has a decent agent harness and a very good editor.
actually this is not the reason - the harness is significantly better.
There is no comparable harness to Claude Code with skills, etc.
Opencode was getting there, but it seems the founders lost interest. Pi could be it, but its very focused on OpenClaw. Even Codex cli doesnt have all of it.
which harness works well with Deepseek v4 ?
What's the issue with OC? I tried it a bit over 2 months ago, when I was still on Claude API, and it actually liked more that CC (i.e. the right sidebar with the plan and a tendency at asking less "security" questions that CC). Why is it so bad nowadays?
reply▲eh idk. until yesterday opus was the one that got spatial reasoning right (had to do some head pose stuff, neither glm 5.1 nor codex 5.3 could "get" it) and codex 5.3 was my champion at making UX work.
So while I agree mixed model is the way to go, opus is still my workhorse.
creamyhorror16 minutes ago
[-] No, the Deepseek V4 paper itself says that DS-V4-Pro-Max is close to Opus 4.5 in their staff evaluations, not better than 4.6:
> In our internal evaluation, DeepSeek-V4-Pro-Max outperforms Claude Sonnet 4.5 and approaches the level of Opus 4.5.
onchainintel3 hours ago
[-] How does it compare to Opus 4.7? I've been immersed in 4.7 all week participating in the Anthropic Opus 4.7 hackathon and it's pretty impressive even if it's ravenous from a token perspective compared to 4.6
reply▲greenknight3 hours ago
[-] The thing is, it doesnt need to beat 4.7. it just needs to do somewhat well against it.
This is free... as in you can download it, run it on your systems and finetune it to be the way you want it to be.
> you can download it, run it on your systems
In theory, sure, but as other have pointed out you need to spend half a million on GPUs just to get enough VRAM to fit a single instance of the model. And you’d better make sure your use case makes full 24/7 use of all that rapidly-depreciating hardware you just spent all your money on, otherwise your actual cost per token will be much higher than you think.
In practice you will get better value from just buying tokens from a third party whose business is hosting open weight models as efficiently as possible and who make full use of their hardware. Even with the small margin they charge on top you will still come out ahead.
oceanplexian1 hour ago
[-] There are a lot of companies who would gladly drop half a million on a GPU to have private inference that Anthropic or OpenAI can’t use to steal their data.
And that GPU wouldn’t run one instance, the models are highly parallelizable. It would likely support 10-15 users at once, if a company oversubscribed 10:1 that GPU supports ~100 seats. Amortized over a couple years the costs are competitive.
libraryofbabel51 minutes ago
[-] > There are a lot of companies who would gladly drop half a million on a GPU to have private inference that Anthropic or OpenAI can’t use to steal their data.
Obviously, and certainly companies do run their own models because they place some value on data sovereignty for regulatory or compliance or other reasons. (Although the framing that Anthropic or OpenAI might "steal their data" is a bit alarmist - plenty of companies, including some with _highly_ sensitive data, have contracts with Anthropic or OpenAI that say they can't train future models on the data they send them and are perfectly happy to send data to Claude. You may think they're stupid to do that, but that's just your opinion.)
> the models are highly parallelizable. It would likely support 10-15 users at once.
Yes, I know that; I understand LLM internals pretty well. One instance of the model in the sense of one set of weights loaded across X number of GPUs; of course you can then run batch inference on those weights, up to the limits of GPU bandwidth and compute.
But are those 100 users you have on your own GPUs usings the GPUs evenly across the 24 hours of the day, or are they only using them during 9-5 in some timezone? If so, you're leaving your expensive hardware idle for 2/3 of the day and the third party providers hosting open weight models will still beat you on costs, even without getting into other factors like they bought their GPUs cheaper than you did. Do the math if you don't believe me.
Do you think a lot of people have “systems” to run a 1.6T model?
reply▲To me, the important thing isn't that I can run it, it's that I can pay someone else to run it. I'm finding Opus 4.7 seems to be weirdly broken compared to 4.6, it just doesn't understand my code, breaks it whenever I ask it to do anything.
