I've been using it extensively since the release and the best summary I can give is that it's good enough to use it for (almost) everything and cheap enough that the cost are irrelevant. I'm running it in Oh My Pi with a second instance running as "advisor" and even with 5-6 active sessions (effectively 12 streams) I'm struggling to spend more than 5 bucks per day.
OpenCode Go even has double limits temporarily so for 10 USD you effectively get 140 USD of tokens to spend. It would impress me if someone could burn that amount with "normal" usage. Even when running multiple sessions.
I have a Claude Max subscription but I've barely touched it, it just feels like a step back to have to think about limits and usage even though the models are stronger.
The beauty of intelligence at this cost (even if it's not SOTA) is that it opens a whole bunch of new use cases. Test failure in CI? Have the bot automatically propose a fix, its cheap enough that you can discard it w/h issues. Test coverage too low? Auto generate tests on CI for every pull-requests! Monitoring server logs, continuous security audits and investigating every received exception now becomes possible.
I'm thinking about having it automatically filter and re-rank my social media feeds so I can steer the algorithm instead of the other way around.
Perhaps other people (with enormous budgets) were already doing all of the above but for us this is a really exciting release!
Dax (from Opencode) has tweeted that they can replicate or beat the price with rented GPUs. Deepseeks secret sauce is the incredibly cheap caching (magnitude cheaper than other providers).
vLLM has recently released a similar approach. It's not as effective as what DeepSeek does but still an interesting development.
I have no doubt that in due time other providers will match or perhaps even beat the current DeepSeek prices.
As someone who recently tried it on some blackwell cards, it's possible to match the prices especially the input can be even cheaper and output can match the costs so you can easily build a net 20-30% margin business even at current GPU prices.
The entire issue is caching, I tried to write some custom to dump to disk kv-caching using some ideas from their papers and my experience with snapshots and vm checkpoint systems, I must say they must have really squeezed that lemon it's hard.
Atleast me with Sol couldn't figure it out over a couple days, a few hours each day, which isn't much but I did feel a bit stuck with existing solutions and felt like I might have to write something from scratch. But if you are willing to put in the effort into the infra I do think it's doable. But it will be really hard to pull it off.
My congrats to anyone who manages to pull it off, they might be able to kill off most AI labs. Assuming they can find the compute, Deepseek really has killed all models for me other than Sol/Fable/Opus/K3 tier stuff.
One read is 1) they're getting a lot of traffic for Flash, 2) they've said they're updating Pro soon and expect that to lead to a traffic spike for Pro, but 3) that would leave them overloaded, so 4) they're going to raise prices to avoid it.
It's interesting that most open models adding 1M context did it in a way that reduces KV cache size (though DeepSeek was the most aggressive, using compressed attention on all layers), but only a couple providers turned it into a discount on cache reads.
> Deepseeks secret sauce is the incredibly cheap caching (magnitude cheaper than other providers).
Can anyone working at one of the main US labs (Google, OpenAI, Anthropic) comment on WTF they haven't even tried MLA - despite the obvious massive advantages?
I know enough to know they aren't completely incompetent. So there must be a quite good reason.
But it remains a mystery to me.
DeepSeek's MLA is like almost 2 years old at this time. They've got thousands of people working on this stuff. They clearly have the ability to at least try it...
Eh, what are you guys even talking about? Deepseek is not cheapest provider as is, and it's MIT. So deepseek making it more expensive to use is just nonsense, they can only change their own pricing. It's the beauty of MIT license and open weights. If anything, these models are some of the safest in the world to use if you worry about a rug pull.
Yes, there is warning, but also there are many providers on OpenRouter[0], hosting open weight model with similar pricing. The question is Will they go up as well?
If you're on the DeepSeek Platform, you'd see this:
"We plan to raise the overall pricing for DeepSeek API services in the near future, with a significant increase expected. Please plan your usage accordingly. The specific pricing plan will be subject to official notice."
> I'm thinking about having it automatically filter and re-rank my social media feeds so I can steer the algorithm instead of the other way around.
I hadn't really thought about this but AI may well be the technology that disrupts and ultimately destroys social media.
The value proposition of something like FB or IG is, as we know, the network effect. The platform gets to extract value from user generated content. I believe that users should own the platform, a bit like the Wikimedia Foundation, because they're the ones that create value. Federation is a popular belief on HN and I've come to believe that's simply the wrong solution to the right problem.
Anyway, how these social media companies make money is by optimizing the feed for engagement. People know it too so you see people trying to build an audience by rage baiting. And then more time spent equals more advertising revenue.
But what happens when the AI can simply slurp all the posts and then filter and rank them? It destroys the engagement and advertising model. And I'm not opposed to that, honestly. It may be on eof the few good thing sto come out of AI.
