It is another thing the BigLabs accuse open weight models of benefiting from distillation & other techniques & essentially avoid higher training costs (which typically bleed into bills end users pay for inference).
Big labs ripped videos off YouTube without caring about the ToS, and grabbed as much published literature they could get their hands on, regardless of legality (Books3, The Pile). The goal of "democratizing human knowledge" by way of thinking machines is far too noble to worry about frivolities like copyright and authorial consent, they said. Until it was their output being exploited, and their earning potential threatened.
We just had years of US model providers arguing it was fine to rip off the world’s cultural output for their own profit, why should their work be treated any different?
True, but why would end users care about that? If anything, training on synthetic AI output is more ethical than on scraped human works (of course, not to say the Chinese labs aren't doing the latter)
Part of their model is that not everyone will use their entire quota each month. I don't think I will. I use under $1/day with deepseek v4 flash. We get $60 for the $10 sub.
I hardly notice DeepSeek being inferior to Claude Opus unless I have it working on tricky and under-defined problems. That is, I trust Opus to reason much better when it has the choice. Otherwise, IME DeepSeek is far cheaper and more effective for anything where the solution is even somewhat obvious.
Out of curiosity, what is your stack? And is this in a legacy project or new one?
I have tried using deep seek flash and pro but they make amateur mistakes. Sonnet level at best.
However v4 flash is absolutely amazing as a generalist model and it’s what we’re using on a product built on top of LLMs. I wish I could code with it but it’s not going to happen anytime soon
I've used it across many new projects as well as many legacy ones. It does make amateur mistakes so you can't leave it unsupervised for hours like I do with Claude, but it's so much cheaper that weeks of heavy usage haven't even cost me $10 yet. Only other downside IMO is that Pro is pretty slow, even compared to frontier models; only around 120t/s IIRC.
Yes I also noticed it is pretty slow, which sort of defeated the purpose of using it for me.
Usually I'm working on a large task, typically with Opus, while also having a bunch of smaller tasks in their own independent worktrees. Those still need supervision, but less. My goal was to get deepseek to drive the cost of those down, but it was too slow and unreliable...
They're leaving us in the dust on solar, while our current administration is still trying to put people in the ground to dig up more coal and die of black lung. https://en.wikipedia.org/wiki/Solar_power_in_China
It is another thing the BigLabs accuse open weight models of benefiting from distillation & other techniques & essentially avoid higher training costs (which typically bleed into bills end users pay for inference).
Ex A: https://www.anthropic.com/research/2028-ai-leadership
Ex B: https://www.reuters.com/world/china/openai-accuses-deepseek-...