Now, at the moment, i can still use 4.6 but eventually Anthropic are going to remove it, and when it's gone it will be gone forever. I'm planning on trying Deepseek v4, because even if it's not quite as good, I know that it will be available forever, I'll always be able to find someone to run it.
No, but businesses do. Being able to run quality LLMs without your business, or business's private information, being held at the mercy of another corp has a lot of value.
reply▲What type of system is needed to self host this? How much would it cost?
reply▲Depends how many users you have and what is "production grade" for you but like 500k gets you a 8x B200 machine.
reply▲Depends on fast you want it to be. I’m guessing a couple of $10k mac studio boxes could run it, but probably not fast enough to enjoy using it.
reply▲One GB200 NVL72 from Nvidia would do it. $2-3 million, or so. If you're a corporation, say Walmart or PayPal, that's not out of the question.
If you want to go budget corporate, 7 x H200 is just barely going to run it, but all in, $300k ought to do it.
How many users can you serve with that?
reply▲For the H200, between 150-700. The GB200 gets you something like 2-10k users.
reply▲$20K worth of RTX 6000 Blackwell cards should let you run the Flash version of the model.
reply▲Not really - on prem llm hosting is extremely labor and capital intensive
reply▲But can be, and is, done. I work for a bootstrapped startup that hosts a DeepSeek v3 retrain on our own GPUs. We are highly profitable. We're certainly not the only ones in the space, as I'm personally aware of several other startups hosting their own GLM or DeepSeek models.
reply▲Why a retrain? What are you using the model for?
reply▲onchainintel3 hours ago
[-] Completely agree, not suggesting it needs ot just genuinely curious. Love that it can be run locally though. Open source LLMs punching back pretty hard against proprietary ones in the cloud lately in terms of performance.
reply▲What's the hardware cost to running it?
reply▲Probably like 100 USD/hour
reply▲I was curious, and some [intrepid soul](
https://wavespeed.ai/blog/posts/deepseek-v4-gpu-vram-require...) did an analysis. Assuming you do everything perfectly and take full advantage of the model's MoE sparsity, it would take:
- To run at full precision: "16–24 H100s", giving us ~$400-600k upfront, or $8-12/h from [us-east-1](https://intuitionlabs.ai/articles/h100-rental-prices-cloud-c...).
- To run with "heavy quantization" (16 bits -> 8): "8xH100", giving us $200K upfront and $4/h.
- To run truly "locally"--i.e. in a house instead of a data center--you'd need four 4090s, one of the most powerful consumer GPUs available. Even that would clock in around $15k for the cards alone and ~$0.22/h for the electricity (in the US).
Truly an insane industry. This is a good reminder of why datacenter capex from since 2023 has eclipsed the Manhattan Project, the Apollo program, and the US interstate system combined...
oceanplexian1 hour ago
[-] All these number are peanuts to a mid sized company. A place I worked at used to spend a couple million just for a support contract on a Netapp.
10 years from now that hardware will be on eBay for any geek with a couple thousand dollars and enough power to run it.
That article is a total hallucination.
"671B total / 37B active"
"Full precision (BF16)"
And they claim they ran this non-existent model on vLLM and SGLang over a month and a half ago.
It's clickbait keyword slop filled in with V3 specs. Most of the web is slop like this now. Sigh.
johnmaguire3 hours ago
[-] ... if you have 800 GB of VRAM free.
reply▲inventor77772 hours ago
[-] I remember reading about some new frameworks have been coming out to allow Macs to stream weights of huge models live from fast SSDs and produce quality output, albeit slowly. Apart from that...good luck finding that much available VRAM haha
reply▲spaceman_202050 minutes ago
[-] Tbh I was more productive with 4.6 than ever before and if AI progress locks in permanently at 4.6 tier, I’d be pretty happy
reply▲It is more than good enough and has effectively caught up with Opus 4.6 and GPT 5.4 according to the benchmarks.
