Likely because it uses fewer thinking tokens (that you don't see anyways).
Give a task you have to 3 different models and see what actually works for you. There are no good benchmarks.
This is like saying mass production is "cheating" against handcraft.
Once it figures out a puzzle it could probably be instructed to design a specialized harness for Luna to be able to solve other instances of the same puzzle. Minimum wage workers are not solving novel problems.
They explain it here: https://openai.com/index/how-two-settings-tripled-our-arc-ag... TLDR: The official ARC harness throws away old context and reasoning. No real-world harness is this bad, the model has to re-learn…
With the contributor pricing being more than 10x cheaper than the standard, that would make it best and cheapest on the DeepSWE leaderboard! It feels fast in my experience too. LLMs keep improving at an insane pace.
[dead]
I'm party using 1.2 to reverse engineer and re-implement an old game binary and it has been quite good and fast. The contributor pricing is very attractive, excited to try 1.3 and see if I feel a difference. 1.2 can get…
3.7 high and 3.8 medium are essentially the same on AA intelligence and cost. Output tokens on DeepSWE gives the same picture. So there might be something to it but they have done other things as well. At least the…
If their platform allows any kind of processing as it sounds then anyone could just dump the data. So I don't see why they would not allow downloads for local processing.
Then use other providers hosting open models. Companies and individuals already put their whole code base on the cloud. I'm genuinely interested in privacy-oriented use cases where ZDR is not enough.
I think the privacy argument that keeps coming up is overrepresented. Certainly ZDR is enough for an absolute majority of use cases? I see so much talk about local inference but I doubt most of it has privacy as a valid…
I would guess such a small difference is within the error margin so "roughly equal" I think is fair to say but different models are good at difference things in practice. It is about the same size as GLM 5.3 at about…
The memory and GPU are integrated into the CPU so those can't be upgraded anyways. That's also how the memory can be so fast (shorter physical distance).
Good code is self-explanatory and don't need comments. I can really recommend the book Clean Code, here is a summary: https://gist.github.com/wojteklu/73c6914cc446146b8b533c0988c...
"Qwen3.8 35B-A3B" and 4B/9B variants are already on huggingface distilled by hobbyists.
I'm glad someone is voicing this. Overconsumption at that level is not defensible. However if they meant cached tokens so it's not that bad.
That might actually compensate for the overthinking, if it can think really fast. Dense models are easier than MoE to put on silicon. https://chatjimmy.ai/ is getting 16k tps with an 8B model. Extrapolating that gives…
On DeepSWE it's now 53% vs 63% which is one of the coding benchmarks I trust the most. DS own measurements also show a more significant increase so I suspect AA might update when they release an article. Surprisingly…
I needs to be harness+model combination, https://artificialanalysis.ai/agents/coding-agents
Right. I'm also curious about those use cases. I don't want an AI clicking through my mailbox.
I upgraded my workflow a few months ago from "copy-paste things in and out of ChatGPT" to "use an agent that edits my project files and runs tests on its own" and the ergonomics are just so much better and enables…
This was a disappointed to me. Why would I use pro over flash now? Is there some area where the difference is significant?
If you take 10 turns with a model A, it has to read the cache 10 times and write a lot of tokens (the expensive part). Switching to model B is just prefilling the diff + your new message, which is still just one turn.…
Likely because it uses fewer thinking tokens (that you don't see anyways).
Give a task you have to 3 different models and see what actually works for you. There are no good benchmarks.
This is like saying mass production is "cheating" against handcraft.
Once it figures out a puzzle it could probably be instructed to design a specialized harness for Luna to be able to solve other instances of the same puzzle. Minimum wage workers are not solving novel problems.
They explain it here: https://openai.com/index/how-two-settings-tripled-our-arc-ag... TLDR: The official ARC harness throws away old context and reasoning. No real-world harness is this bad, the model has to re-learn…
With the contributor pricing being more than 10x cheaper than the standard, that would make it best and cheapest on the DeepSWE leaderboard! It feels fast in my experience too. LLMs keep improving at an insane pace.
[dead]
I'm party using 1.2 to reverse engineer and re-implement an old game binary and it has been quite good and fast. The contributor pricing is very attractive, excited to try 1.3 and see if I feel a difference. 1.2 can get…
3.7 high and 3.8 medium are essentially the same on AA intelligence and cost. Output tokens on DeepSWE gives the same picture. So there might be something to it but they have done other things as well. At least the…
If their platform allows any kind of processing as it sounds then anyone could just dump the data. So I don't see why they would not allow downloads for local processing.
Then use other providers hosting open models. Companies and individuals already put their whole code base on the cloud. I'm genuinely interested in privacy-oriented use cases where ZDR is not enough.
I think the privacy argument that keeps coming up is overrepresented. Certainly ZDR is enough for an absolute majority of use cases? I see so much talk about local inference but I doubt most of it has privacy as a valid…
I would guess such a small difference is within the error margin so "roughly equal" I think is fair to say but different models are good at difference things in practice. It is about the same size as GLM 5.3 at about…
[dead]
The memory and GPU are integrated into the CPU so those can't be upgraded anyways. That's also how the memory can be so fast (shorter physical distance).
Good code is self-explanatory and don't need comments. I can really recommend the book Clean Code, here is a summary: https://gist.github.com/wojteklu/73c6914cc446146b8b533c0988c...
"Qwen3.8 35B-A3B" and 4B/9B variants are already on huggingface distilled by hobbyists.
I'm glad someone is voicing this. Overconsumption at that level is not defensible. However if they meant cached tokens so it's not that bad.
That might actually compensate for the overthinking, if it can think really fast. Dense models are easier than MoE to put on silicon. https://chatjimmy.ai/ is getting 16k tps with an 8B model. Extrapolating that gives…
On DeepSWE it's now 53% vs 63% which is one of the coding benchmarks I trust the most. DS own measurements also show a more significant increase so I suspect AA might update when they release an article. Surprisingly…
I needs to be harness+model combination, https://artificialanalysis.ai/agents/coding-agents
Right. I'm also curious about those use cases. I don't want an AI clicking through my mailbox.
I upgraded my workflow a few months ago from "copy-paste things in and out of ChatGPT" to "use an agent that edits my project files and runs tests on its own" and the ergonomics are just so much better and enables…
This was a disappointed to me. Why would I use pro over flash now? Is there some area where the difference is significant?
If you take 10 turns with a model A, it has to read the cache 10 times and write a lot of tokens (the expensive part). Switching to model B is just prefilling the diff + your new message, which is still just one turn.…