Hilarious sentence: "DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks." (emphasis mine)
It's a bit refreshing to read updates from an AI lab without bullshit doomerism and overwhelming sense of self-importance from shit like OpenAI and Claude.
It passed my threshold for not caring to try new models back with DS4, I use it for literally everything. Announcements of new models in the series always feel curious, because it doesn't really feel like there's much left to fix that would improve on what's already there for any task I know of.
It's a bit like obsessing over patch releases of some software after a major upgrade already solved the original problem long ago.
I have quite the opposite experience: I can do any concrete coding change with any model, but the intelligence limit just doesn't go through more complex debugging examples.
And then when I try a smart model, like Astra medium and it solves it (I had a firmware bug in my new laptop where I couldn't charge through USB-C) on the first try (altough with a repo already having lots of information about the hardware/firmware/debugging programs/traces), I realize how much time I'm losing on the few examples that the less intelligent models are just not able to debug and solve.
Also they can work in completely new domains, like Astra and Opus 5.5 starting to be able to use something like Blender. Currently they still often do horrible topology and basic UV mistakes, and the animations are really bad, but who knows where they’ll be in 5 years.
I don’t think these are completely new domains in training. Like I’ve taken up offers to help code up AI training material just to see what they’re up to and get a sneak peek at how the sausage is made, no idea who these front companies are working for of course, but they pay for all kinds of fringe cases and Blender wouldn’t even be considered fringe in what I saw at the beginning of the year. (I was only there to observe, never submitted anything, so I didn’t betray you guys by helping train your replacements.)
Is your laptop something like framework which has open source firmware?
I(and many other people) have a similar issue with usb-c charging not working consistently. I've been meaning to try one of these new smart models to help me debug the issue.
Speed and price. You should try the 4.1 Flash on one of the fast providers. It's pretty crazy how fast this model works, and it's great for iterative software development because you can steer it fast to where you want to go.
Seems that V4.1 Flash is quite cheap to serve. Especially how it implements the cache, meaning the cache pricing can be very low depending on how you implement it in your inference engine.
Depends on your use case. But if you do software development with a harness such as oh-my-pi, your cache rates are probably somewhere above 98%. Coralbricks has free cache reads, meaning it's much cheaper than the Deepseek API rates.
Lithosai is more expensive especially in the ultra tier. It's if you want extremely snappy and fast responses, which is important e.g. for a personal assistant where every second counts.
If you generate a lot of output, you're better off with the Deepseek API rates.
You just search around, or heck, just ask Google for inference providers that are ZDR and whatever else you want. Just make sure to read their terms/privacy and review the security center. You will have to request access if you are wanting to get into the deeper details.
Outside of major players like Amazon/Google which have compatible offerings available, I believe together.ai, fireworks.ai, and deepinfra.com are, as best as I can tell, US-based (if that matters in your situation) SOC2/HIPPA/ZDR. Each of them offer serverless model hosting with caching, which is going to be the cheapest overall. Most of them also offer private AI deployment which is more expensive but your entirely own instance. Some of them more heavily quantize their models (deepinfra) so take that into your workload considerations. I included deepinfra because they tend to have some the new models sooner than others or have models the others don’t (like mimo). IMO, together > fireworks > deepinfra.
However, you’ll have to keep in mind that for true ZDR you give up caching which increases your inference costs, so it depends on how “Zero” you need the zero to be and on what timeframe. If you just don’t want your data being trained on, normal use is fine imo. If you are working on something actually sensitive (like, because of NDA agreements and such), then they might not be the right choice and you may be looking at more expensive offerings or building a machine.
One note: you have to pay attention to the source of the inference. Many (similar to open router) offer models that are pass-throughs to a providers API, but they are pretty clearly marked. Just make sure the model is marked as serverless when evaluating new ones. This is where a proxy comes in handy so you can prevent your LLM from accessing a different models from the specific ones you’ve vetted.
Above GLM 5.3, nice! That is a very capable model. I do wish that we will see some smaller "flash" models again though - running this one locally is tough (very costly).
