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It's a race to the bottom. DeepSeek beats all others (single-shot), and it is ~50% cheaper than the cost of local electricity only. > DeepSeek V3.2 Reasoning 86.2% ~$0.002 API, single-shot > ATLAS V3 (pass@1-v(k=3)) 74.6% ~$0.004 Local electricity only, best-of-3 + repair pipeline
Can you explain what that means?
Earlier quoted context omitted.
Really intriguing set of techniques to improve accuracy by generating multiple solutions. Even with the work to predict the most likely solutions, it's not clear to me based on the description how this could all be done efficiently. Would definitely be really impressive if it pans out on real-world use cases. Will look to kick the tires on this if I can get some time.
Seems like the key insight is to train a small model that acts as a heuristic for embeddings that resemble quality code. I imagine a lot depends on how well this model is trained. And you could probably create specialized versions for different languages and domains. Another interesting approach could be to use this set up with a language like Clojure or Common Lisp which facilitates interactive development. If you c…
So it seems like it's a difficulty classifier for task descriptions written in English.
This is then used to score embeddings of Python code, which is a completely different distribution.
Presumably it's going to look at a simple solution, figure out it lands kinda close to simple problems in embedding space and pass it.
But none of this helps you solve harder problems, or distinguish between a simple solution which is wrong, and a more complex solution which is correct.
It's a race to the bottom. DeepSeek beats all others (single-shot), and it is ~50% cheaper than the cost of local electricity only. > DeepSeek V3.2 Reasoning 86.2% ~$0.002 API, single-shot > ATLAS V3 (pass@1-v(k=3)) 74.6% ~$0.004 Local electricity only, best-of-3 + repair pipeline
> cheaper than the cost of local electricity only. Can you explain what that means?
Local model enthusiasts often assume that running locally is more energy efficient than running in a data center, but fail to take the economies of scale into account.
It's a race to the bottom. DeepSeek beats all others (single-shot), and it is ~50% cheaper than the cost of local electricity only. > DeepSeek V3.2 Reasoning 86.2% ~$0.002 API, single-shot > ATLAS V3 (pass@1-v(k=3)) 74.6% ~$0.004 Local electricity only, best-of-3 + repair pipeline
> cheaper than the cost of local electricity only. Can you explain what that means?
Am I still SOL on AMD (9070 XT) when it comes to this stuff?
I can't imagine trying to using this model on either GPU for real work. I can use much bigger and faster models on the $3 Chutes subscription or $10 OpenCode Go subscription.
Even so, I am still excited. I don't feel like there was even a model worth using with a tool like OpenCode 6 to 9 months ago. I like the way things are heading, and I am looking forward to seeing how capable coding models of this size are in another 6 to 9 months!
I’d encourage devs to use MiniMax, Kimi, etc for real world tasks that require intelligence. The down sides emerge pretty fast: much higher reasoning token use, slower outputs, and degradation that is palpable. Sadly, you do get what you pay for right now. However that doesn’t prevent you from saving tons through smart model routing, being smart about reasoning budgets, and using max output tokens wisely. And optimiz…
https://aibenchy.com/compare/minimax-minimax-m2-7-medium/moo...
Earlier quoted context omitted.
> cheaper than the cost of local electricity only. Can you explain what that means?
China has cheap electricity.