It's about 2 months behind GPT 5.5 and Opus 4.7.
As long as it is cheap to run for the hosting providers and it is frontier level, it is a very competitive model and impressive against the others. I give it 2 years maximum for consumer hardware to run models that are 500B - 800B quantized on their machines.
It should be obvious now why Anthropic really doesn't want you to run local models on your machine.
Vibes > Benchmarks. And it's all so task-specific. Gemini 3 has scored very well in benchmarks for very long but is poor at agentic usecases. A lot of people prefering Opus 4.6 to 4.7 for coding despite benchmarks, much more than I've seen before (4.5->4.6, 4->4.5).
Doesn't mean Deepseek v4 isn't great, just benchmarks alone aren't enough to tell.
snovv_crash2 hours ago
[-] With the ability of the Qwen3.6 27B, I think in 2 years consumers will be running models of this capability on current hardware.
reply▲What's going to change in 2 years that would allow users to run 500B-800B parameter models on consumer hardware?
reply▲doctoboggan3 hours ago
[-] Is it honestly better than Opus 4.6 or just benchmaxxed? Have you done any coding with an agent harness using it?
If its coding abilities are better than Claude Code with Opus 4.6 then I will definitely be switching to this model.
Apparently glm5.1 and qwen coder latest is as good as opus 4.6 on benchmarks. So I tried both seriously for a week (glm Pro using CC) and qwen using qwen companion. Thought I could save $80 a month. Unfortunately after 2 days I had switched back to Max. The speed (slower on both although qwen is much faster) and errors (stupid layout mistakes, inserting 2 footers then refusing to remove one, not seeing obvious problems in screenshots & major f-ups of functionality), not being able to view URLs properly, etc. I'll give deepseek a go but I suspect it will be similar. The model is only half the story. Also been testing gpt5.4 with codex and it is very almost as good as CC... better on long running tasks running in background. Not keen on ChatGPT codex 'personality' so will stick to CC for the most part.
reply▲Their Chinese announcement says that, based on internal employee testing, it is not as good as Opus 4.6 Thinking, but is slightly better than Opus 4.6 without Thinking enabled.
reply▲I appreciate this, makes me trust it more than benchmarks.
reply▲That's super interesting, isn't Deepseek in China banned from using Anthropic models? Yet here they're comparing it in terms of internal employee testing.
reply▲They use VPN to access. Even Google Deepmind uses Anthropic. There was a fight within Google as to why only DeepMind is allowed to Claude while rest of the Google can't.
reply▲For the curious, I did some napkin math on their posted benchmarks and it racks up 20.1 percentage point difference across the 20 metrics where both were scored, for an average improvement of about 2% (non-pp). I really can't decide if that's mind blowing or boring?
Claude4.6 was almost 10pp better at at answering questions from long contexts ("corpuses" in CorpusQA and "multiround conversations" in MRCR), while DSv4 was a staggering 14pp better at one math challenge (IMOAnswerBench) and 12pp better at basic Q&A (SimpleQA-Verified).
NitpickLawyer2 hours ago
[-] > (better than Opus 4.6)
There we go again :) It seems we have a release each day claiming that. What's weird is that even deepseek doesn't claim it's better than opus w/ thinking. No idea why you'd say that but anyway.
Dsv3 was a good model. Not benchmaxxed at all, it was pretty stable where it was. Did well on tasks that were ood for benchmarks, even if it was behind SotA.
This seems to be similar. Behind SotA, but not by much, and at a much lower price. The big one is being served (by ds themselves now, more providers will come and we'll see the median price) at 1.74$ in / 3.48$ out / 0.14$ cache. Really cheap for what it offers.
The small one is at 0.14$ in / 0.28$ out / 0.028$ cache, which is pretty much "too cheap to matter". This will be what people can run realistically "at home", and should be a contender for things like haiku/gemini-flash, if it can deliver at those levels.
slopinthebag2 hours ago
[-] Anthropic fans would claim God itself is behind Opus by 3-6 months and then willingly be abused by Boris and one of his gaslighting tweets.