I use Opus for planning, through API on medium thinking, it doesn't cost anything significant and the rest can be happily done with deepseek or glm flash. Using sol 6.1 for review loops at the end. All running on pi.
A very complex task tends to cost like 10$ max. It's comparable to a subscription with enormous flexibility.
Honestly, I don't know what people see in Sol 6.1. Opus 5.5 is so much more inteligent, as in proactive, avoiding stupid mistakes, where it contradicts itself, ignores the context. Sol 6.1 has a gold-fish memory, it's as annoying for me to work with as Opus 5 was.
Cant you run it on a pair of DGX spark? Sure it's expensive now, but at the initial retail price that was what, 8k in hardware? Not exactly unachievable
My experience running quantised models is that they look the same at first but are all noticeably worse once the context size gets beyond anything trivial.
I've tried both 2bpw and 3bpw quants for QWEN3.8 27b, and while i do notice a difference in terms of higher rates of getting stuck in loops, they are both reasonably usable.
Are they as good as opus 5.5? No.
Are they about on par with opus 4.6? Yes.
The long run tasks i run seem to be about the same level of quality, though there are obvious differences in model styles, token budgets, etc....
By next year we will likely see GLM 5.3 level performance in Qwen 4 MoE models that can run on most higher end consumer systems.
I don't think there is a reason to care about any of it, just get a few dollars on a few openrouter or similar platforms and just use the models for now, wait for cheaper models that are good enough for you to become available for your hardware.
Sure you might never see frontier performance on consumer hardware but do you really need that though? Does anyone? And if you ever do just get a 30-day sub for that work, and cancel it.
You can stick with Claude Code if you're already using it. DeepSeek has an Anthropic-compatible endpoint, so you just need to set ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic and ANTHROPIC_AUTH_TOKEN to your DeepSeek API key.
It costs money to run serious tests. We're way past the time when you could get an idea of the new model in a couple of tricky prompts. Now I need to compare a whole resulting project to get a clear view. If someone else proves the benchmarks don't hold up in reality, then it saves us further tests.
Presumably. The current opencode slug for the model is just 'deepseek-flash' suggesting they will just update that one going forward, not keeping versions in parallel.
BTW who would have thought China would be the champion of freedom and opensource in the AI arena, while US is fighting for corporate/government control.
There are different types of freedom and China's bet is that as long as they provide enough economic freedom the public will be ok with somewhat restricted social and political one.
Yeah, I had a somewhat odd conversation with a Chinese acquaintance last year when she got to talking about how America sees China. Her point was that, "How am I not free? I can do whatever I want, go where I want, buy what I want. I can talk to you and say whatever I want; nobody is here listening to me."
The fact that she can't call Xi "Winnie the Pooh" on Douyin wasn't something that occurred to her, or maybe that she was aware of at all. I didn't ask. I don't like to get into these discussions when I'm in China.
There is no such thing as “free speech” anywhere. It’s all a spectrum and the Americans in particular are indoctrinated. It’s a red herring to distract from the real social issues. People in America have some of the worst healthcare in the world. But hey at least we have “free speech”.
Fixing healthcare or economic policies is typically an act of congress type of thing - not even an amendment. Large popular support - and it can be fixed. Fixing freedom of speech or the ability to vote at all - once it is gone - usually takes a price payed in blood. Sometimes rivers of it.
I believe these are the last sets of super-high-end models available to public (open or closed).. these companies probably will now start to withhold top-of-the-line models for themselves, governments, and their super-high-paying partners only.
Here's How Distill APT Used McKinsey's Private Access To Top Anthropic Model To Help The Chinese Keep Up With The Us Top IA Labs Despite Access Restrictions
That hasn't been the way to bet. DeepSeek and the other Chinese firms show no signs of slowing down, so if OAI and Anthropic halt releases and "pace the frontier" they'll just fall behind. (And there's a chance that high-end AI will become fully commoditized, which suits DeepSeek just fine.)
Besides, Fable 5.5 is reportedly releasing within the next few days, probably on Tuesday...
