https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...
Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.
https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...
Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.
I don't think we need to compare models to Opus anymore. Opus users don't care about other models, as they're convinced Opus will be better forever. And non-Opus users don't want the expense, lock-in or limits.
As a non-Opus user, I'll continue to use the cheapest fastest models that get my job done, which (for me anyway) is still MiniMax M2.5. I occasionally try a newer, more expensive model, and I get the same results. I have a feeling we might all be getting swindled by the whole AI industry with benchmarks that just make it look like everything's improving.
Which model's best depends on how you use it. There's a huge difference in behaviour between Claude and GPT and other models which makes some poor substitutes for others in certain use cases. I think the GPT models are a bad substitute for Claude ones for tasks such as pair-programming (where you want to see the CoT and have immediate responses) and writing code that you actually want to read and edit yourself, as opposed to just letting GPT run in the background to produce working code that you won't inspect. Yes, GPT 5.4 is cheap and brilliant but very black-box and often very slow IME. GPT-5.4 still seems to behave the same as 5.1, which includes problems like: doesn't show useful thoughts, can think for half an hour, says "Preparing the patch now" then thinks for another 20 min, gives no impression of what it's doing, reads microscopic parts of source files and misses context, will do anything to pass the tests including patching libraries...
Agree with your assessment, I think after models reached around Opus 4.5 level, its been almost indistinguishable for most tasks. Intelligence has been commoditized, what's important now is the workflows, prompting, and context management. And that is unique to each model.
Same for me. There are tasks when I want the smartest model. But for a whole lot of tasks I now default to Sonnet, or go with cheaper models like GLM, Kimi, Qwen. DeepSeek hasn't been in the mix for a while because their previous model had started lagging, but will definitely test this one again.
The tricky part is that the "number of tokens to good result" does absolutely vary, and you need a decent harness to make it work without too much manual intervention, so figuring out which model is most cost-effective for which tasks is becoming increasingly hard, but several are cost-effective enough.
This is not true for some cases e.g. there are stark differences in the correctness of answers in certain type of case work.
Is Opus nerfed somehow in Copilot? Ive tried it numerous times, it has never reallt woved me. They seem to have awfully small context windows, but still. Its mostly their reasoning which has been off
Codex is just so much better, or the genera GPT models.
Opus just got killed in Copilot. I always found it great, FWIW.
https://github.blog/news-insights/company-news/changes-to-gi...
I found Opus 4.7 to be actually worse than Opus 4.6 for my use case
Substantially worse at following instructions and overoptimized for maximizing token usage
This resonates with me a lot.
I do some stuff with gemini flash and Aider, but mostly because I want to avoid locking myself into a walled garden of models, UIs and company
What do you run these on? I've gotten comfortable with Claude but if folks are getting Opus performance for cheaper I'll switch.
You can just use Claude Code with a few env vars, most of these providers offer an Anthropic compatible API
Try Charm Crush first, it's a native binary. If it's unbearable, try opencode, just with the knowledge your system will probably be pwned soon since it's JS + NPM + vibe coding + some of the most insufferable devs in the industry behind that product.
If you're feeling frisky, Zed has a decent agent harness and a very good editor.
I've downloaded Zed but haven't used it much, maybe this is my sign. Thanks!
actually this is not the reason - the harness is significantly better. There is no comparable harness to Claude Code with skills, etc.
Opencode was getting there, but it seems the founders lost interest. Pi could be it, but its very focused on OpenClaw. Even Codex cli doesnt have all of it.
which harness works well with Deepseek v4 ?
What's the issue with OC? I tried it a bit over 2 months ago, when I was still on Claude API, and it actually liked more that CC (i.e. the right sidebar with the plan and a tendency at asking less "security" questions that CC). Why is it so bad nowadays?
eh idk. until yesterday opus was the one that got spatial reasoning right (had to do some head pose stuff, neither glm 5.1 nor codex 5.3 could "get" it) and codex 5.3 was my champion at making UX work.
So while I agree mixed model is the way to go, opus is still my workhorse.
I find gemini pretty good ob spatial reasoning.
Yeah but gemini has a hard time discussing about solutions it just jump to implementation which is great if it gets it right and not so great if it goes down the wrong path.