LMAO
NitpickLawyer1 hour ago
[-] > Anthropic fans ...
I have no idea why you'd think that, but this is straight from their announcement here (https://mp.weixin.qq.com/s/8bxXqS2R8Fx5-1TLDBiEDg):
> According to evaluation feedback, its user experience is better than Sonnet 4.5, and its delivery quality is close to Opus 4.6's non-thinking mode, but there is still a certain gap compared to Opus 4.6's thinking mode.
This is the model creators saying it, not me.
sergiotapia3 hours ago
[-] The dragon awakes yet again!
reply▲kindkang20242 hours ago
[-] There appears a flight of dragons without heads. Good fortune.
That's literally what the I Ching calls "good fortune."
Competition, when no single dragon monopolizes the sky, brings fortune for all.
Is there a harness that is as good as cloud code that can be used with open weight models?
reply▲npodbielski2 minutes ago
[-] Never used Claude myself but there are agents that can use local model. I.e.
- Jetbrains Junie
- Mistral Vibe
reply▲Numerlor10 minutes ago
[-] I've liked Hermes agent, but never used Claude code so don't know how it compares
reply▲It is great! I asked the question what I always ask of new models ("what would Ian M Banks think about the current state of AI") and it gave me a brilliant answer! Funny enough the answer contained multiple criticisms of his own creators ("Chinese state entities", "Social Credit System").
reply▲They released 1.6 T pro base model on huggingface. First time I'm seeing a "T" model here.
reply▲The Flash version is 284B A13B in mixed FP8 / FP4 and the full native precision weights total approximately 154 GB. KV cache is said to take 10% as much space as V3. This looks very accessible for people running "large" local models. It's a nice follow up to the Gemma 4 and Qwen3.5 small local models.
reply▲Price is appealing to me. I have been using gemini 3 flash mainly for chat. I may give it a try.
input: $0.14/$0.28 (whereas gemini $0.5/$3)
Does anyone know why output prices have such a big gap?
Output is what the compute is used for above all else; costs more hardware time basically than prompt processing (input) which is a lot faster
reply▲tokenmaxxinej1 hour ago
[-] input tokens are processed at 10-50 times the speed of output tokens since you can process then in batches and not one at a time like output tokens
reply▲Just tested it via openrounter in the Pi Coding agent and it regularly fails to use the read and write tool correctly, very disappointing. Anyone know a fix besides prompting "always use the provided tools instead of writing your own call"
reply▲They have just released it, give it some time, they probably haven't pretested it with Pi
reply▲How can they fix it after the release? They would have to retrain/finetune it further, no?
reply▲It's only in preview right now. And anyway, yes, models regularly get updated training.
But in this case, it's more likely just to be a tooling issue.
Any way to connect this to claude code?
reply▲What's the current best framework to have a 'claude code' like experience with Deepseek (or in general, an open-source model), if I wanted to play?
reply▲whoopdeepoo2 hours ago
[-] You can use deepseek with Claude code
reply▲esperent55 minutes ago
[-] You can, but does it work well? I assume CC has all kinds of Claude specific prompts in it, wouldn't you be better with a harness designed to be model agnostic like pi.dev or OpenCode?
reply▲Alifatisk56 minutes ago
[-] You can use CC with other models, you aren’t forced to use Claude model.
reply▲claude-code-cli/opencode/codex
reply▲reply▲No way. The Pro pelican is fatter, has a customized front fork, and the sun is shining! He’s definitely living the best life.
reply▲The pro pelican is a work of art! It goes dimensions that no other LLM has gone before.
reply▲yeah. look at these 4 feathers (?) on his bum too.
reply▲This is just a random thought, but have you tried doing an 'agentic' pelican?
As in have the model consider its generated SVG, and gradually refine it, using its knowledge of the relative positions and proportions of the shapes generated, and have it spin for a while, and hopefully the end result will be better than just oneshotting it.
