41–50 of 478 posts
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DeepSeek V4 Flash 0731 is an open-weights model which means price is determined by competition/invisible hand of the marketplace: https://openrouter.ai/deepseek/deepseek-v4-flash-0731 With the exception of cache costs, all providers have similar input/output costs.
Not counting the cost of making the model, which is subsidized by… someone? The chinese gov i think?
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 fo…
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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…
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.
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).
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.
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But DeepSeek now has a warning they’re going to sharply increase their API pricing sometime in the future.
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…
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.
Price is not a good meter. Active parameters per token are. Joule would be even better.
Energy and intelligence are good too, sure.
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. What secret sauce do they have?
pair it with codewhale, 50 agents, 200 MB of ram.
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 fo…
But DeepSeek now has a warning they’re going to sharply increase their API pricing sometime in the future.
Earlier quoted context omitted.
But DeepSeek now has a warning they’re going to sharply increase their API pricing sometime in the future.
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…
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...
Earlier quoted context omitted.
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…
any link to this caching tech?
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.