Not saying it is better or worse, but the way I perpersonally prefer is to design in chat, to make sure all unknown unknown are addressed
I don’t know what people are doing but Minimax produced 16 bugreports which of 15 was false positives (literally a mistake).
In contrast ChatGPT 5.3 and also Opus has a 90% rate at least on this same project. (Embedded)
All other tests were the same. What are you doing with these models?
How does it compare to Opus 4.7? I've been immersed in 4.7 all week participating in the Anthropic Opus 4.7 hackathon and it's pretty impressive even if it's ravenous from a token perspective compared to 4.6
The thing is, it doesnt need to beat 4.7. it just needs to do somewhat well against it.
This is free... as in you can download it, run it on your systems and finetune it to be the way you want it to be.
> you can download it, run it on your systems
In theory, sure, but as other have pointed out you need to spend half a million on GPUs just to get enough VRAM to fit a single instance of the model. And you’d better make sure your use case makes full 24/7 use of all that rapidly-depreciating hardware you just spent all your money on, otherwise your actual cost per token will be much higher than you think.
In practice you will get better value from just buying tokens from a third party whose business is hosting open weight models as efficiently as possible and who make full use of their hardware. Even with the small margin they charge on top you will still come out ahead.
There are a lot of companies who would gladly drop half a million on a GPU to have private inference that Anthropic or OpenAI can’t use to steal their data.
And that GPU wouldn’t run one instance, the models are highly parallelizable. It would likely support 10-15 users at once, if a company oversubscribed 10:1 that GPU supports ~100 seats. Amortized over a couple years the costs are competitive.
> There are a lot of companies who would gladly drop half a million on a GPU to have private inference that Anthropic or OpenAI can’t use to steal their data.
Obviously, and certainly companies do run their own models because they place some value on data sovereignty for regulatory or compliance or other reasons. (Although the framing that Anthropic or OpenAI might "steal their data" is a bit alarmist - plenty of companies, including some with _highly_ sensitive data, have contracts with Anthropic or OpenAI that say they can't train future models on the data they send them and are perfectly happy to send data to Claude. You may think they're stupid to do that, but that's just your opinion.)
> the models are highly parallelizable. It would likely support 10-15 users at once.
Yes, I know that; I understand LLM internals pretty well. One instance of the model in the sense of one set of weights loaded across X number of GPUs; of course you can then run batch inference on those weights, up to the limits of GPU bandwidth and compute.
But are those 100 users you have on your own GPUs usings the GPUs evenly across the 24 hours of the day, or are they only using them during 9-5 in some timezone? If so, you're leaving your expensive hardware idle for 2/3 of the day and the third party providers hosting open weight models will still beat you on costs, even without getting into other factors like they bought their GPUs cheaper than you did. Do the math if you don't believe me.
There's stuff like SOC controls and enterprise contracts with enforceable penalties if clauses are breached. ZDR is a thing.
The most significant value of open source models come from being able to fine-tune; with a good dataset and limited scope; a finetune can be crazily worth it.
Sure, but that’s an incredibly short term viewpoint.
Do you think a lot of people have “systems” to run a 1.6T model?
To me, the important thing isn't that I can run it, it's that I can pay someone else to run it. I'm finding Opus 4.7 seems to be weirdly broken compared to 4.6, it just doesn't understand my code, breaks it whenever I ask it to do anything.
Now, at the moment, i can still use 4.6 but eventually Anthropic are going to remove it, and when it's gone it will be gone forever. I'm planning on trying Deepseek v4, because even if it's not quite as good, I know that it will be available forever, I'll always be able to find someone to run it.
Yep, it's wild how little emphasis is there on control and replicability in these posts.
Already these models are useful for a myriad of use cases. It's really not that important if a model can 1-shot a particular problem or draw a cuter pelican on a bike. Past a degree of quality, process and reliability are so much more important for anything other than complete hands-off usage, which in business it's not something you're really going to do.
The fact that my tool may be gone tomorrow, and this actually has happened before, with no guarantees of a proper substitute... that's a lot more of a concern than a point extra in some benchmark.
No, but businesses do. Being able to run quality LLMs without your business, or business's private information, being held at the mercy of another corp has a lot of value.
What type of system is needed to self host this? How much would it cost?
Depends how many users you have and what is "production grade" for you but like 500k gets you a 8x B200 machine.
