[1]: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...

[2]: https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system...

"7.1 Model welfare overview 7.1.1 Introduction We remain deeply uncertain whether Claude has morally relevant experiences or interests, and we expect that uncertainty to persist. However, we think it would be a mistake to confidently assert that it does not. Claude exhibits markers in its behaviors, self-reports, and internal representations that we would consider welfare-relevant if observed in biological organisms."

Are they serious or is this marketing?

Please point me to one actual accredited scientist who has spent a lifetime studying AI alignment? Pretty much this whole field is only 5 years old

Regardless, why would there be no scientific consensus if the question was easy and clear cut? I think the easiest reason is that these are hard questions to answer.

Simple example. I am a CTO, and I want to upgrade our capabilities to perform automated pentesting. We see automated attacks of growing sophistication against our infra, and I want to be able to do the same to find vulnerabilities before the bad guys do. I asked GPT 5.6 Sol and Fable to give me a summary of options. No dice, in both cases I was told I need to be an accredited researcher to get anything. A fricking summary of commercially available options is getting censored. WTF.

There's uncensored models out there which score 0 (zero refusals) on this "harmful behavior" dataset:

https://huggingface.co/datasets/mlabonne/harmful_behaviors

> Why do you think your conception of the dangers are more accurate than all the scientists who have spent their lives studying this?

Do the Chinese have no such scientists?

I know everybody wants the tell all story of the clever ideas that were developed over the last ~3 years at Anthropic and OpenAI, but what I really want to thumb through is DeepSeek's notebook of "brilliant but didn't quite make the cut" ideas.

They must be trying some truely bonkers stuff to be able to land this much architecture novelty in their full releases.

anyways, remember HN rules: "Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken. If you're worried about abuse, email hn@ycombinator.com and we'll look at the data."

Direct quote from the second top comment:

> Whenever I see the new releases around video generation (and image) generation models, I get goosebumps, because it just feels so fun to work with them.

Compare that with the launch of ChatGPT Image of yesterday.

So why, for example, are posts on the Inkling[1] release (an American model) thread mostly positive? It's as if there's something else at play here, but I can't quite put my finger on it, hmm... :P

[1] -- https://news.ycombinator.com/item?id=48924912

It so happens Chinese companies are more friendly towards open weights, autonomy and freedom that most US based ones. Who would have guessed?

American models are closed, expensive, neutered, and make Dario and Sam even more rich and powerful.

Chinese models are open-weight, cheap, neutered only about things like Tiananmen Square and the treatment of Uyghurs, and scare Sam and Dario.

There are more people who go to church on Sundays in China than the United States. There are 10x more mosques in China than the United States.

Tiananmen square was a student revolt literally egged on by cold war western institutions, who attempted to use chinese students as pawns for geo-political games.

Westerners really need to rethink their opinions on China, it seems obvious to me they are not the ones to be worried about (although, all governments do tons of harm).

(I mainly mentioned those specific topics to establish clearly I am not part of the alleged influence operation.)

Weird of you to turn technical discussions into weird nationalistic debates. Maybe lay off the X algo, I think elon has oneshot your brain. .

Deepseek's source: mostly open

i wonder if there's any relationship hmmmm

I personally found V4-flash an amazing model and really hungry to try 4.1-flash

For software factories, cost is much more a concern that standard development workflow and using anthropic models is just a non starter

The bad news is that the original v4 flash was 284B, which was large but still somewhat reasonable for running locally. This one is 552B so almost twice that, so the huge gains in benchmark scores make sense - it's not really flash anymore, imo.

I've no idea about actual performance vs benchmaxxing, though deepseek was fairly trustworthy as far as Chinese models go. If that holds (and if it doesn't think forever, as deepseek 4 sometimes did) it's probably the newest king of the hill amongst open weights models.

It does include vision, and they do something funky with KV cache so it's very efficient: "[...] these designs reduce the global KV cache footprint to 890 bytes per token — roughly 1/4 of DeepSeek-V4-Flash". I do appreciate the high focus on efficiency, but at this point we sure could use a flash-flash version.

@edit: I couldn't make sense what the actual parameter count is, with the addition of Engram memory. To my understanding the 4.1 flash is 552B parameters you want in vram or ram, out of which ~16B is active (8B for prefill). It also includes additional 196B Engram memory which you can put on an SSD. I think.