OpenAI models don’t come close to competing with deepseek in cost quality ratio. I have a $20 plus plan and I run out of my 5h limit in literal minutes, every tick of the harness is like 10% of my 5h limit.
You can in contrast get far more done with $20 worth of deepseek api spend. No comparison.
OpenAI's models -- and even Google's Argon -- have particular niches where they shine. For GPT-6/6.1, it's 3D modeling; Astra is hands-down the best at making .step files, Blender files, etc. For Argon, it's natural language writing and everything that hooks up to Google, e.g. search and maps-based itinerary planning.
Anthropic and DeepSeek (+ GLM) are definitely solving coding, though!
Artificial Analysis recently released a dedicated 3D benchmark, where Astra lags behind both Opus and Sonnet 5.5 (and yes, open weight models seem to be quite far behind).
For me it's large files in Blender (in some cases >400MB), mostly, where Astra excels. I gave it a look, but I don't know if the benchmark encompasses that sort of thing.
.step files are simpler pieces of straightforward code, so it makes sense that Opus and Sonnet 5.5 do very well...
Hilarious sentence: "DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks." (emphasis mine)
Kinda shows their team is more engineers than marketers.
Strictly speaking, it just shows they aren't marketers. Bad copy doesn't tell us anything about their engineering skill.
Strictly speaking, they didn't say anything about their engineering skill
It's not a bad copy, or at least that's not what the person you responded to is insinuating.
Deepseep-v4.1-Flash-1017 is a minor update. A major update is something like Deepseek-v5-Flash.
But with that version number, it's not a "minor" update! It's a patchlevel update! -Engineer
It's not really contradictory, minor changes can have major effects.
So they meant a "small" update - "conservative", "inexpensive"...
An example when using a less frequent term actually reduces specificity.
That is how exponential functions work. Improve every aspect of a system with 1% and you may get easily 50% more performance.
Hoping it's minor relative to what's coming
It's about playing to two different audiences.
The CCP requires extensive certification for new major releases, to make sure a model doesn't do anything they don't want it to.
To get around this, Chinese labs only bump versions when they have to and usually claim it's a minor update.
Then on the other side, they're obviously trying to market to the tech crowd...
Source?
Drum Tower podcast (from the Economist, paywalled) about Chinese AI.
Would highly recommend it, it's IMO the only serious Western reporting about China.
> the only serious Western reporting about China.
as good as Gordon Chang.
I don't know who that is, but from a quick Google he doesn't exactly seem trustworthy?
Everything in China is strictly regulated but loosely enforced. Just take that as a grain of salt
It's a bit refreshing to read updates from an AI lab without bullshit doomerism and overwhelming sense of self-importance from shit like OpenAI and Claude.
Also, DS is an excellent model.
That's one small step for a man, one giant leap for mankind.
I'm at 1 trying to get to 10. I move to 2. Minor update. 100% improvement towards goal.
These are not mutually exclusive.
It passed my threshold for not caring to try new models back with DS4, I use it for literally everything. Announcements of new models in the series always feel curious, because it doesn't really feel like there's much left to fix that would improve on what's already there for any task I know of.
It's a bit like obsessing over patch releases of some software after a major upgrade already solved the original problem long ago.
definitely could use less thinking time and lower token usage, which is what this bump is focused on
I have quite the opposite experience: I can do any concrete coding change with any model, but the intelligence limit just doesn't go through more complex debugging examples.
And then when I try a smart model, like Astra medium and it solves it (I had a firmware bug in my new laptop where I couldn't charge through USB-C) on the first try (altough with a repo already having lots of information about the hardware/firmware/debugging programs/traces), I realize how much time I'm losing on the few examples that the less intelligent models are just not able to debug and solve.
Also they can work in completely new domains, like Astra and Opus 5.5 starting to be able to use something like Blender. Currently they still often do horrible topology and basic UV mistakes, and the animations are really bad, but who knows where they’ll be in 5 years.
5 years? Look at the leaps since last November. Since the Spring, even.
