These general intelligence/capability benchmarks don't apply to your specific task. If you are coding, you want just benchmarks for coding. I don't see performance+price at this benchmark for coding. When you look at just coding, DeepSeek is Pareto optimal for a good portion of coding benchmarks.
For example here, although even this is very flawed because it only benchmarks max/xhigh effort: https://artificialanalysis.ai/agents/coding-agents
Closed models might be better generalists because they don't have that much constraint on size and come from well funded labs that can afford to better train for generalization. If a model's actual goal is to be open then smaller is better.
Yes, 100% agree. Without more details on how they got their data, it's very hard to understand why they don't value DS Flash 4.1 as much as pretty much every other analysis I've seen (and my own experience).
That's to be expected since all the proprietary models were just released. Give it six months and the Chinese labs will distill from the proprietary models like they did last time.
The article itself goes against this intuition, open models have taken up as much as 40-50% of frontier for capable/affordable. Right now is very much a low point. The fact there's a lot of churn as new models are released is not unexpected, I think.
If required, so far I'm not impressed with newest proprietary. i have a claude sub mostly rotting at this point, so i come back from time to time until the year long sub expires, but at this stage i will not renew and see no point in doing so.
Despite its simplicity, price-performance is not the only metric; this obsession is a typical consequence of extreme overextension of the Friedman doctrine, and it is why so much software and hardware has been qualitatively shitty even when it matters that it’s not. That something can be measured doesn’t automatically make it preferable as a decision-making basis over intangibles.
It’s pretty obvious that local AI has a higher TCO if you look only at money. But there are other things to be considered.
True but let's think about whether this actually matters in practice going forward, given that today, Deepseek/any OS-27B variant can do all the things that most devs need it to.
Let's scale this out 3 years into the future:
- Opus 6.7 costs an arm and a leg to run for 2 prompts
- Astra 7.89 literally requires you to buy-now-pay-later because it's so expensive
- As a bootstrapped dev, your only option is serving local Qwen-4.04-27B that runs on your old RTX (that you bought in 2024)
Sure, it's worse performing. But if you can't afford the best ones in the first place, what's so bad about having a "pretty decent" model that you run essentially free (only paying electricity) if it does what you need it to?
... and if we're all in the permanent underclass anyway, what does it matter that your local model is dumber than the SOTAs? At least you have one! Some people who can't afford GPUs still have to type emails by hand.
Even as the article points out, this fluctuates constantly. In a few months, we're guaranteed to get a new batch of open models, and the balance will shift again.
What's more important is that open models can be inspected, they can be customized, and you don't have to worry that a company will decide that you're not allowed to use them for stuff like reverse engineering things or do security work.
That's why open models, and especially ones you can run locally will always be important. When you're renting closed models, you're basically a serf who has no say in how the model can be used.
Closed models might be better generalists because they don't have that much constraint on size and come from well funded labs that can afford to better train for generalization. If a model's actual goal is to be open then smaller is better.
Let’s say I want to run qwen 27B or flash next on a machine I own, off solar panels that are also sunk cost.
How did they price the marginal cost of a token? Depreciation on the box + reverse billing opportunity cost with the power company?
It’s pretty obvious that local AI has a higher TCO if you look only at money. But there are other things to be considered.
Let's scale this out 3 years into the future: - Opus 6.7 costs an arm and a leg to run for 2 prompts - Astra 7.89 literally requires you to buy-now-pay-later because it's so expensive - As a bootstrapped dev, your only option is serving local Qwen-4.04-27B that runs on your old RTX (that you bought in 2024)
Sure, it's worse performing. But if you can't afford the best ones in the first place, what's so bad about having a "pretty decent" model that you run essentially free (only paying electricity) if it does what you need it to?
... and if we're all in the permanent underclass anyway, what does it matter that your local model is dumber than the SOTAs? At least you have one! Some people who can't afford GPUs still have to type emails by hand.
(jk, this is actually a cool dataset though ^)
I'm running full swarming on a 90$ subscription lol
What's more important is that open models can be inspected, they can be customized, and you don't have to worry that a company will decide that you're not allowed to use them for stuff like reverse engineering things or do security work.
That's why open models, and especially ones you can run locally will always be important. When you're renting closed models, you're basically a serf who has no say in how the model can be used.
Open-weights excel primarily at coding.