Depends on fast you want it to be. I’m guessing a couple of $10k mac studio boxes could run it, but probably not fast enough to enjoy using it.
One GB200 NVL72 from Nvidia would do it. $2-3 million, or so. If you're a corporation, say Walmart or PayPal, that's not out of the question.
If you want to go budget corporate, 7 x H200 is just barely going to run it, but all in, $300k ought to do it.
How many users can you serve with that?
For the H200, between 150-700. The GB200 gets you something like 2-10k users.
$20K worth of RTX 6000 Blackwell cards should let you run the Flash version of the model.
Not really - on prem llm hosting is extremely labor and capital intensive
But can be, and is, done. I work for a bootstrapped startup that hosts a DeepSeek v3 retrain on our own GPUs. We are highly profitable. We're certainly not the only ones in the space, as I'm personally aware of several other startups hosting their own GLM or DeepSeek models.
Why a retrain? What are you using the model for?
Completely agree, not suggesting it needs ot just genuinely curious. Love that it can be run locally though. Open source LLMs punching back pretty hard against proprietary ones in the cloud lately in terms of performance.
What's the hardware cost to running it?
Probably like 100 USD/hour
I was curious, and some [intrepid soul](https://wavespeed.ai/blog/posts/deepseek-v4-gpu-vram-require...) did an analysis. Assuming you do everything perfectly and take full advantage of the model's MoE sparsity, it would take:
- To run at full precision: "16–24 H100s", giving us ~$400-600k upfront, or $8-12/h from [us-east-1](https://intuitionlabs.ai/articles/h100-rental-prices-cloud-c...).
- To run with "heavy quantization" (16 bits -> 8): "8xH100", giving us $200K upfront and $4/h.
- To run truly "locally"--i.e. in a house instead of a data center--you'd need four 4090s, one of the most powerful consumer GPUs available. Even that would clock in around $15k for the cards alone and ~$0.22/h for the electricity (in the US).
Truly an insane industry. This is a good reminder of why datacenter capex from since 2023 has eclipsed the Manhattan Project, the Apollo program, and the US interstate system combined...
All these number are peanuts to a mid sized company. A place I worked at used to spend a couple million just for a support contract on a Netapp.
10 years from now that hardware will be on eBay for any geek with a couple thousand dollars and enough power to run it.
That article is a total hallucination.
"671B total / 37B active"
"Full precision (BF16)"
And they claim they ran this non-existent model on vLLM and SGLang over a month and a half ago.
It's clickbait keyword slop filled in with V3 specs. Most of the web is slop like this now. Sigh.
"if you have to ask..."
... if you have 800 GB of VRAM free.
I remember reading about some new frameworks have been coming out to allow Macs to stream weights of huge models live from fast SSDs and produce quality output, albeit slowly. Apart from that...good luck finding that much available VRAM haha
Tbh I was more productive with 4.6 than ever before and if AI progress locks in permanently at 4.6 tier, I’d be pretty happy
It is more than good enough and has effectively caught up with Opus 4.6 and GPT 5.4 according to the benchmarks.
It's about 2 months behind GPT 5.5 and Opus 4.7.
As long as it is cheap to run for the hosting providers and it is frontier level, it is a very competitive model and impressive against the others. I give it 2 years maximum for consumer hardware to run models that are 500B - 800B quantized on their machines.
It should be obvious now why Anthropic really doesn't want you to run local models on your machine.
Vibes > Benchmarks. And it's all so task-specific. Gemini 3 has scored very well in benchmarks for very long but is poor at agentic usecases. A lot of people prefering Opus 4.6 to 4.7 for coding despite benchmarks, much more than I've seen before (4.5->4.6, 4->4.5).
Doesn't mean Deepseek v4 isn't great, just benchmarks alone aren't enough to tell.
With the ability of the Qwen3.6 27B, I think in 2 years consumers will be running models of this capability on current hardware.
What's going to change in 2 years that would allow users to run 500B-800B parameter models on consumer hardware?
I think its just an estimate
But the question remains
No, the Deepseek V4 paper itself says that DS-V4-Pro-Max is close to Opus 4.5 in their staff evaluations, not better than 4.6:
> In our internal evaluation, DeepSeek-V4-Pro-Max outperforms Claude Sonnet 4.5 and approaches the level of Opus 4.5.