DeepSeek is my cheap and cheerful Chinese model of choice for API use. Has been for a while, but now it's Flash instead of Pro. Even cheaper, and now better then Pro. I feel like most of the major Chinese models are benchmaxxed, they have weird quirks every time I use them (Qwen 3.8 Max doesn't check its work and leaves stuff broken, doesn't write tests unless prompted, etc., Kimi ends up being quite expensive and rarely better than GPT Sol or Opus 5), while DeepSeek models seem to be generally as good as the benchmarks indicate: Not the best, but stronger across the board than any model within an order of magnitude of its price.
It's not frontier, but it's far past what we had at the beginning of the year. It's very usable. I get great instruction compliance, tool calling, and with a trivial workflows flow it has very good long-running performance as well.
Kimi K3 was an interesting model only a month ago, and now we're looking at the same performance for 1/20th of the price. Wild how fast this is advancing.
In my use, DeepSeek v4 Flash (which replaced the quite excellent MiniMax M3) lags behind GLM 5.2 & Muse Spark 1.2 (let alone Kimi K3). Also, K3 is a much bigger multi-modal model, while Flash is text-only and likely optimised for coding tasks.
Yep, and the v4 flash final is about 2.5x slower than preview making it no longer a fast model, in fact slower than Luna and bigger models in many cases.
Spark is actually the interesting one imo. It's significantly better, also significantly faster. If you are ok with letting Meta soak up your data (which DS does too) it's also the same price.
I strongly recommend trying this for programming tasks.
It is strong (not Fable strong though) with a much better “persona” than Opus, and very different blindspots. If you flip between Claude and this you will find both catch the mistakes of the other before they get out of control.
On balance I actually prefer DeepSeek for programming now, because of the way it talks.
This also reflects my experience and should put to bed the distillation rumours. This model feels nothing like the Claude models, including tone and blindspots.
I love DeepSeek V4 Flash since the pre-0731, now even more. It is the first model that is truly too cheap to meter.
But I find it having a pretty significant problem with tool calling - no idea why, but tool calling with it is SLOW. As long as the model is reasoning, all good. But give it a bunch of tools and it becomes extremely slow.
Tell your PjM who should tell your PgM who should tell your PdM, all the PMs...
Maybe if "the business" sees it is true of LLMs, they might believe it's true of giving better context to engineers up front then giving them time to think and prototype (thinking tokens are an answer prototype).
I’ve been refreshing hacker news constantly for a week now waiting for v4 pro, after they stated it would follow «soon». I have learnt «soon» is a matter of definition.
I'm still not sure, there's a promo going on now, but generally Go gives $60 of API credit and right now it might be $120 with deepseek. But $20/month OpenAI subscription I believe gives you many hundreds of API-equivalent usage? I've heard $100/month giving many thousands API-equivalent per month.
They did recently announce they're increasing prices though (got a mail yesterday I think), so not sure this analysis showing it as price outlier will last
This latest DeepSeek is almost at the "too cheap to meter" level. That's going to be a larger unlock than models like Fable/Mythos that are way too expensive to justify, IMO.
It's an excellent metric, the amount of applications not viable now due to cost/latency/throughput is vastly bigger than the amount of current use cases. Even current ones do benefit, e.g. it's a great executor subagent.
Price is confounded by VC subsidies, economies of scale, and inference optimizations. I think a more interesting chart would be ARC AGI vs forwards pass flops or ARC AGI vs training tokens. Of course we don't have those numbers for the closed source models or even some of the open weight ones.
weak argument. deepseek v4 flash is open weight, you can easily find other providers with competitive price with Deepseek (except for input caching), some even half as cheap.
OpenCode Go even has double limits temporarily so for 10 USD you effectively get 140 USD of tokens to spend. It would impress me if someone could burn that amount with "normal" usage. Even when running multiple sessions.
I have a Claude Max subscription but I've barely touched it, it just feels like a step back to have to think about limits and usage even though the models are stronger.
The beauty of intelligence at this cost (even if it's not SOTA) is that it opens a whole bunch of new use cases. Test failure in CI? Have the bot automatically propose a fix, its cheap enough that you can discard it w/h issues. Test coverage too low? Auto generate tests on CI for every pull-requests! Monitoring server logs, continuous security audits and investigating every received exception now becomes possible.
I'm thinking about having it automatically filter and re-rank my social media feeds so I can steer the algorithm instead of the other way around.
Perhaps other people (with enormous budgets) were already doing all of the above but for us this is a really exciting release!
vLLM has recently released a similar approach. It's not as effective as what DeepSeek does but still an interesting development.
I have no doubt that in due time other providers will match or perhaps even beat the current DeepSeek prices.
