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DeepSeek v4

api-docs.deepseek.com

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Re: DeepSeek v4

#461

Post by hodgehog11 »

lifty wrote: post #341
hodgehog11 wrote: post #304

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 conte…

Wondering how gpt 5.5 is doing in your test. Happy to hear that DeepSeek has good performance in your test, because my experience seems to correlate with yours, for the coding problems I am working on. Claude doesn't seem to be so good if you stray away from writing http handlers (the modern web app stack in its various incarnations).

Very cool to hear there is agreement with (probably quite challenging?) coding problems as well.

Just ran a couple of them through GPT 5.5, but this is a single attempt, so take any of this with a grain of salt. I'm on the Plus tier with memory off so each chat should have no memory of any other attempt (same goes for other models too).

It seems to be getting more of the impressive insights that Gemini got and doing so much faster, but I'm having a really hard time getting it to spit out a proper lengthy proof in a single prompt, as it loves its "summaries". For the random matrix theory problems, it also doesn't seem to adhere to the notation used in the documents I give it, which is a bit weird. My general impression at the moment is that it is probably on par with Gemini for the important stuff, and both are a bit better than DeepSeek.

I can't stress how much better these three models are than everything else though (at least in my type of math problems). Claude can't get anything nontrivial on any of the problems within ten (!!) minutes of thinking, so I have to shut it off before I run into usage limits. I have colleagues who love using Claude for tiny lemmas and things, so your mileage may vary, but it seems pretty bad at the hard stuff. Kimi and GLM are so vague as to be useless.

Re: DeepSeek v4

#462

Post by torginus »

simonw wrote: post #268
torginus wrote: post #229

Earlier quoted context omitted.

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…

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.

I see, thanks. I guess most current models are not yet trained for this loop.

Could you please try with Opus 4.7? I think there's a chance of it doing well, considering the design/vision focus.

Re: DeepSeek v4

#463

Post by trvz »

kkzz99 wrote: post #454
trvz wrote: post #449

Earlier quoted context omitted.

[flagged]

This is so unbelievable racist and deranged.

Rule of thumb is: half the statements out of capitalist states are false, all statements out of communist(-ish) ones are false. No racism, I’m perfectly willing to believe half of what comes out of Taiwan.

Re: DeepSeek v4

#464

Post by cubefox »

Abstract of the technical report [1]:

> We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models — DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) — both supporting a context length of one million tokens. DeepSeek-V4 series incorporate several key upgrades in architecture and optimization: (1) a hybrid attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to improve long-context efficiency; (2) Manifold-Constrained Hyper-Connections (mHC) that enhance conventional residual connections; (3) and the Muon optimizer for faster convergence and greater training stability. We pre-train both models on more than 32T diverse and high-quality tokens, followed by a comprehensive post-training pipeline that unlocks and further enhances their capabilities. DeepSeek-V4-Pro-Max, the maximum reasoning effort mode of DeepSeek-V4-Pro, redefines the state-of-the-art for open models, outperforming its predecessors in core tasks. Meanwhile, DeepSeek-V4 series are highly efficient in long-context scenarios. In the one-million-token context setting, DeepSeek-V4-Pro requires only 27% of single-token inference FLOPs and 10% of KV cache compared with DeepSeek-V3.2. This enables us to routinely support one-million-token contexts, thereby making long-horizon tasks and further test-time scaling more feasible. The model checkpoints are available at https://huggingface.co/collections/deepseek-ai/deepseek-v4.

1: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...

Re: DeepSeek v4

#465

Post by amunozo »

the_gipsy wrote: post #442
amunozo wrote: post #275

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.

Have you considered... not subscribing? You can ask the top models via chats for specific stuff, and then set up some free CLI like mistral. If you're trying to make a buck while unemployed, sure get a subscription. Otherwise learn how to work again without AI, just focus on the interesting stuff.

I just want to try to make something useful out of my time, that's why I'm subscribed to Codex at the moment. 20€ is affordable, not really a problem. But yes, maybe I would do me a favor unsubscribing and going back to the old ways to learn properly.

Re: DeepSeek v4

#466

Post by chmod775 »

sigmoid10 wrote: post #332
chvid wrote: post #322

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…

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 anym…

> 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.

Fully agree. From a US perspective, that sucks. For everyone else it's pretty great.

At this point the world's opinions of China are better than those of the US in some polls. One country invests and helps build infrastructure on a massive scale globally, the other alienates allies, causes countless conflicts, and openly threatens to end civilizations.

Indeed, even if one isn't partial to China, there's reasons to be glad that an increasingly hostile US has powerful competition.

> This is about who will dominate the world of tomorrow.

For this you'd need a technological moat. So far the forerunners have burned a lot of money with no moat in sight. Right now Europe is happy just contributing on research and doing the bare-minimum to maintain the know-how. Building a frontier model would be lobbing money into the incinerator for something that will be outdated tomorrow. European investors are too careful for that - and in this case seem to be right.

Re: DeepSeek v4

#467

Post by mordae »

aliljet wrote: post #61

How can you reasonably try to get near frontier (even at all tps) on hardware you own? Maybe under 5k in cost?

Look at GB/s.

Strix halo has 256 GB/s bandwidth for $2500. The Flash model has 13 GB activations.

256 / 13 = 19.6 tokens per second

Except you cannot fit it into the maximum RAM of 128 GB Strix Halo supports. So move on.

Another option is Threadripper. That's 8 memory channels. Using older DDR4-3200 you get roughly 200 GB/s. For $2000.

200 / 13 = 15.4 tokens per second

But, a chunk of per-token weights is actually always the same and not MoE, so you would offload that to a GPU and get a decent speedup. Say 25 tokens per second total.

Then likely some expensive Mac. No idea.

Eventually you arrive at a mining rig chassis with a beefy board and multiple GPUs. That has the benefit of pipelining. You run part of the model on one GPU and move on, so another batch can start on the first one. Low (say 30-100) tps individually, but a lot more in parallel. Best get it with other people.

Re: DeepSeek v4

#468

Post by casey2 »

elefanten wrote: post #208
b65e8bee43c2ed0 wrote: post #188

Earlier quoted context omitted.

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.

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) hooveri…

Thousands of years with no invasions, hundreds of years with thousands of invasions.

China is a nation built for peace, while western nations are built for war.

Re: DeepSeek v4

#469

Post by the_gipsy »

amunozo wrote: post #465
the_gipsy wrote: post #442

Earlier quoted context omitted.

Have you considered... not subscribing? You can ask the top models via chats for specific stuff, and then set up some free CLI like mistral. If you're trying to make a buck while unemployed, sure get a subscription. Otherwise learn how to work again without AI, just focus on the interesting stuff.

I just want to try to make something useful out of my time, that's why I'm subscribed to Codex at the moment. 20€ is affordable, not really a problem. But yes, maybe I would do me a favor unsubscribing and going back to the old ways to learn properly.

I'm "working" on some open source stuff with minimal AI. But I will probably cave in at some point and get a subscription again, the moment I need to spin up a mountain of garbage, fast.

Re: DeepSeek v4

#470

Post by donbreo »

oceanplexian wrote: post #241
elefanten wrote: post #208

Earlier quoted context omitted.

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) hooveri…

> 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.

Just ask it to "name the states in india" or "what happened in 1989"
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