Or maybe going even one step further - most modern models have tool use and image recognition capabilities - what if you have it generate an SVG (or parts/layers of it, as per the model's discretion) and feed it back to itself via image recognition, and then improve on the result.
I think it'd be interesting to see, as for a lot of models, their oneshot capability in coding is not necessarily corellated with their in-harness ability, the latter which really matters.
I tried that for the GPT-5 launch - a self-improving loop that renders the SVG, looks at it and tries again - and the results were surprisingly disappointing.
I should try it again with the more recent models.
The Flash one is pretty impressive. Might be my favorite so far in the pelican-riding-a-bicycle series
reply▲DeepSeek pelicans are the angriest pelicans I’ve seen so far.
reply▲Being a bicycle geometry nerd I always look at the bicycle first.
Let me tell you how much the Pro one sucks... It looks like failed Pedersen[1]. The rear wheel intersects with the bottom bracket, so it wouldn't even roll. Or rather, this bike couldn't exist.
The flash one looks surprisingly correct with some wild fork offset and the slackest of seat tubes. It's got some lowrider[2] aspirations with the small wheels, but with longer, Rivendellish[3], chainstays. The seat post has different angle than the seat tube, so good luck lowering that.
[1] https://en.wikipedia.org/wiki/Pedersen_bicycle
[2] https://en.wikipedia.org/wiki/Lowrider_bicycle
[3] https://www.rivbike.com/
This is an excellent comment. Thanks for this - I've only ever thought about whether the frame is the right shape, I never thought about how different illustrations might map to different bicycle categories.
reply▲Some other reactions:
I wonder which model will try some more common spoke lacing patterns. Right now there seems to be a preference for radial lacing, which is not super common (but simple to draw). The Flash and Pro one uses 16 spoke rims, which actually exist[1] but are not super common.
The Pro model fails badly at the spokes. Heck, the spokes sit on the outside of the drive side of the rim and tire. Have a nice ride riding on the spokes (instead of the tire) welded to the side of your rim.
Both bikes have the drive side on the left, which is very very uncommon. That can't exist in the training data.
[1] https://cicli-berlinetta.com/product/campagnolo-shamal-16-sp...
The Pedersen looks like someone failed the "draw a bicycle" test and decided to adjust the universe.
reply▲I think the pelican on a bike is known widely enough that of seizes to be useful as a benchmark. There is even a pelican briefly appearing in the promo video of GPT-5, if I'm not mistaken
https://openai.com/gpt-5/. So the companies are apparently aware of it.
To me this is the perfect proof that
1) LLM is not AGI. Because surely if AGI it would imply that pro would do better than flash?
2) and because of the above, Pelican example is most likely already being benchmaxxed.
Is it then Deepseek hosted by Deepseek?
How much does the drawing change if you ask it again?
brutal_chaos_1 hour ago
[-] What was your prompt for the image? Apologies if this should be obvious.
reply▲>Generate an SVG of a pelican riding a bicycle
at the top of the linked pages.
I really like the pro version. The pelican is so cute.
reply▲Where is the GPT 5.5 Pelican?
reply▲This should not be the top comment on every model release post. It's getting tiring.
reply▲This should be the bottom comment on the pelican comment on every model release post.
reply▲Clearly the top comment should be "Imagine a beowulf cluster of Deepseek v4!"
reply▲My mother was murdered by Beowulf, you insensitive Claude!
reply▲I don't mind that High Flyer completely ripped off Anthropic to do this so much as I mind that they very obviously waited long enough for the GAB to add several dozen xz-level easter eggs to it.
reply▲MMLU-Pro:
Gemini-3.1-Pro at 91.0
Opus-4.6 at 89.1
GPT-5.4, Kimi2.6, and DS-V4-Pro tied at 87.5
Pretty impressive
Funny how Gemini is theoretically the best -- but in practice all the bugs in the interface mean I don't want to use it anymore. The worst is it forgets context (and lies about it), but it's very unreliable at reading pdfs (and lies about it). There's also no branch, so once the context is lost/polluted, you have to start projects over and build up the context from scratch again.