The entire issue is caching, I tried to write some custom to dump to disk kv-caching using some ideas from their papers and my experience with snapshots and vm checkpoint systems, I must say they must have really squeezed that lemon it's hard.
Atleast me with Sol couldn't figure it out over a couple days, a few hours each day, which isn't much but I did feel a bit stuck with existing solutions and felt like I might have to write something from scratch. But if you are willing to put in the effort into the infra I do think it's doable. But it will be really hard to pull it off.
My congrats to anyone who manages to pull it off, they might be able to kill off most AI labs. Assuming they can find the compute, Deepseek really has killed all models for me other than Sol/Fable/Opus/K3 tier stuff.
It's interesting that most open models adding 1M context did it in a way that reduces KV cache size (though DeepSeek was the most aggressive, using compressed attention on all layers), but only a couple providers turned it into a discount on cache reads.
Can anyone working at one of the main US labs (Google, OpenAI, Anthropic) comment on WTF they haven't even tried MLA - despite the obvious massive advantages?
I know enough to know they aren't completely incompetent. So there must be a quite good reason.
But it remains a mystery to me.
DeepSeek's MLA is like almost 2 years old at this time. They've got thousands of people working on this stuff. They clearly have the ability to at least try it...
The big US labs are opaque and don't publish much of any technical details anymore. We don't know what they are or aren't doing, honestly.
Deepseek charges $0.0028 per cache read on Openrouter. The next cheapest is $0.018.
That's a massive difference and quickly adds up on coding sessions (which often hit 95%+ cached tokens).
This adds disk as a tier in the HBM → CPU → Disk KV cache hierarchy.
There's also a cluster of related KV-offload FS PRs: #49225 (read/write batching, still open) and #49152 (batch store/load in C, merged Jul 28).
It's hard to say if these are similar to the approach DeepSeek takes but they definitely seem very interesting.
[0] https://openrouter.ai/deepseek/deepseek-v4-flash-0731#provid...
"We plan to raise the overall pricing for DeepSeek API services in the near future, with a significant increase expected. Please plan your usage accordingly. The specific pricing plan will be subject to official notice."
And, probably 99.99% of people using LLM probably don't even need SOTA anyway.
I hadn't really thought about this but AI may well be the technology that disrupts and ultimately destroys social media.
The value proposition of something like FB or IG is, as we know, the network effect. The platform gets to extract value from user generated content. I believe that users should own the platform, a bit like the Wikimedia Foundation, because they're the ones that create value. Federation is a popular belief on HN and I've come to believe that's simply the wrong solution to the right problem.
Anyway, how these social media companies make money is by optimizing the feed for engagement. People know it too so you see people trying to build an audience by rage baiting. And then more time spent equals more advertising revenue.
But what happens when the AI can simply slurp all the posts and then filter and rank them? It destroys the engagement and advertising model. And I'm not opposed to that, honestly. It may be on eof the few good thing sto come out of AI.
Terrible use-case.
Spark is actually the interesting one imo. It's significantly better, also significantly faster. If you are ok with letting Meta soak up your data (which DS does too) it's also the same price.
it's still $3/$15 for all providers on openrouter
because of some Kimi license
https://openrouter.ai/moonshotai/kimi-k3#providers
Uptime looks crap, though.
It is strong (not Fable strong though) with a much better “persona” than Opus, and very different blindspots. If you flip between Claude and this you will find both catch the mistakes of the other before they get out of control.
On balance I actually prefer DeepSeek for programming now, because of the way it talks.
[0] https://taylor.town/silver-landmines
When I see dramatic leaps like this, it tells me that the important hacks haven't yet been discovered.
But I find it having a pretty significant problem with tool calling - no idea why, but tool calling with it is SLOW. As long as the model is reasoning, all good. But give it a bunch of tools and it becomes extremely slow.
Am I the only one experiencing this?
Tell your PjM who should tell your PgM who should tell your PdM, all the PMs...
Maybe if "the business" sees it is true of LLMs, they might believe it's true of giving better context to engineers up front then giving them time to think and prototype (thinking tokens are an answer prototype).
https://reddit.com/r/DeepSeek is where the fellow F5ers are at.
https://news.ycombinator.com/item?id=49198661
https://x.com/thdxr/status/2085377844515922210
promising!
ARC-AGI II:
- GPT-5.2 (medium) %26.7 ($0.759)
- DSV4-Flash (max) %61.4 ($0.04)
That's irrelevant when you use $/task as the metric, which the OP does use.
What secret sauce do they have?
pair it with codewhale, 50 agents, 200 MB of ram.
Imagine if they had GPU resources of western labs.
SV companies get way too comfortable when they have enough in the bank to stay running more than three months.
Energy and intelligence are good too, sure.
With the exception of cache costs, all providers have similar input/output costs.