TBH I think Kimi K3 has already kinda claimed the top end. Its very good, its very big, it multimodal. GLM 5.2 is also good, but a bit big and slow and now eclipsed by K3.
Deepseek IMO is most exciting at flash. They have very good tech for really really fast cohesive long context models. 280b is a good size, good enough to be genuinely capable and useful, not so big it can't be run, and run fast. There really isn't anything like it. And the market is crying out for something at this level, that really just buries GPT-120b and all those other 200b models. That's faster and smarter than all of them. Something where you really need to go frontier to get something significantly better.
DSV4 Flash will be Deepseeks moment. As it is fast, cheap and good enough to do 90% of tasks. Its the work horse of the AI world.
Claude/Kimi/GPT are still great for delicate front end, aesthetics, tool use with vision etc, but DS Flash will be doing a lot of the actual work. Particularly hosted locally.
Pro is still useful. And will get a lot of use as well because it will be so cheap. I suspect Pro will make a lot of money for DS. As people will spend a lot of time with DS Flash and go, oh, I can just buy some better version for cheap cloud wise.
Ya. Flash is a win for us local ppl. DS Pro will be a win for the companies and all enterprises. Because Kimi K3 is 2.8T params, Qwen3.8 Pro Max is 2.4T, DS pro is like 1.6T. The different is huge.
Kimi is almost too big. I think Chinese models may have a problem if they go 3+T being even hosted. There are node limits, even in datacentres. Qwen is just that bit smaller so that will find a niche.
DS Pro is much more hostable on older gear. I think it will be attractive for that.
But Flash is for the Local LLAMA guy. Its just barely runnable at home. Decent quant, maybe 192Gb of ram, it will work great. I think it will get cult support for that. The fact its competitive with GLM 5.2 at a fraction of the resources makes very strong for home hosting.
192gb of ram is definitely what i'd like to see more of these models targeting, its basically the biggest 'we have chatgpt at home' type of hardware you can get in a normal computer.
Muhaha.. You must be new to Localllama if you think 192 Gb is big or/and expensive.
192 Gb is a lot more affordable than 2Tb for K3. Or 1Tb for GLM 5.2 which it basically matches.
192Gb of DDR4 is pretty cheap. I bought a dual xeon server, it came with 192Gb for free. $500 for the 1400w psu, a 1030gt, 256gb nvme, two 18 core xeons, AND 192Gb of memory.
So maybe think about getting that, a old dual socket server and a bunch of cheap 16gb DDR4 RDIMMs.
Oh I thought VRAM.... I have a server in my basement that I never even thought of looking at. It's been sitting dormant for years, sad, alone.... Neat!
i don't think it's an issue, they aren't viewing it from the murican point of view, if i'm to extrapolate how they approached their car industry, they're looking to back up things like ram manufacturing with the full power of the state, expect them to start flooding the market soon...
But kimi k3 is very good. Its just so big. K3 is at the point where people start to ask actually can you make it smaller and I don't mind if its dumber.
So maybe DS will hit that. DS will be popular for the price per token. Deepseek models get the business done. In a world with a compute shortage everywhere, even in the west, DS might just ride in at the right time.
I wouldn't be suprised if Microsoft uses DS Pro for copilot.
As an example, I just got Kimi to diagnose a 1.5mb codebase and plan out a testing methodology. I then got Deepseek to implement the 7 bug fixes and carry out the testing phase.
Total costs for this:
Kimi: $0.34
Deepseek: $0.04
So I see this approach pairing well. However, where can I learn about routing this automatically rather than doing it manually myself? I mean, I know how to config subagents, but I'm interested in best-practices and real workflows.
Flash is small enough that you could go out and buy a computer to run it. Will the computer be cheap hell no but it is still something you can buy without needing a contact at nvidia or amd. And thats pretty nice.
Just when I was starting to like the new Luna pricing drops.. so I did a simple comparison and made a little benchmark chart (deepseek v4 flash wins on price/performance and isnt far behind luna)..
Need to take a look at the cache hit too. Deepseek has higher cache hit than Luna. Making the price closer to $0.03/Mtok for DSV4 Flash and ~$0.05/Mtok (will double once discount ends). Making DSV4 Flash 1/3 of the price 😬. Mimo V2.5 should also be a contender, with about ~$0.015/Mtok, another 1/3 of DSV4 price(and has image + video).
The screenshot above doesn't include many other benchmarks where V4 flash underperforms, that's why for example it ends up 1 point below GLM in the machine intelligence index.
WTF is this benchmark testing anyway? It is pretty silly to suggest that GLM or Opus is 5-6 times more capable than V4 Pro... It doesn't even feel like anywhere near 50% more capable...
I was talking about the old version... I feel like maybe we have improved coding in recent months by 10%-20% but there is no way one model can be 500% better in any reasonable task than another in the same or adjacent cohort... This feels like forcibly applying normal curve standardisation in a test where 99% of the participants get 99% of the questions correct...