reply▲spaceman_202047 minutes ago
[-] The sheer number of bugs and lack of meaningful improvements in Google products is a clear counterargument to the AI bull thesis
If AI was so good at coding, why can’t it actually make a usable Gemini/AI Studio app?
esperent51 minutes ago
[-] Yeah if I could use Gemini with pi.dev that would be my choice. But Gemini CLI is just so, so bad.
reply▲lazycatjumping45 minutes ago
[-] I gave up on Gemini 3.1 Pro in VSCode after 2 hours. They fully refunded me.
reply▲coderssh47 minutes ago
[-] Feels like the real story here is cost/performance tradeoff rather than raw capability. Benchmarks keep moving incrementally, but efficiency gains like this actually change who can afford to build on top.
reply▲865 GB: I am going to need a bigger GPU.
reply▲This is shockingly cheap for a near frontier model. This is insane.
For context, for an agent we're working on, we're using 5-mini, which is $2/1m tokens. This is $0.30/1m tokens. And it's Opus 4.6 level - this can't be real.
I am uncomfortable about sending user data which may contain PII to their servers in China so I won't be using this as appealing as it sounds. I need this to come to a US-hosted environment at an equivalent price.
Hosting this on my own + renting GPUs is much more expensive than DeepSeek's quoted price, so not an option.
esperent52 minutes ago
[-] > I am uncomfortable about sending user data which may contain PII to their servers in China
As a European I feel deeply uncomfortable about sending data to US companies where I know for sure that the government has access to it.
I also feel uncomfortable sending it to China.
If you'd asked me ten years ago which one made me more uncomfortable. China.
But now I'm not so sure, in fact I'm starting to lean towards the US as being the major risk.
Right now Im much more worried about sending data to the US and A.. At least theres a less chanse it will be missused against -me-
reply▲At this point 'frontier model release' is a monthly cadence, Kimi 2.6 Claude 4.6 GPT 5.5, the interesting question is which evals will still be meaningful in 6 months.
reply▲Oh well, I should have bought 2x 512GB RAM MacStudios, not just one :(
reply▲cztomsik32 minutes ago
[-] So is this the first AI lab using MUON for their frontier model?
reply▲hodgehog1128 minutes ago
[-] No, Muon was developed by Moonshot; they've been using it in their Kimi models since Kimi K2 in 2025.
reply▲Such different time now than early 2025 when people thought Deepaeek was going to kill the market for Nvidia.
reply▲Ifkaluva48 minutes ago
[-] They might still kill the market for NVIDIA, if future releases prioritize Huawei chips
reply▲Is V4 still not a multi-modal model?
reply▲Excited that the long awaited v4 is finally out. But feel sad that it's not multimodal native.
reply▲Looking forward to DeepSeek Coding Plan
reply▲This FLash model might be affordable for OpenClaw. I run it on my mac 48gb ram now but it's slowish.
reply▲For those who didn't check the page yet, it just links to the API docs being updated with the upcoming models, not the actual model release.
reply▲A few hours after GPT5.5 is wild. Can’t wait to try it.
reply▲KaoruAoiShiho3 hours ago
[-] SOTA MRCR (or would've been a few hours earlier... beaten by 5.5), I've long thought of this as the most important non-agentic benchmark, so this is especially impressive. Beats Opus 4.7 here
reply▲giving meta a run for its money, esp when it was supposed to be the poster child for OSS models. deepseek is really overshadowing them rn
reply▲Which version fits in a Mac Studio M3 Ultra 512 GB?
reply▲How can you reasonably try to get near frontier (even at all tps) on hardware you own? Maybe under 5k in cost?
reply▲revolvingthrow2 hours ago
[-] For flash? 4 bit quant, 2x 96GB gpu (fast and expensive) or 1x 96GB gpu + 128GB ram (still expensive but probably usable, if you’re patient).