I don’t think these are completely new domains in training. Like I’ve taken up offers to help code up AI training material just to see what they’re up to and get a sneak peek at how the sausage is made, no idea who these front companies are working for of course, but they pay for all kinds of fringe cases and Blender wouldn’t even be considered fringe in what I saw at the beginning of the year. (I was only there to observe, never submitted anything, so I didn’t betray you guys by helping train your replacements.)
Very cool!
Is your laptop something like framework which has open source firmware?
I(and many other people) have a similar issue with usb-c charging not working consistently. I've been meaning to try one of these new smart models to help me debug the issue.
> improve on what's already there for any task I know of
What about "good governance"?
Does it have zero hallucinations? Does it reflect thoroughly on every single idea it uses, verifying its solidity?
Speed and price. You should try the 4.1 Flash on one of the fast providers. It's pretty crazy how fast this model works, and it's great for iterative software development because you can steer it fast to where you want to go.
https://www.lithosai.com/
https://www.coralbricks.ai/
How are these high-speed providers close to matching the API rates from deepseek?
Seems that V4.1 Flash is quite cheap to serve. Especially how it implements the cache, meaning the cache pricing can be very low depending on how you implement it in your inference engine.
Depends on your use case. But if you do software development with a harness such as oh-my-pi, your cache rates are probably somewhere above 98%. Coralbricks has free cache reads, meaning it's much cheaper than the Deepseek API rates.
Lithosai is more expensive especially in the ultra tier. It's if you want extremely snappy and fast responses, which is important e.g. for a personal assistant where every second counts.
If you generate a lot of output, you're better off with the Deepseek API rates.
Where does one even get decent ZDR provider? I'm wary of sending sensitive data but not at a scale where I would build my own machine.
Scaleway have DS4. I don't think they call it ZDR, but their framework is very strong https://www.scaleway.com/en/docs/generative-apis/reference-c...
You just search around, or heck, just ask Google for inference providers that are ZDR and whatever else you want. Just make sure to read their terms/privacy and review the security center. You will have to request access if you are wanting to get into the deeper details.
Outside of major players like Amazon/Google which have compatible offerings available, I believe together.ai, fireworks.ai, and deepinfra.com are, as best as I can tell, US-based (if that matters in your situation) SOC2/HIPPA/ZDR. Each of them offer serverless model hosting with caching, which is going to be the cheapest overall. Most of them also offer private AI deployment which is more expensive but your entirely own instance. Some of them more heavily quantize their models (deepinfra) so take that into your workload considerations. I included deepinfra because they tend to have some the new models sooner than others or have models the others don’t (like mimo). IMO, together > fireworks > deepinfra.
However, you’ll have to keep in mind that for true ZDR you give up caching which increases your inference costs, so it depends on how “Zero” you need the zero to be and on what timeframe. If you just don’t want your data being trained on, normal use is fine imo. If you are working on something actually sensitive (like, because of NDA agreements and such), then they might not be the right choice and you may be looking at more expensive offerings or building a machine.
One note: you have to pay attention to the source of the inference. Many (similar to open router) offer models that are pass-throughs to a providers API, but they are pretty clearly marked. Just make sure the model is marked as serverless when evaluating new ones. This is where a proxy comes in handy so you can prevent your LLM from accessing a different models from the specific ones you’ve vetted.
I like privatemode.ai for better than ZDR, encrypted attested hardware (in EU)
Above GLM 5.3, nice! That is a very capable model. I do wish that we will see some smaller "flash" models again though - running this one locally is tough (very costly).
For comparison, close to Opus 5. Far behind Sol 6.1 and Opus 5.5.
Probably a pretty decent model for 90% of the usecases!
I've been doing a lot of hobby work with GLM 5.3 Flash and I can count the amount of times that I need a stronger model on two hands.
I have access to unlimited tokens at works and Opus is great but also often overkill if you know exactly what you need.
I use Opus for planning, through API on medium thinking, it doesn't cost anything significant and the rest can be happily done with deepseek or glm flash. Using sol 6.1 for review loops at the end. All running on pi.