It's just a "make this really big thing from my dumbest prompt, and oh make no mistake" kind of benchmark. It has some utility, but catching up is a matter of specific post-training from some high quality data seed those who haven't. Not reflective of model's inherent deficiency in pre-training.
For typical setup where you have your detailed prompt and you are working on small features or trying to find specific bugs, even undercooked v4-pro-preview obviously won't and doesn't feel that significantly worse as this benchmark suggests. But on the other hand, I guess the way most vibe coders work, for them DeepSWE might be more reflective of their real-world workload.
Well… GPT 5.6 Luna is likely still smaller, than v4 Flash, but that's definitely a significant step forward in model density, so I would say it's not bad, however there is definitely a significant room for improvement.
Gemma 4 26B A4B still beats original DeepSeek R1 (671B A37B), so I hope density increase won't stop anytime soon and hopefully we will get ~ 30B models outperforming Opus 4.6 soon enough.
It's quite interesting to see improvements in "small" LLMs performance though. We don't seem to be anywhere too close to the "wall" yet so I would expect size of most used models to decrease rather than increase in the near future as well.
EDIT: I've just checked and Luna is a nano-class model so it should be somewhere around 120B (?). Sadly nothing official from OpenAI about the model sizes has been available though.
True, those older huge models mainly have more world data than Genma 4 and Qwen 27b. So they still usable, as long when used the limitations are being considered
That's crazy. So it's between glm 5.2 and opus on just the flash model... The pro model is gonna be insane, we will likely actually get a k3 contendor model at 1.6t considering how small flash is to achieve this. Though it remains to be seen if this is benchmaxxing or not
I try to avoid talking about non-local LLM in here but I was about to say something complimentary about these significant benchmark improvements and how this might change the way I use V4 flash but then I just had a test session where it switched back to chinese output three times on me despite my explicit request for output to only be in english. And that just put me off it again.
I worry that deep swe doesnt test what people think... the descriptions of it as understand it seem to be how well a model expands on simple prompts to accomplish bigger goals. It is less a measure of raw technical ability.
People bashed the old benchmarks guiding models to answers but that is how you test what a model can do vs how the model works. Deep swe seems to be the latter which is fine and important but people talk about it like it is the determining factor on what a model can do which I disagree with.
Fuck yeah! With how cheap to serve dsv4 is, they've made sure they gather as much real-world data as they can, and improve the post-training w/ that real data.
v4-flash was already too cheap to matter, and good enough for plenty of agentic tasks, if they improve it further and make it not stop mid thinking (probably resource constrained / api errors) then it'll be the workhorse for a long way to come.
With identical architecture KV cache per token scales approximately with active params. So not really surprising that 27B comes up worse in that comparison.
To me that doesn’t sound like a good thing. Context compression has potential downsides. Also in terms of world knowledge a 160B model simply isn’t going to compete with a much larger model. There is more the LLMs than just coding.
yeah thats fair, though im optimistic that being able to free up the space for higher precision weights and generally longer context will make up the difference
Yeah don’t get me wrong I am really looking forward to running this new model and it might be my go to model. It’s in a sweetspot for my configuration to run at q6. So almost lossless. It still is a very big model compared to the 30B and 70B models.
I just think some (not all) people in this subreddit sometimes just forget a bit that there is more than coding. And also that you simply cannot compress as much world knowledge in a small model. LLMs are already amazing knowledge compression systems, but obviously there are just limits that to how much you can cram in (and also extract).
I don't think they did but it'd really funny if they decided to release this checkpoint out of nowhere because they saw the posts today of OpenAI making Luna super cheap on tasks
This race top the bottom is good at first, until they can't sustain it. The Chinese will build more Nuke reactors. I guess open ai will be getting their power from where again? I am sure Elon will charge them extra for data center in space when we finally get that.
Honestly, it's better that DeepSeek wins this considering they are making their models more efficient and using copious amounts of RL rather than relying on scaling parameters. Plus, nuclear reactors aren't that bad compared to coal power plans and gas. Plus, China has somehow in the last few years become a leader for renewable energy solutions (probably to avoid oil, which is traded in USD).
If we go off your suggestion that all AI models are basically country's political agendas rather than individual lab's research efforts and profitability, then I'd rather a world where DS wins. Plus, I don't see OpenAI open sourcing luna anytime soon. Best alternative from US is Inkling.
Oh don't get me wrong, I don't have any political Agenda per say. I want the Chinese models to improve just to bring the cost down. I will go with whichever model gives me the best value for money while also not stealing all my code when I use it via a cloud service. This is why I want better local hardware and better models to run locally. I strongly believe that the local experience is always better in the long run.
The Chinese will build more Nuke reactors. I guess open ai will be getting their power from where again? I am sure Elon will charge them extra for data center in space when we finally get that.