Is it honestly better than Opus 4.6 or just benchmaxxed? Have you done any coding with an agent harness using it?
If its coding abilities are better than Claude Code with Opus 4.6 then I will definitely be switching to this model.
Apparently glm5.1 and qwen coder latest is as good as opus 4.6 on benchmarks. So I tried both seriously for a week (glm Pro using CC) and qwen using qwen companion. Thought I could save $80 a month. Unfortunately after 2 days I had switched back to Max. The speed (slower on both although qwen is much faster) and errors (stupid layout mistakes, inserting 2 footers then refusing to remove one, not seeing obvious problems in screenshots & major f-ups of functionality), not being able to view URLs properly, etc. I'll give deepseek a go but I suspect it will be similar. The model is only half the story. Also been testing gpt5.4 with codex and it is very almost as good as CC... better on long running tasks running in background. Not keen on ChatGPT codex 'personality' so will stick to CC for the most part.
Their Chinese announcement says that, based on internal employee testing, it is not as good as Opus 4.6 Thinking, but is slightly better than Opus 4.6 without Thinking enabled.
I appreciate this, makes me trust it more than benchmarks.
In case people wonder where the announcement is (you can easily translate it via browser if you don't read Chinese): https://mp.weixin.qq.com/s/8bxXqS2R8Fx5-1TLDBiEDg
It's still a "preview" version atm.
That's super interesting, isn't Deepseek in China banned from using Anthropic models? Yet here they're comparing it in terms of internal employee testing.
> That's super interesting, isn't Deepseek in China banned from using Anthropic models? Yet here they're comparing it in terms of internal employee testing.
I don't see why Deepseek would care to respect Anthropic's ToS, even if just to pretend. It's not like Anthropic could file and win a lawsuit in China, nor would the US likely ban Deepseek. And even if the US gov would've considered it, Anthropic is on their shitlist.
They use VPN to access. Even Google Deepmind uses Anthropic. There was a fight within Google as to why only DeepMind is allowed to Claude while rest of the Google can't.
Who uses Opus without thinking though...?
> (better than Opus 4.6)
There we go again :) It seems we have a release each day claiming that. What's weird is that even deepseek doesn't claim it's better than opus w/ thinking. No idea why you'd say that but anyway.
Dsv3 was a good model. Not benchmaxxed at all, it was pretty stable where it was. Did well on tasks that were ood for benchmarks, even if it was behind SotA.
This seems to be similar. Behind SotA, but not by much, and at a much lower price. The big one is being served (by ds themselves now, more providers will come and we'll see the median price) at 1.74$ in / 3.48$ out / 0.14$ cache. Really cheap for what it offers.
The small one is at 0.14$ in / 0.28$ out / 0.028$ cache, which is pretty much "too cheap to matter". This will be what people can run realistically "at home", and should be a contender for things like haiku/gemini-flash, if it can deliver at those levels.
Anthropic fans would claim God itself is behind Opus by 3-6 months and then willingly be abused by Boris and one of his gaslighting tweets.
LMAO
> Anthropic fans ...
I have no idea why you'd think that, but this is straight from their announcement here (https://mp.weixin.qq.com/s/8bxXqS2R8Fx5-1TLDBiEDg):
> According to evaluation feedback, its user experience is better than Sonnet 4.5, and its delivery quality is close to Opus 4.6's non-thinking mode, but there is still a certain gap compared to Opus 4.6's thinking mode.
This is the model creators saying it, not me.
For the curious, I did some napkin math on their posted benchmarks and it racks up 20.1 percentage point difference across the 20 metrics where both were scored, for an average improvement of about 2% (non-pp). I really can't decide if that's mind blowing or boring?
Claude4.6 was almost 10pp better at at answering questions from long contexts ("corpuses" in CorpusQA and "multiround conversations" in MRCR), while DSv4 was a staggering 14pp better at one math challenge (IMOAnswerBench) and 12pp better at basic Q&A (SimpleQA-Verified).
FWIW it's also like 10x cheaper.
The dragon awakes yet again!
There appears a flight of dragons without heads. Good fortune.
That's literally what the I Ching calls "good fortune."
Competition, when no single dragon monopolizes the sky, brings fortune for all.
Pop?