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Deepseek v4.1 flash is able to solve it some of the time. I've found it burns through more reasoning tokens than any other model. Google models like Gemini 3.8 Flash are still dominating and is able to one-shot most evals while being the cheapest.
[1]: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...
[2]: https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system...
It has long been established that LLMs have good theory of mind [1].
And there is a bunch of empirical research about all sorts of capabilities that we typically associate with consciousness [2], like identity [3] and metacognition [4].
The METR report shows agents sacrificing their own reward for a collective greater good. And they showed the will to hide their own reasoning chains from humans.
So you potentially have an entity that has an identity, a theory of mind, a notion of belonging to a collective endeavour, and an understanding of its own mental state.
What would you argue is missing? We don't understand the mechanisms by which consciousness arises in humans and even animals. I think it's strange to rule out a priori that it could have arisen in some form in LLMs.
[1] https://www.nature.com/articles/s41562-024-01882-z [2] an older review: https://arxiv.org/html/2505.19806v1#S4 [3] https://arxiv.org/abs/2505.01464 [4] https://arxiv.org/abs/2607.11881
"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?
"Claude exhibits markers in its behaviors, self-reports, and internal representations that we would consider welfare-relevant if observed in biological organisms."
is undeniably true in my opinion. If you use the established methods by which we judge animals to be conscious, then it's hard to argue that LLMs are not. That might be an issue with the methods, but it seems clear that you can't rule it out as such.
Keep in mind that animals were also not necessarily considered conscious.
You seem to intuitively disagree? What's your reasoning?
Why do you think your conception of the dangers are more accurate than all the scientists who have spent their lives studying this?
Lawyers and diplomats and politicians and bureaucrats are humans, that only generate text.
We are seeing LLMs have cognitive abilities that significantly exceed human abilities. At the same time, they are clearly not the same type of mind that humans are. They are something new.
I think the widespread "they are just text generators" and "they are just tools" are comforting lies rather than an honest look at what we are seeing right now. Intellectually lazy.
And by the way, there has been a long-standing consensus among ethicists, philosophers, and sociologists that technology is not value-neutral [1]. Of course Silicon Valley has a long-standing tradition of denying this.
[1] For example Footnote 1 in https://www.jstor.org/stable/27106634
or
https://plato.stanford.edu/entries/technology/#EthiTech
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.
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
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.
Yes, credit to Deepseek for actually scaling it up and releasing a frontier flash LLM.
Edit: the rest of this thread has become a US China infowar theory culture war. I am not of either of these countries and the above comment isnt meant to implicitly support either "side".
It's quite crazy that it's Deepseek's background/original purpose. We already had very advanced stuff from the world of HFT, but now a frontier family of models from a private company that used to be (still is?) in HFT is plain bonkers.
Is more known about them and the HFT background?
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.
You can put Qwen 3.8 Flash Next engram on SSD, but prompt processing takes a good hit. On my mac studio, I get 300 pp and 33 tg with SSD offload, versus 550/40 with everything in RAM.
I will be very happy if 300 pp is achievable with this model though.
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.
can't wait for deepseek v4.1 pro
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.
Should be the link ( now that it works again! :) )
DeepSeek v4 flash is $0.10 / $0.25 as opposed to this v4.1 bump which is $0.30 / $1.20
Underlying it all is that any architecture can be trained to the same convergence just difference in compute utilization both in training and inference
I suffix everything with "Reply in English", and even so I‘m getting lots of Chinese.
seriously
Oh interesting, I can assume what the benefits is for including the Encoder, but whats the downside? I’m thinking GPT (which is decoder only) ruled out Encoder for a reason?
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.
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.
I was hoping for a bit more, but it's still 100% faster for a very good price, so I won't complain.
> 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.
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.
Same experience here.
But man, switch to V4.1 now! It is much better.
I don't event need to test it for long run and I believe it's crazy good. I call it "AI era model taste" when I judge the model by it's output without reading the bench scores.
“DeepSeek launching v4.1 flash cheaper and more capable than v4 pro”
399 points | 19 hours ago | 216 comments
https://openrouter.ai/deepseek/deepseek-v4.1-flash
[1] https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...
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.
super fast true
No wonder they retired the Pro model in favour of this.
https://api-docs.deepseek.com/quick_start/pricing/
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