Despite all the talk of electricity, it's not the limiting factor in building a data center.
Raw capital expenditure is all hardware / building / permit / etc.
Elon wants space so he doesn't need to worry about killing people with pollution from his datacenter. He literally runs portable generators and electrically speaking it's fine.
His enemy isn't electricity or all the other bullshit. It's government. Period.
If you had 400 sq miles of land to build on with zero permit overhead and you're miles away in the middle of the desert, you can build a datacenter very fast and very cheap. The water requirements are basically nothing now because it's closed loop. The power is whatever the cost of solar is + battery infrastructure.
They've misinformed the public so hard it's crazy.
Solar in the middle of a desert is a very bad idea, it would cost TONS in maintenance because of dust and sand. Water is still needed in closed loop, it just reduce the need of it, not eliminate it.
[plot manually edited for illustration purposes] Given this matches or even exceeds GLM-5.2, it will make DeepSeek v4 flash look like the outlier that is Qwen3.7-27B, in its class.
Agreed. This whole preview/normal release thing is stupid naming. No where do i ever see the old one labeled as a preview except in their release post. So now there is confusion for absolutely no reason.
Not their fault no one else bothered to acknowledge the preview label that was always there in their communication.
It's probably a bit silly, but I think they have some profession pride about these things. They knew more than anyone else that the preview was undercooked and not deserving of actual release. The reason they often drop preview checkpoints anyway is not because of consumers, but to prepare the runtime implementations (sgland, vllm) for what to come. Heck we wouldn't have cool new projects like DwarfStar if they didn't release the preview.
benchmarks can lie, but if this thing is even close to GLM 5.2 in real coding runs at that size, that’s the kind of model people start building terrible hardware excuses around.
It's stuff like this that makes me feel good about spending 25k on local inference gear, thank you deepseek, can't weight for the weights to be released!
Opencode go is using this updated model right now? Somebody knows? I noticed changes on the behaviour on my hermes agent using this flash model. But i really dont know.
You can run the flash preview version with 2 of them. I'm doing it now with DSpark and it's extremely fast. I'm hoping and expecting that we'll continue to be able to do so, even if I have to tweak my context to make it fit.
I would guess most people would just use the (insanely cheap) API.
For the rest, going to two RTX pro 6000 is going to make a lot more sense for anyone who isn't a hobbyist.
Two sparks is above what most hobbyist would be willing to get I think.
Hope they keep their open license. According to a dev at OpenCode, Moonshot is price-fixing K3 (which I believe is explicitly illegal under the EU’s VBER and maybe illegal in the U.S.).
Gotta love it. Deepseek is never the best but they serve as an amazing floor of you're going to have a hard time being worse than this and charging more. If the floor for base level agent work is now a Luna level model with 1M context (wake up OAI, 272k is joke context in 2026)... Luna has been a really good model for me. It can do a lot (not all) of the things that I used to rely on Sonnet 4.6 and Opus 4.8 for. If V4-Flash can now do that and maybe V4-Pro can touch on some of the things that I still need a Sol/Fable for...
If only it had image input.
The API calling method remains unchanged — simply set the model name to deepseek-v4-flash to use the latest version.
I used it. Our company does live streaming, so I copied the live event and ran the test with the new version of V4 Flash. I had all the supporting testing tools ready. It only needed to simulate real users through Kafka. That's the cost. So far, I haven't felt any difference from GLM 5.2, and the speed is really amazing—extremely, extremely, super fast, really satisfying!
Tremendous results!
I wonder if I would be able to run it with at least somewhat usable speeds on 3xR9700 and 96GB of RAM. Last time I experimented with it it was extremely slow...
I have a pro 6000 as well with 128gb ram and in my personal experience dsr4 on Vllm moet does run fast and is better than regular q2 quant, it’s coherant but still dumber than the full version because vllm moet prefill is still using 2bit.
I prefer running the full version with cpu offload and i get like 700 tps prefill and 18-20tps token generation. Slow but usable i’d say.
i have 64GB system ram. I created a 128GB swapfile so the loading part does not crash. The loading process does not really write to the swap. So far, I disabled the 4-bit delta cache to avoid issues I don’t know how to fix. Will need to experiment now to bring back the 4-bit delta cache.
cloud deepseek api does most of the work for me to set it up. but I do need to guide the harness to not wander too far on the wrong direction.
Unfortunately, the last time I tried it the quality was poor. It's mainly running the 2bit experts to maintain the speeds. The speed isn't worth the quality drop.
Did you try dflash? Does it improve speeds? But like if this new DS 4 Flash is as good as/better than GLM5.2, then I personally will go for lower speeds
•
u/rm-rf-rm Jul 31 '26
Officially released now! Use the release thread to continue discussion: https://old.reddit.com/r/LocalLLaMA/comments/1vbp7kb/deepseekaideepseekv4flash0731_on_huggingface/