A mac with 256 GB memory would run it but be very slow, and so would be a 256GB ram + cheapo GPU desktop, unless you leave it running overnight.
The big model? Forget it, not this decade. You can theoretically load from SSD but waiting for the reply will be a religious experience.
Realistically the biggest models you can run on local-as-in-worth-buying-as-a-person hardware are between 120B and 200B, depending on how far you’re willing to go on quantization. Even this is fairly expensive, and that’s before RAM went to the moon.
Flash is less than 160 GB. No need to quantize to fit in 2x 96 GB. Not sure how much context fits in 30 GB, but it should be a good amount.
reply▲It seems to be 160GB at mixed FP4+FP8 precision, FYI. Full FP8 is 250GB+. (B)F16 at around double I would assume.
reply▲There is no BF16. There is no FP8 for the instruct model. The instruct model at full precision is 160 GB (mixed FP4 and FP8). The base model at full precision is 284 GB (FP8). Almost everyone is going to use instruct. But I do love to see base models released.
reply▲Run on an old HEDT platform with a lot of parallel attached storage (probably PCIe 4) and fetch weights from SSD. You'd ultimately be limited by the latency of these per-layer fetches, since MoE weights are small. You could reduce the latencies further by buying cheap Optane memory on the second-hand market.
reply▲awakeasleep2 hours ago
[-] The same way you fit a bucket wheel excavator in your garage
reply▲A loaded macbook pro can get you to the frontier from 24 months ago at ~10-40tok/s, which is plenty fast enough for regular chatting.
reply▲The low end could be something like an eBay-sourced server with a truckload of DDR3 ram doing all-cpu inference - secondhand server models with a terabyte of ram can be had for about 1.5K. The TPS will be absolute garbage and it will sound like a jet engine, but it will nominally run.
The flash version here is 284B A13B, so it might perform OK with a fairly small amount of VRAM for the active params and all regular ram for the other params, but I’d have to see benchmarks. If it turns out that works alright, an eBay server plus a 3090 might be the bang-for-buck champ for about $2.5K (assuming you’re starting from zero).
Interesting note:
"Due to constraints in high-end compute capacity, the current service capacity for Pro is very limited. After the 950 supernodes are launched at scale in the second half of this year, the price of Pro is expected to be reduced significantly."
So it's going to be even cheaper
Any visualised benchmark/scoreboard for comparison between latest models? DeepSeek v4 and GPT-5.5 seems to be ground breaking.
reply▲Does deepseek has any coding plan?
reply▲Aaaand it cant still name all the states in India,or say what happened in 1989
reply▲The paper is here: [0]
Was expecting that the release would be this month [1], since everyone forgot about it and not reading the papers they were releasing and 7 days later here we have it.
One of the key points of this model to look at is the optimization that DeepSeek made with the residual design of the neural network architecture of the LLM, which is manifold-constrained hyper-connections (mHC) which is from this paper [2], which makes this possible to efficiently train it, especially with its hybrid attention mechanism designed for this.
There was not that much discussion around it some months ago here [3] about it but again this is a recommended read of the paper.
I wouldn't trust the benchmarks directly, but would wait for others to try it for themselves to see if it matches the performance of frontier models.
Either way, this is why Anthropic wants to ban open weight models and I cannot wait for the quantized versions to release momentarily.
[0] https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...
[1] https://news.ycombinator.com/item?id=47793880
[2] https://arxiv.org/abs/2512.24880
[3] https://news.ycombinator.com/item?id=46452172
> this is why Anthropic wants to ban open weight models
Do you have a source?
louiereederson2 hours ago
[-] More like he wants to ban accelerator chip sales to China, which may be about “national security” or self preservation against a different model for AI development which also happens to be an existential threat to Anthropic. Maybe those alternatives are actually one and the same to him.
reply▲Is there a Quantized version of this?
reply▲We will be hosting it soon at getlilac.com!
reply▲gigatexal44 minutes ago
[-] Has anyone used it? How does it compare to gpt 5.5 or opus 4.7?
reply▲coolThingsFirst51 minutes ago
[-] I got an API key without credit card details I didn’t know they had a free plan.
reply▲Using it with opencode sometimes it generates commands like:
bash({"command":"gh pr create --title "Improve Calendar module docs and clean up idiomatic Elixir" --body "$(cat <<'EOF'
Problem
The Calendar modu...
like generating output, but not actually running the bash command so not creating the PR ultimately. I wonder if it's a model thing, or an opencode thing.
