I don't understand the hype because I'm out of the loop. Is the only advantage the lower hardware requirements, thus cost? Is there something I'm missing?
OpenAI o1 and Deepseek r1 have similar performance (o1 is a bit better at reasoning though you can see r1’s though process which you could argue trumps the competition). OpenAI o1 api cost: $60/million output tokens. Deepseek r1 api cost: $2.19/million output tokens.
So basically I can get a ~lifetime supply of r1 tokens for… $20?
~lifespan = 2.27 billion seconds
r1 api can spit out 63 tokens per second
~143 billion lifetime tokens.
~$313 million for a lifetime supply of tokens.
$313 thousand not million which seems reasonable enough.
It isn't really hype for me.
For my use, it is better than $20 a month o1 and being able to see the chain of thought is absolutely incredible.
I have learned as much this week from seeing the chain of thought as I have from what it actually outputs.
It's also open source
open weights you mean. People confuse open weights with open source.
Yeah it's a lot more efficient, it's also a very advanced model that answers questions in a multi-step way, like OpenAI-O1, it performs extremely well.
Love that Microsoft is getting behind this actually good model
They’re selling shovels
This is bizarre to see the entire AI hype cycle speedrun all over on just DeepSeek.
I'm trying to square the excitement over DeepSeek with its good -but not dominant- performance in evals.
Previously choosing a top tier AI model tied you to what that provider wanted to do with hosting the model long term and the pricing they wanted to charge for it. Now you can get the same model anywhere with GPU, hosted or not, for minimal cost overhead to what it takes to run the model itself. You're also free to tune, retrain, or otherwise mess with the model as you see fit without needing approval.
The excitement is probably a bit much but it's not just about the eval results themselves but the baggaged attached with them.
For me the excitement is that around the o3 announcement I had a feeling like we were heading to an OpenAI / Sam Altman controlled dystopia. This resets that - you can run the model yourself, you can modify it yourself, it's essentially on par with the best public models, and it gives hope that the smaller players have a fighting chance going forward. They also published their innovations bringing back some of the feeling of open science that used to be in ML research but which mostly went away.
Google models are already in the lead in many areas in capability and cost, so I never felt like OpenAI was dominant. OpenAI was first to make a splash, but ChatGPT is in a ~5 way tie in terms of what it can do.
AFAIK o1 is hidden behind an expensive subscription (iirc $20/mo and still rate-limited), it might as well just not exist for most users (since R1 is free, provided service availability).
Also R1 (and its distilled models) expose their CoT & web interface has a websearch option too.
With the 14b distilled models, I found multiple math-related prompts where it gives the right answers almost immediately but then wastes 10 minutes making self-verification mistakes (e.g. "Write Python3 code that computes the modular inverse of a mod 2^32")
IMO more anti hype for openAI who might be dominant, but are they $3500 per task (O3 high) dominant, or $200 per month dominant.
This is it.
I was planning on spending the $200 for a month but had been thinking of prompts to try it out.
DeepSeek already answered them all for free so I am not just going to light $200 on fire for fun.
Right, but Google already had models that were as good at much lower cost.
Which models at what cost? IMO Deepseek websearch potential to challenge Google search moat also makes Google particularly vunerable, because it dramatically evaporates advantages of 100s of billions of hardware. Not to imply Google does not maintain advantages, but it gap just went from insurmountable to many actors can potentially build AI search to rival Google on shoe string budget. Certainly on sovereign budget.
Reliably serving planet-scale inference is a whole different ballgame.
It's going to be an increasingly irrelevant game when models make regional scale, i.e. country/sovereign scale inference attainable. Countries that couldn't even role out domestic search pre accessible models that displaces search likely soon can.
For 2024-level tech I agree. The future is multimodal and a lot of processing power will be needed for that.
It can only be deployed in FIRA.
Looks like DeepSeek R1 is a Microsoft shady move against Sam ;]
What is shady about it? Should a company the size of Microsoft stand by instead?
This is exciting.
Is there a free version of DeepSeek R1 that's completely US based, so we're not sending data to China? I guess you can use this to deploy it, but I'm asking for an application that would be safer to use if you're concerned about Chinese influence.
You can run it yourself: https://workos.com/blog/how-to-run-deepseek-r1-locally
Distilled R1 models != R1
You can run the full R1 (671B variant) locally as well so long as you have the hardware for it.
`ollama run deepseek-r1:671b`
will do that
Yeah I mean, most users won't. Sorry if I got on the defensive, saw a bit too many posts on social media claiming you could run the model on your consumer-grade GPU.
competition is beneficial for all of us, this is great
How's the pricing as compared to official API or openrouter providers?
That was quick
Why wouldn’t it be?
What is the price on Azure?
What is the price?
Free in preview, at least in my personal subscription (there was a disclaimer saying that it was in preview, no guarantees of response times, etc.)
(I work at Microsoft but am not on the clock as I write this, and keep my personal projects separate)