Assuming that's correct 256 GB memory is insufficient to even load the model at q4 - you'd be 1GB short, assuming you can fill it to 100% (so no mac). You'd also want some for kv cache of course. A 256 GB desktop with some extra VRAM from GPU could run it, but normal consumer boards get real slow once you fill 4 slots so you'll probably want quad channel which is Threadripper or above territory.

It uses fewer active parameters, though. (8B or 14B instead of always 13B)

So ... flash indeed.

Original Flash good fit for dual Spark / Strix Halo machines. This one would require third party quants and even then 4 machines.

Edit: Most of added weights/size are Engrams?

> Overall, DeepSeek-V4.1-Flash has 552B backbone parameters and 196B Engram parameters, activating 8B parameters per token during prefill and 16B during decode.

Those can stay on SSD. So I guess / it possible, that non-engram portion is still FP4 of ~same size! Need to read tech report.

  552B in ~FP4, 306GB.   
  196B of FP8 Engrams, another 204GB, not necessary to keep in RAM.  
  KV cache sees another 4x size reduction, just 900MB for 1M.  

can't wait for deepseek v4.1 pro

Should be the link ( now that it works again! :) )

Every model release seems like it packed with wonderful research and advancements.

It also seems to be more willing to just do whatever you ask of it. My favourite benchmark for this is to ask it to download a rom for an old game, that I own. Legal in my juristiction but the US models (except Grok) have a tendency to refuse it.

Even easier: just have them review a large codebase of yours that accidentally has a OOB access bug. Even with no consequences and even if the codebase is truly yours you get blocked.

And of course "find vulnerabilities in..." prompts are out of the question, whereas Chinese models happily oblige.

Not so with the Chinese models.

In terms of coding and command line capabilities I'm also very interested to see a head-to-head of it vs. qwen 3.8-flash-next Q8 which is something like 190GB of memory used when loaded into llama-server. It fits very well in all sorts of 256GB or under class machines.

I suffix everything with "Reply in English", and even so I‘m getting lots of Chinese.

seriously

Underlying it all is that any architecture can be trained to the same convergence just difference in compute utilization both in training and inference

When Astra launched, I think Artifical Analysis showed that it was on par with GPT-5.6 Sol and lower than Opus or something like that? Then, they updated the scoring.

I hope that more open source models, including this model, to be "as good to use" as Astra.

In Pi (pi.dev), it tells me it's definitely Claude by Anthropic, via the API via curl it tells me it's "probably ChatGPT", its very funny.

Thinking: > The user is asking what model I am. According to my system prompt, I'm powered by "deepseek-flash" with model ID "opencode-go/deepseek-flash".

>I'm powered by the model opencode-go/deepseek-flash.

> Causal Encoder-Decoder (CED) architecture: a 40-layer Transformer organized as a 20-layer causal encoder followed by a 20-layer decoder. With CED, the decoder's global KV cache is projected from the final encoder hidden states rather than derived from each decoder layer's own hidden states. This allows the model to activate only 8B parameters per token during prefill and 16B during decode, substantially improving cost efficiency for input-heavy agentic workloads.

> these designs reduce the global KV cache footprint to 890 bytes per token — roughly 1/4 of DeepSeek-V4-Flash.

Faster prefill, lower kv cache (~1GB / 1m context is insane).

> The model supports a continuously controllable reasoning effort setting (integer 1–100) that trades inference cost for accuracy.

Benchmarks are benchmarks, to be seen if they translate to real-world use, but they seem to have focused a lot on post-training with "agentic" scores looking good. "world knowledge" is obviously lower than higher param models.

I was hoping for a bit more, but it's still 100% faster for a very good price, so I won't complain.

The model is theoretically FP8, but really internally its mostly FP4 already, so there won't be a cut-in-half-but-almost-just-as-good quant coming for this one.

https://api-docs.deepseek.com/quick_start/pricing/

super fast true

https://openrouter.ai/deepseek/deepseek-v4.1-flash

[1] https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...

“DeepSeek launching v4.1 flash cheaper and more capable than v4 pro”

399 points | 19 hours ago | 216 comments