Anyone tried with make web UI with it? How good is it? For me opus is only worth because of it.
reply▲Incredible model quality to price ratio
reply▲How long does it usually take for folks to make smaller distills of these models? I really want to see how this will do when brought down to a size that will run on a Macbook.
reply▲inventor77772 hours ago
[-] Weren't there some frameworks recently released to allow Macs to stream weights from fast SSDs and thus fit way more parameters than what would normally fit in RAM?
I have never tried one yet but I am considering trying that for a medium sized model.
I've been calling that the "streaming experts" trick, the key idea is to take advantage of Mixture of Expert models where only a subset of the weights are used for each round of calculations, then load those weights from SSD into RAM for each round.
As I understand it if DeepSeek v4 Pro is a 1.6T, 49B active that means you'd need just 49B in memory, so ~100GB at 16 bit or ~50GB at 8bit quantized.
v4 Flash is 284B, 13B active so might even fit in <32GB.
The "active" count is not very meaningful except as a broad measure of sparsity, since the experts in MoE models are chosen per layer. Once you're streaming experts from disk, there's nothing that inherently requires having 49B parameters in memory at once. Of course, the less caching memory does, the higher the performance overhead of fetching from disk.
reply▲inventor77772 hours ago
[-] Ahh, that actually makes more sense now. (As you can tell, I just skimmed through the READMEs and starred "for later".)
My Mac can fit almost 70B (Q3_K_M) in memory at once, so I really need to try this out soon at maybe Q5-ish.
> ~100GB at 16 bit or ~50GB at 8bit quantized.
V4 is natively mixed FP4 and FP8, so significantly less than that. 50 GB max unquantized.
Streaming weights from RAM to GPU for prefill makes sense due to batching and pcie5 x16 is fast enough to make it worthwhile.
Streaming weights from RAM to GPU for decode makes no sense at all because batching requires multiple parallel streams.
Streaming weights from SSD _never_ makes sense because the delta between SSD and RAM is too large. There is no situation where you would not be able to fit a model in RAM and also have useful speeds from SSD.
These are more like experiments than a polished release as of yet. And the reduction in throughput is high compared to having the weights in RAM at all times, since you're bottlenecked by the SSD which even at its fastest is much slower than RAM.
reply▲I hope the update is an improvement. Losing 3.2 would be a real loss, it's excellent.
reply▲History doesn't always repeat itself.
But if it does, then in the following week we'll see DeepSeek4 floods every AI-related online space. Thousands of posts swearing how it's better than the latest models OpenAI/Anthropic/Google have but only costs pennies.
Then a few weeks later it'll be forgotten by most.
It's difficult because even if the underlying model is very good, not having a pre-built harness like Claude Code makes it very un-sticky for most devs. Even at equal quality, the friction (or at least perceived friction) is higher than the mainstream models.
reply▲OpenCode? Pi?
If one finds it difficult to set up OpenCode to use whatever providers they want, I won't call them 'dev'.
The only real friction (if the model is actually as good as SOTA) is to convince your employer to pay for it. But again if it really provides the same value at a fraction of the cost, it'll eventually cease to be an issue.
throwa3562621 hour ago
[-] "If one finds it difficult to set up OpenCode to use whatever providers they want, I won't call them 'dev'."
I feel the same way. But look at the ollama vs llama.cpp post from HN few days back and you will see most of the enthusiasts in this space are very non technical people.
I think you mean ollama vs llama.cpp.
reply▲cmrdporcupine2 hours ago
[-] They have instructions right on their page on how to use claude code with it.
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