A very complex task tends to cost like 10$ max. It's comparable to a subscription with enormous flexibility.
It's a steal.
Honestly, I don't know what people see in Sol 6.1. Opus 5.5 is so much more inteligent, as in proactive, avoiding stupid mistakes, where it contradicts itself, ignores the context. Sol 6.1 has a gold-fish memory, it's as annoying for me to work with as Opus 5 was.
You can override the context length of Sol to 1M
Without this change I hated Sol and was burning tokens way faster due to all the forced compacting.
Since making this (obscure/hidden) change I enjoy Codex much more.
Cant you run it on a pair of DGX spark? Sure it's expensive now, but at the initial retail price that was what, 8k in hardware? Not exactly unachievable
No, this is not a small model. I believe 4x Sparks is possible but 4.1 is pretty big for a flash model.
MiMo 2.6 Flash is the most capable model you can currently run on 2x Sparks without resorting to a quantised version of something.
I meant quantized. You can get an EXL3 quantized version thats almost the same performance.
My experience running quantised models is that they look the same at first but are all noticeably worse once the context size gets beyond anything trivial.
I've tried both 2bpw and 3bpw quants for QWEN3.8 27b, and while i do notice a difference in terms of higher rates of getting stuck in loops, they are both reasonably usable.
Are they as good as opus 5.5? No.
Are they about on par with opus 4.6? Yes.
The long run tasks i run seem to be about the same level of quality, though there are obvious differences in model styles, token budgets, etc....
By next year we will likely see GLM 5.3 level performance in Qwen 4 MoE models that can run on most higher end consumer systems.
I don't think there is a reason to care about any of it, just get a few dollars on a few openrouter or similar platforms and just use the models for now, wait for cheaper models that are good enough for you to become available for your hardware.
Sure you might never see frontier performance on consumer hardware but do you really need that though? Does anyone? And if you ever do just get a 30-day sub for that work, and cancel it.
Very keen to try this out tonight. What coding harness do people like to use with these models?
You can stick with Claude Code if you're already using it. DeepSeek has an Anthropic-compatible endpoint, so you just need to set ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic and ANTHROPIC_AUTH_TOKEN to your DeepSeek API key.
Thank you deepseek, we are eating good today.
Looks very impressive especially considering the price and speed. Let's wait for AA to confirm how well their benchmarks hold up.
Why not just try it yourself?
It costs money to run serious tests. We're way past the time when you could get an idea of the new model in a couple of tricky prompts. Now I need to compare a whole resulting project to get a clear view. If someone else proves the benchmarks don't hold up in reality, then it saves us further tests.
You trust AA since the sol fiasco a month or two ago?
What did I miss?
I'd never heard of the BabyVision benchmark they do so well on. Pretty interesting stuff:
https://arxiv.org/abs/2601.06521
Is it coming to OpenCode et al?
and do we know which version runs on opencode at the moment (on the free and on the paid version), is there a way to find out?
The only way I'm aware of is to check their twitter feed. They moved to Deepseek v4.1 flash in September
sure, but which version are they running. they could be running an older revision
Presumably. The current opencode slug for the model is just 'deepseek-flash' suggesting they will just update that one going forward, not keeping versions in parallel.
It's better than GLM 5.3 and Kimi K3 and way cheaper.
that's honestly insane. it's like the go-to for when you don't want to use opus 5.5/sol 6.1
The go-to is a local model.
Limp-to, maybe.
Those are crazy numbers for a Flash model. Hopefully they can scale what they are doing to 4.1 Pro.
Another big win for opensource AI.
BTW who would have thought China would be the champion of freedom and opensource in the AI arena, while US is fighting for corporate/government control.
Well, at least mostly everyone outside the US.
Anyone that has observed China since the 90s.
There are different types of freedom and China's bet is that as long as they provide enough economic freedom the public will be ok with somewhat restricted social and political one.
Yeah, I had a somewhat odd conversation with a Chinese acquaintance last year when she got to talking about how America sees China. Her point was that, "How am I not free? I can do whatever I want, go where I want, buy what I want. I can talk to you and say whatever I want; nobody is here listening to me."
The fact that she can't call Xi "Winnie the Pooh" on Douyin wasn't something that occurred to her, or maybe that she was aware of at all. I didn't ask. I don't like to get into these discussions when I'm in China.
There is no such thing as “free speech” anywhere. It’s all a spectrum and the Americans in particular are indoctrinated. It’s a red herring to distract from the real social issues. People in America have some of the worst healthcare in the world. But hey at least we have “free speech”.
Fixing healthcare or economic policies is typically an act of congress type of thing - not even an amendment. Large popular support - and it can be fixed. Fixing freedom of speech or the ability to vote at all - once it is gone - usually takes a price payed in blood. Sometimes rivers of it.
That's an image China wants the rest of the world to believe. Towards inside it still fits the typical stereotype.
So this is the reason for the crash a few days ago.
What crash?
Ai/tech stock crash
Deepseek servers went unavailable for 5 hours. They compensated that by rewarding the previous week usage as credit though. Pretty nice.
beating K3 and GLM-5.3 is kinda insane
"a minor update" they say before showing off benchmarks that display a substantial improvement.
they must've been using this since 2-3 days when it became free again in OpenCode, it's noticeable smarter, beats GLM
We're in a crisis
Censored, no surprise
Those benchmarks can't possibly be real, right? Right? <insert padme 4-panel meme here>
At this rate the frontier labs will be Anthropic and DeepSeek, with OpenAI struggling to keep up.
I believe these are the last sets of super-high-end models available to public (open or closed).. these companies probably will now start to withhold top-of-the-line models for themselves, governments, and their super-high-paying partners only.
News from a near future:
Here's How Distill APT Used McKinsey's Private Access To Top Anthropic Model To Help The Chinese Keep Up With The Us Top IA Labs Despite Access Restrictions
We have the weights, we have the papers, nothing preventing development on this foundation.
That hasn't been the way to bet. DeepSeek and the other Chinese firms show no signs of slowing down, so if OAI and Anthropic halt releases and "pace the frontier" they'll just fall behind. (And there's a chance that high-end AI will become fully commoditized, which suits DeepSeek just fine.)
Besides, Fable 5.5 is reportedly releasing within the next few days, probably on Tuesday...
China just announced tightened export controls on AI models, for what it's worth.
idk, sol 6.1 is both cheap and capable. i've been using it exclusively, i don't need to care about usage, and it's really smart.
yes maybe opus 5.5 is stronger and flash is cheaper, but 6.1 strikes a good balance imo.
i have a feeling that Sol is just Astra low, but with lowest possible priority on the compute.
I believe it's Astra quantized. It was briefly named astra-lite.
OpenAI models don’t come close to competing with deepseek in cost quality ratio. I have a $20 plus plan and I run out of my 5h limit in literal minutes, every tick of the harness is like 10% of my 5h limit.
You can in contrast get far more done with $20 worth of deepseek api spend. No comparison.
OpenAI's models -- and even Google's Argon -- have particular niches where they shine. For GPT-6/6.1, it's 3D modeling; Astra is hands-down the best at making .step files, Blender files, etc. For Argon, it's natural language writing and everything that hooks up to Google, e.g. search and maps-based itinerary planning.
Anthropic and DeepSeek (+ GLM) are definitely solving coding, though!
They're all quite different tools.
Artificial Analysis recently released a dedicated 3D benchmark, where Astra lags behind both Opus and Sonnet 5.5 (and yes, open weight models seem to be quite far behind).
https://artificialanalysis.ai/articles/aa-3d
its unfortunate they didn't include deepseek in the initial batch of benchmarks
Interesting!
For me it's large files in Blender (in some cases >400MB), mostly, where Astra excels. I gave it a look, but I don't know if the benchmark encompasses that sort of thing.
.step files are simpler pieces of straightforward code, so it makes sense that Opus and Sonnet 5.5 do very well...